FORM 2
THE PATENTS ACT, 1970 (39 OF 1970) & THE PATENT RULES, 2003
COMPLETE SPECIFICATION
(See section 10 and rule 13)
“METHOD AND SYSTEM FOR POWER OPTIMIZATION IN A CELLULAR NETWORK”
We, Jio Platforms Limited, an Indian National, of Office - 101, Saffron, Nr. Centre Point, Panchwati 5 Rasta, Ambawadi, Ahmedabad - 380006, Gujarat, India.
The following specification particularly describes the invention and the manner in which it is to be performed.

METHOD AND SYSTEM FOR POWER OPTIMIZATION IN A CELLULAR NETWORK
TECHNICAL FIELD
[0001] Embodiments of the present disclosure generally relate to network performance optimization systems. More particularly, embodiments of the present disclosure relate to methods and systems for power optimization in a cellular network.
BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Wireless communication technology has rapidly evolved over the past few decades, with each generation bringing significant improvements and advancements. The first generation of wireless communication technology was based on analog technology and offered only voice services. However, with the advent of the second-generation (2G) technology, digital communication and data services became possible, and text messaging was introduced. The third generation (3G) technology marked the introduction of high-speed internet access, mobile video calling, and location-based services. The fourth generation (4G) technology revolutionized wireless communication with faster data speeds, better network coverage, and improved security. Currently, the fifth generation (5G) technology is being deployed, promising even faster data speeds, low latency, and the ability to connect multiple devices simultaneously. With each generation, wireless

communication technology has become more advanced, sophisticated, and capable of delivering more services to its users.
[0004] A new age technology such as the 5G technology requires a substantial amount of energy for executing one or more operations. Also, the communication through the new technology consumes three times (3x) power in comparison with power consumed in with its legacy technology such as the 4G technology. With the development in the communication technology, several solutions were introduced for reducing a power consumption in a system associated with the new age technology. One such solution is a network densification. The concept of network densification involves deployment of numerous small-sized base stations for increasing a network capacity via efficient frequency reuse. Another such solution is Massive Multiple-Input, Multiple-Output (M-MIMO) that involves utilization of one or more large sized antenna arrays for serving multiple users simultaneously, by using the same time-frequency resources.
[0005] However, the existing solutions for reducing the power consumption in the system of said technology depends on selectively turning off or activating one or more specific radio frequency (RF) channels based on a traffic demand, one or more network conditions or one or more energy saving goals. The existing solutions also require dynamic reconfiguration of the one or more RF channels of the one or more base stations.
[0006] Further, a concept termed as symbol TRX shutdown in the 5G technology involves selectively shutting down or deactivating a transmission and a reception (TRX) of one or more symbols in the network when the one or more symbols are not required. Additionally, when the specific one or more symbols are deactivated, one or more associated power amplifiers (PAs) may be identified based on an established symbol-PA association.

[0007] Further, one or more conventional energy-saving techniques frequently use one or more fixed thresholds and one or more timers for activating one or more energy-saving features. However, the one or more fixed thresholds may not adapt to one or more dynamic network conditions such as a network congestion, a network failure, a load balancing and one or more dynamic traffic patterns such as event driven peaks, time -based variations. Additionally, the existing solutions fail to identify an appropriate time for performing the symbol TRX shutdown, which results in inefficient use of one or more resources. Hence, the existing solutions fail to provide an approach to save maximum amount of energy without an impact on a user experience.
[0008] Therefore, due to abovementioned drawbacks, there is a need to provide an enhanced solution for power optimization in a cellular network.
SUMMARY
[0009] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0010] An aspect of the present disclosure may relate to a method for power optimization in a cellular network. The method comprises predicting, by a prediction unit, one or more time periods for receiving a low traffic data, the prediction based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network. The method comprises reducing, by a power optimization unit, a power output of one or more components in the cellular network, the one or more components including a power amplifier (PA) and one or more transmission and reception (TRX) symbols. Further, reducing the power output of the PA is based on the predicted one or more time periods and a pre¬defined threshold value related to the traffic data. The method comprises updating,

by an updation unit, a system status to reflect the reduction in the power output of the one or more components. The method comprises calculating, by an analysis unit, an amount of power saved by reducing the power output of the one or more components. The method comprises facilitating, by the analysis unit, at least one of a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low power mode.
[0011] In an exemplary aspect of the present disclosure, prior to the predicting, by the prediction unit, the one or more time periods for receiving the low traffic data, the method comprises monitoring continuously, by the prediction unit, the real-time traffic data associated with the one or more users of the cellular network. The method further comprises storing, at a storage unit, the continuous monitoring of the real-time traffic data based on a predefined criteria.
[0012] In an exemplary aspect of the present disclosure, the predefined criteria comprises one or more of a total traffic data, a radio release control (RRC) connected user data, a cell throughput data, a physical resource block (PRB) utilization data, an application layer data, a crowd sourced data, a low traffic conditions data, and a specific TRX symbols requirement data.
[0013] In an exemplary aspect of the present disclosure, the method further comprises monitoring continuously, by the prediction unit, the real-time traffic data associated with the one or more users. The method further comprises determining, by the prediction unit, one of a threshold breach status and a threshold keep status based on the continuous monitoring of the real-time traffic data. The threshold breach status is determined in an event the amount of the real-time traffic is above a second pre-defined threshold value, and the threshold keep status is determined in an event the amount of the real-time traffic is below the second pre-defined threshold value.

[0014] In an exemplary aspect of the present disclosure, for the facilitating, by the analysis unit, the exit from the existing low power mode, the method comprises determining, by the analysis unit, one of a positive reactivation and a negative reactivation based on the continuous monitoring of the real-time traffic data. The positive reactivation is determined in an event of at least one of the determination of the threshold breach status and a specific TRX symbol need, and the negative reactivation is determined in an event of the determination of the threshold keep status. The positive reactivation is associated with increase in power output of one or more target components, and the negative reactivation is associated with maintenance of reduced power output of the one or more target components. The method further comprises identifying, by the analysis unit, a set of one or more of target TRX symbols and target power amplifiers for positive reactivation, in an event of determination of the positive reactivation. The method further comprises increasing, by the power optimization unit, the power output of the identified set of one or more target TRX symbols and target power amplifiers, for reactivation. The method further comprises updating, by the updation unit, a system status to reflect the increase in the power output of the set of one or more target TRX symbols and target power amplifiers. The method further comprises calculating, by the analysis unit, an amount of power consumed by the set of one or more target TRX symbols and target power amplifiers by reactivation.
[0015] In an exemplary aspect of the present disclosure, the facilitating at least one of the entering the low power mode and the maintenance of the reduced power output of one or more components further comprises detecting, by the analysis unit, a low latency traffic data based on the continuous monitoring of the real-time traffic data. The method further comprises prioritising, by the analysis unit, a transmission of the low latency traffic data to the one or more users of the cellular network.
[0016] Another aspect of the present disclosure may relate to a system for power optimization in a cellular network. The system comprises a prediction unit configured to predict one or more time periods for receiving a low traffic data, the

prediction based on a historical traffic data and a real-time traffic data associated
with one or more users in the cellular network. The system further comprises a
power optimization unit connected to at least the prediction unit, the power
optimization unit configured to reduce a power output of one or more components
5 in the cellular network, the one or more components including a power amplifier
(PA) and one or more transmission and reception (TRX) symbols. Further to reduce the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data. The system further comprises an updation unit connected to at least the power optimization unit, the
10 updation unit configured to update a system status to reflect the reduction in the
power output of the one or more components. The system further comprises an analysis unit connected to at least the updation unit. The analysis unit configured to calculate an amount of power saved by reducing the power output of the one or more components. The analysis unit is further configured to facilitate at least one
15 of a trigger for entering a low power mode, a maintenance of the reduced power
output of one or more components, and an exit from an existing low power mode.
[0017] Another aspect of the present disclosure may relate to a user equipment (UE) for power optimization in a cellular network. The UE comprises a memory;
20 and a processor coupled to the memory. The processor is configured to transmit to
a system, a prediction of one or more time periods for receiving a low traffic data, wherein the prediction is based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network. The processor is configured to receiving from the system, a response associated with the prediction.
25 The response is received based on reducing, by the system, a power output of one
or more components in the cellular network, the one or more components including a power amplifier (PA) and one or more transmission and reception (TRX) symbols. Further, reducing the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data. Further,
30 the response is received based on updating, by the system, a system status to reflect
the reduction in the power output of the one or more components. The processor is
7

configured to calculating, by the system, an amount of power saved by reducing the
power output of the one or more components. Further, the response is received
based on facilitating, by the system, at least one of a trigger for entering a low power
mode, a maintenance of the reduced power output of one or more components, and
5 an exit from an existing low power mode.
[0018] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for power optimization in a cellular network. The instructions include executable code which, when executed
10 by one or more units of a system causes a prediction unit of the system to predict,
one or more time periods for receiving a low traffic data, the prediction based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network. Further, the executable code when executed causes a power optimization unit of the system to reduce, a power output of one or more
15 components in the cellular network, the one or more components including a power
amplifier (PA) and one or more transmission and reception (TRX) symbols. Further, reducing the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data. Further, the executable code when executed causes an updation unit of the system to update, by
20 a system status to reflect the reduction in the power output of the one or more
components. Further, the executable code when executed causes an analysis unit of the system to calculate, an amount of power saved by reducing the power output of the one or more components. Further, the executable code when executed causes the analysis unit of the system to facilitate, at least one of a trigger for entering a
25 low power mode, a maintenance of the reduced power output of one or more
components, and an exit from an existing low power mode.
OBJECTS OF THE DISCLOSURE
30 [0019] Some of the objects of the present disclosure, which at least one
embodiment disclosed herein satisfies are listed herein below.
8

[0020] It is an object of the present disclosure to provide a system and method for
a trained model for proactive power optimization by a radio frequency (RF)
Reconfiguration (Channel shutdown and symbol TRX shutdown) in cellular
5 networks.
[0021] It is another object of the present disclosure to provide a system and method
for proactive power optimization in cellular networks that improve energy
efficiency in networks, particularly during periods of low or medium data
10 transmission.
[0022] It is another object of the present disclosure to provide a system and method for proactive power optimization in cellular networks that reduce the operational costs associated with energy usage in networks by optimizing power consumption.
15
[0023] It is another object of the present disclosure to provide a system and method for proactive power optimization in cellular networks by introducing a Distributed Unit (DU) Scheduler that ensure that network performance remains optimal even while energy consumption is reduced.
20
[0024] It is another object of the present disclosure to provide a system and method for proactive power optimization in cellular networks that aims to employ a proactive approach in identifying cells for action through a traffic prediction.
25 [0025] It is yet another object of the present disclosure to provide a system and
method for proactive power optimization in cellular networks that aims to be easily adoptable into standard technology without necessitating significant infrastructural changes.
30 BRIEF DESCRIPTION OF THE DRAWINGS
9

[0026] The accompanying drawings, which are incorporated herein, and constitute
a part of this disclosure, illustrate exemplary embodiments of the disclosed methods
and systems in which like reference numerals refer to the same parts throughout the
different drawings. Components in the drawings are not necessarily to scale,
5 emphasis instead being placed upon clearly illustrating the principles of the present
disclosure. Also, the embodiments shown in the figures are not to be construed as
limiting the disclosure, but the possible variants of the method and system
according to the disclosure are illustrated herein to highlight the advantages of the
disclosure. It will be appreciated by those skilled in the art that disclosure of such
10 drawings includes disclosure of electrical components or circuitry commonly used
to implement such components.
[0027] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture. 15
[0028] FIG. 2 illustrates an exemplary block diagram of a computing device upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure.
20 [0029] FIG. 3 illustrates an exemplary block diagram of a system for power
optimization in a cellular network, in accordance with exemplary implementations of the present disclosure.
[0030] FIG. 4 illustrates a flow diagram of a method for power optimization in a
25 cellular network in accordance with exemplary implementations of the present
disclosure.
[0031] FIG. 5 illustrates an exemplary modulation diagram of a normal mode
(mode A) and a low power mode (mode B) in accordance with exemplary
30 implementations of the present disclosure.
10

[0032] FIG. 6 illustrates an exemplary architecture diagram of implementation of the system for power optimization in a cellular network, in accordance with exemplary implementations of the present disclosure.
5 [0033] FIG. 7 illustrates a flow diagram of an exemplary method for power
optimization in a cellular network, in accordance with exemplary implementations of the present disclosure.
[0034] The foregoing shall be more apparent from the following more detailed
10 description of the disclosure.
DETAILED DESCRIPTION
[0035] In the following description, for the purposes of explanation, various
15 specific details are set forth in order to provide a thorough understanding of
embodiments of the present disclosure. It will be apparent, however, that
embodiments of the present disclosure may be practiced without these specific
details. Several features described hereafter may each be used independently of one
another or with any combination of other features. An individual feature may not
20 address any of the problems discussed above or might address only some of the
problems discussed above.
[0036] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather,
25 the ensuing description of the exemplary embodiments will provide those skilled in
the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
30
11

[0037] Specific details are given in the following description to provide a thorough
understanding of the embodiments. However, it will be understood by one of
ordinary skill in the art that the embodiments may be practiced without these
specific details. For example, circuits, systems, processes, and other components
5 may be shown as components in block diagram form in order not to obscure the
embodiments in unnecessary detail.
[0038] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure
10 diagram, or a block diagram. Although a flowchart may describe the operations as
a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure.
15
[0039] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not
20 necessarily to be construed as preferred or advantageous over other aspects or
designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive—in a manner
25 similar to the term “comprising” as an open transition word—without precluding
any additional or other elements.
[0040] As used herein, a “processing unit” or “processor” or “operating processor”
includes one or more processors, wherein processor refers to any logic circuitry for
30 processing instructions. A processor may be a general-purpose processor, a special
purpose processor, a conventional processor, a digital signal processor, a plurality
12

of microprocessors, one or more microprocessors in association with a Digital
Signal Processing (DSP) core, a controller, a microcontroller, Application Specific
Integrated Circuits, Field Programmable Gate Array circuits, any other type of
integrated circuits, etc. The processor may perform signal coding data processing,
5 input/output processing, and/or any other functionality that enables the working of
the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor.
[0041] As used herein, “a user equipment”, “a user device”, “a smart-user-device”,
10 “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”,
“a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and/or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment/device may include, but is not limited to, a mobile phone, smart
15 phone, laptop, a general-purpose computer, desktop, personal digital assistant,
tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receive an input from unit(s) which are required to implement the features of the present disclosure.
20
[0042] As used herein, “storage unit” or “memory unit” refers to a machine or computer-readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”),
25 magnetic disk storage media, optical storage media, flash memory devices or other
types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.
30 [0043] As used herein “interface” or “user interface refers to a shared boundary
across which two or more separate components of a system exchange information
13

or data. The interface may also be referred to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with each other, which also includes the methods, functions, or procedures that may be called. 5
[0044] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a
general-purpose processor, a special purpose processor, a conventional processor, a
digital signal processor (DSP), a plurality of microprocessors, one or more
10 microprocessors in association with a DSP core, a controller, a microcontroller,
Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
[0045] As used herein, a “Distributed Unit (DU) Scheduler” refers to a component
15 of a base station (gNodeB) of a network that is responsible for scheduling and
managing the allocation of radio resources, such as time and frequency, for multiple
User Equipment (UE) devices in the network. Further, the DU Scheduler schedules
and coordinates the transmission and reception of data between the UE and the
gNodeB, enabling fast and reliable data transfer and maintaining the quality of
20 service (QoS) in the network.
[0046] As used herein the transceiver unit include at least one receiver and at least
one transmitter configured respectively for receiving and transmitting data, signals,
information or a combination thereof between units/components within the system
25 and/or connected with the system.
[0047] As discussed in the background section, that a new age technology such as
the 5G technology requires a substantial amount of energy for executing one or
more operations. Also, the communication through said technology consumes three
30 times (3x) power in comparison with power consumed by its legacy technology
such as the 4G technology. With the development in the communication technology,
14

several solutions were introduced for reducing a power consumption in a system
associated with the new age technology. Further, one or more conventional energy-
saving techniques frequently use one or more fixed thresholds and one or more
timers for activating one or more energy-saving features. However, the one or more
5 fixed thresholds may not adapt to one or more dynamic network conditions such as
a network congestion, a network failure, a load balancing and one or more dynamic traffic patterns such as event driven peaks, time -based variations. Additionally, the existing solutions fails to identify an appropriate time for performing the symbol TRX shutdown, which results in inefficient use of one or more resources. Thus, the
10 existing solutions fails to provide an approach to save maximum amount of energy
without an impact on a user experience. Hence, current known solutions have several shortcomings. The present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing a novel solution for power optimization in a cellular network that predicts a plurality
15 of time periods for receiving a low traffic data. Further, a power output of a power
amplifier (PA ) is reduced based on the predicted plurality of time periods and a pre¬defined threshold value related to the traffic data. Further, a system status is updated to reflect the reduction in the power output of one or more components such as the power amplifier (PA) and one or more transmission and reception (TRX) symbols.
20 Thereafter, the novel solution also encompasses calculating an amount of power
saved by reducing the power output of the one or more components. Further, the novel solution of the present disclosure facilitates a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low power mode for power optimization in the cellular
25 network.
[0048] FIG. 1 illustrates an exemplary block diagram representation of 5th
generation core (5GC) network architecture, in accordance with exemplary
implementation of the present disclosure. As shown in fig. 1, the 5GC network
30 architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], an access and mobility management function (AMF) [106], a Session
15

Management Function (SMF) [108], a Service Communication Proxy (SCP) [110],
an Authentication Server Function (AUSF) [112], a Network Slice Specific
Authentication and Authorization Function (NSSAAF) [114], a Network Slice
Selection Function (NSSF) [116], a Network Exposure Function (NEF) [118], a
5 Network Repository Function (NRF) [120], a Policy Control Function (PCF) [122],
a Unified Data Management (UDM) [124], an application function (AF) [126], a User Plane Function (UPF) [128], and a data network (DN) [130], wherein all the components are assumed to be connected to each other in a manner as obvious to the person skilled in the art for implementing features of the present disclosure.
10
[0049] Radio Access Network (RAN) [104] is the part of a mobile telecommunications system that connects user equipment (UE) [102] to the core network (CN) and provides access to different types of networks (e.g., 5G network). It consists of radio base stations and the radio access technologies that enable
15 wireless communication.
[0050] Access and Mobility Management Function (AMF) [106] is a 5G core
network function responsible for managing access and mobility aspects, such as UE
registration, connection, and reachability. It also handles mobility management
20 procedures like handovers and paging.
[0051] Session Management Function (SMF) [108] is a 5G core network function
responsible for managing session-related aspects, such as establishing, modifying,
and releasing sessions. It coordinates with the User Plane Function (UPF) for data
25 forwarding and handles IP address allocation and QoS enforcement.
[0052] Service Communication Proxy (SCP) [110] is a network function in the 5G
core network that facilitates communication between other network functions by
providing a secure and efficient messaging service. It acts as a mediator for service-
30 based interfaces.
16

[0053] Authentication Server Function (AUSF) [112] is a network function in the 5G core responsible for authenticating UEs during registration and providing security services. It generates and verifies authentication vectors and tokens.
5 [0054] Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and authorization services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
10 [0055] Network Slice Selection Function (NSSF) [116] is a network function
responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies.
[0056] Network Exposure Function (NEF) [118] is a network function that exposes
15 capabilities and services of the 5G network to external applications, enabling
integration with third-party services and applications.
[0057] Network Repository Function (NRF) [120] is a network function that acts
as a central repository for information about available network functions and
20 services. It facilitates the discovery and dynamic registration of network functions.
[0058] Policy Control Function (PCF) [122] is a network function responsible for policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies. 25
[0059] Unified Data Management (UDM) [124] is a network function that centralizes the management of subscriber data, including authentication, authorization, and subscription information.
17

[0060] Application Function (AF) [126] is a network function that represents external applications interfacing with the 5G core network to access network capabilities and services.
5 [0061] User Plane Function (UPF) [128] is a network function responsible for
handling user data traffic, including packet routing, forwarding, and QoS enforcement.
[0062] Data Network (DN) [130] refers to a network that provides data services to
10 user equipment (UE) in a telecommunications system. The data services may
include but are not limited to Internet services, private data network related services.
[0063] FIG. 2 illustrates an exemplary block diagram of a computing device [200] upon which the features of the present disclosure may be implemented in
15 accordance with exemplary implementation of the present disclosure. In an
implementation, the computing device [200] may also implement a method for power optimization in a cellular network utilising the system [300]. In another implementation, the computing device [200] itself implements the method for power optimization in the cellular network using one or more units configured
20 within the computing device [200], wherein said one or more units are capable of
implementing the features as disclosed in the present disclosure.
[0064] The computing device [200] may include a bus [202] or other communication mechanism for communicating information, and a hardware
25 processor [204] coupled with the bus [202] for processing information. The
hardware processor [204] may be, for example, a general-purpose microprocessor. The computing device [200] may also include a main memory [206], such as a random-access memory (RAM), or other dynamic storage device, coupled to the bus [202] for storing information and instructions to be executed by the processor
30 [204]. The main memory [206] also may be used for storing temporary variables or
other intermediate information during execution of the instructions to be executed
18

by the processor [204]. Such instructions, when stored in non-transitory storage
media accessible to the processor [204], render the computing device [200] into a
special-purpose machine that is customized to perform the operations specified in
the instructions. The computing device [200] further includes a read only memory
5 (ROM) [208] or other static storage device coupled to the bus [202] for storing static
information and instructions for the processor [204].
[0065] A storage device [210], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [202] for storing information and
10 instructions. The computing device [200] may be coupled via the bus [202] to a
display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for displaying information to a computer user. An input device [214], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the
15 bus [202] for communicating information and command selections to the processor
[204]. Another type of user input device may be a cursor controller [216], such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor [204], and for controlling cursor movement on the display [212]. The input device typically has two degrees
20 of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allow
the device to specify positions in a plane.
[0066] The computing device [200] may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware
25 and/or program logic which in combination with the computing device [200] causes
or programs the computing device [200] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device [200] in response to the processor [204] executing one or more sequences of one or more instructions contained in the main memory [206]. Such
30 instructions may be read into the main memory [206] from another storage medium,
such as the storage device [210]. Execution of the sequences of instructions
19

contained in the main memory [206] causes the processor [204] to perform the process steps described herein. In alternative implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with software instructions. 5
[0067] The computing device [200] also may include a communication interface [218] coupled to the bus [202]. The communication interface [218] provides a two-way data communication coupling to a network link [220] that is connected to a local network [222]. For example, the communication interface [218] may be an
10 integrated services digital network (ISDN) card, cable modem, satellite modem, or
a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface [218] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such
15 implementation, the communication interface [218] sends and receives electrical,
electromagnetic or optical signals that carry digital data streams representing various types of information.
[0068] The computing device [200] can send messages and receive data, including
20 program code, through the network(s), the network link [220] and the
communication interface [218]. In the Internet example, a server [230] might
transmit a requested code for an application program through the Internet [228], the
ISP [226], the local network [222], the host [224] and the communication interface
[218]. The received code may be executed by the processor [204] as it is received,
25 and/or stored in the storage device [210], or other non-volatile storage for later
execution.
[0069] Referring to FIG. 3, an exemplary block diagram of a system [300] for
power optimization in a cellular network, is shown, in accordance with the
30 exemplary implementations of the present disclosure. The system [300] comprises
at least one prediction unit [302], at least one power optimization unit [304], at least
20

one updation unit [306], at least one analysis unit [308] and at least one storage unit
[310]. Also, all of the components/ units of the system [300] are assumed to be
connected to each other unless otherwise indicated below. As shown in the figures
all units shown within the system [300] should also be assumed to be connected to
5 each other. Also, in FIG. 3 only a few units are shown, however, the system [300]
may comprise multiple such units or the system [300] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the system [300] may be present in a user device/ user equipment [102] to implement the features of the present disclosure.
10 The system [300] may be a part of the user device [102]/ or may be independent of
but in communication with the user device [102] (may also referred herein as a UE). In another implementation, the system [300] may reside in a server or a network entity. In yet another implementation, the system [300] may reside partly in the server/ network entity and partly in the user device.
15
[0070] The system [300] is configured for power optimization in the cellular network, with the help of the interconnection between the components/units of the system [300].
20 [0071] In order to optimize the energy in the cellular network, the prediction unit
[302] is configured to predict one or more time periods for receiving a low traffic data, the prediction based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network.
25 [0072] As used herein, “low traffic data” refers to a data associated with a lower
amount of data that is communicated by the one or more users across the cellular network at a pre-defined instance. For example, in a time period i.e., 12 A.M. to 2 P.M., lower amount of data is communicated by the one or more users, within the cellular network, hence, the data at this time periods may consider as low traffic
30 data.
21

[0073] The present disclosure encompasses that the prediction unit [302] may
utilize one or more machine learning techniques for predicting the one or more time
periods. The one or more machine learning techniques may be pre-stored in the
5 storage unit [310] and/or pre-defined by an administrator.
[0074] The present disclosure encompasses that the historical traffic data refers to one or more past records or one or more logs of network usage patterns over a period of time such as last 30 days or 3 months. Further, the present disclosure
10 encompasses that the real-time traffic data refers to current or ongoing amount of
data that is communicated by the one or more users across the cellular network. For example, an amount of request or the amount data communicated by the user in the cellular network from a time period T0 to T3, wherein T0 is an instant time and T3 is 3 hours after T0. For ease of understanding, let us consider an example, wherein
15 let suppose a daily traffic patterns of a User 1 show a consistent drop in traffic
between 2 AM and 4 AM, with an average of 100 KB of data transmitted during this period. Further, a weekly traffic patterns of the User 1show a consistent drop in traffic on Sundays between 10 PM and 1 AM, with an average of 50 KB of data transmitted during this period (both are historical data). Further at a current time
20 i.e., a real time such at 1:45 AM the User 1 is currently transmitting 50 KB of data.
Then, based on historical data and real-time traffic, the solution of the present disclosure may predict a low traffic period for the next 1 hour (2 AM - 3 AM) for the User 1, with an expected traffic volume of less than 100 KB.
25 [0075] The present disclosure encompasses that the prediction unit [302] prior to
predicting the one or more time periods for receiving the low traffic data, is
configured to monitor continuously, the real-time traffic data associated with the
one or more users of the cellular network. The prediction unit [302] is further
configured to store, at the storage unit [310], the continuous monitoring of the real-
30 time traffic data based on a predefined criteria.
22

[0076] For example, the prediction unit [302] may receiving the low traffic data
(i.e., the traffic data between the time period 12 A.M to 3 A.M.) for a user A who is
associated with the cellular network. Further, the prediction unit [302] is configured
to monitor the real-time traffic data (i.e., T0 to T1, as explained in the above
5 example) of the user A of the cellular network such as 5g network, based on the
predefined criteria such as low traffic conditions, specific symbols not required, etc.
[0077] The present disclosure encompasses that the predefined criteria comprises
one or more of a total traffic data, a radio release control (RRC) connected user
10 data, a cell throughput data, a physical resource block (PRB) utilization data, an
application layer data, a crowd sourced data, a low traffic conditions data, and a specific TRX symbols requirement data.
[0078] The present disclosure encompasses that the total traffic data refers to an
15 overall amount of data transmitted over the cellular network within a specific time
frame such as last 3 months.
[0079] The present disclosure encompasses that the radio release control (RRC)
connected user data refers to an information about one or more users currently
20 connected to the cellular network via a RRC protocol which manage an
establishment, a maintenance, and a release of radio connections between a user device and the cellular network.
[0080] The present disclosure encompasses that the cell throughout data refers to
25 an amount of data that is transferred through a specific cell in the cellular network
over a period of time.
[0081] The present disclosure encompasses that the physical resource block (PRB)
utilization data refers to a data related to allocation of PRBs to the one or more user
30 equipment within the cellular network.
23

[0082] The present disclosure encompasses that the application layer data refers to an information about type of applications or one or more services utilized by the one or more users.
5 [0083] The present disclosure encompasses that the crowd sourced data refers to a
data collected from the one or more user equipment associated with the cellular network.
[0084] The present disclosure encompasses that the low traffic conditions data
10 refers to an information about one or more conditions and/ or the one or more time
periods and/or one or more locations within the cellular network where the network traffic is lower than a threshold value.
[0085] The present disclosure encompasses that specific TRX symbols requirement
15 data refers to data stipulating a number, or a type of Time Division Multiple Access
(TDMA) symbols required for transmission and reception within the cellular network.
[0086] The present disclosure encompasses that the prediction unit [302] is further
20 configured to determine one of a threshold breach and a threshold keep status based
on the continuous monitoring of the real-time traffic data. The threshold breach status is determined in an event the amount of the real-time traffic is above a second pre-defined threshold value, and the threshold keep status is determined in an event the amount of the real-time traffic is below the second pre-defined threshold value. 25
[0087] As explained in the above example, the prediction unit [302] may detect that
during a peak hour such as 12 P.M. to 2 P.M. the amount of real time traffic such as
10 request or 10 tasks related to the user A, exceeds the second pre-defined
threshold value such as 9 request or 9 tasks. For another example, during late night
30 hours 2 A.M. to 3 A.M., the prediction unit [302] may determine that the amount of
real time traffic such as 5 request or 5 tasks falls below the second pre-defined
24

threshold value. Additionally, the request and tasks used in the example may include but not limited to a resource allocation, a service access, a data transmission, a location update, a voice transmission, a message transmission.
5 [0088] The present disclosure encompasses that the second pre-defined threshold
value is determined by the prediction unit [302] based on continuously monitoring of the real-time traffic data associated with the one or more users in the cellular network. Such as, in an event the real time traffic data indicates that the user A is performing 10 tasks within a pre-define time period such 1 hour, then the prediction
10 unit may determine the second pre-defined threshold value as 9 tasks. Further, the
prediction unit [302] may continuously monitors real-time traffic data associated with users in the cellular network to determine the second pre-defined threshold value by analysing the user's behaviour, the prediction unit [302] may identify patterns and adjusts the threshold accordingly. For instance, if user A performs 10
15 tasks within a pre-defined time period of 1 hour, the prediction unit may set the
second pre-defined threshold value at 9 tasks. This adaptive approach ensures that a pre-defined threshold is dynamically adjusted to the user's specific usage pattern, enabling more accurate predictions and optimized resource allocation. This dynamic adjustment enables the network to proactively manage resources,
20 mitigating potential congestion and ensuring seamless connectivity for users.
[0089] Further the power optimization unit [304] connected to at least the
prediction unit [302]. The power optimization unit [304] is configured to reduce a
power output of one or more components in the cellular network. The one or more
25 components includes a power amplifier (PA) and one or more transmission and
reception (TRX) symbols. Further the reduction in the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data.
30 [0090] With reference to the above explained examples, in an event, the real time
traffic data of the user A during the time period 12 A.M. to 2 A.M is 2 tasks or 2
25

request, which is lower than the second threshold value, then the power optimization unit [304] may reduce the power output of the power amplifier (PA) and the respective TRX symbol.
5 [0091] The present disclosure encompasses that the power amplifier (PA) refers to
a component in the cellular network that is used to increase (i.e., amplify) a power level of signals before a data transmission.
[0092] The present disclosure encompasses that the Transmission and Reception
10 (TRX) symbols refers to one or more symbols used in the downlink transmission
of data. The TRX symbols are derived based on one or more modulation schemes
that transform blocks of scrambled bits into complex symbols for transmission. The
LTE downlink supports modulation schemes such as QPSK (Quadrature Phase Shift
Keying), 16QAM (16-Quadrature Amplitude Modulation), and 64QAM (64-
15 Quadrature Amplitude Modulation), which correspond to encoding two, four, and
six bits per symbol, respectively. Additionally, the cellular network may optionally
supports 256QAM (256-Quadrature Amplitude Modulation), which allow for eight
bits per symbol for increasing the data rate.
20 [0093] For example, the reduction in the power output of the PA may be done via
one or more operations such as a signal adjustment, a PA setting adjustment, a mode switching operation.
[0094] For example, the prediction unit [302] may analyse the historical data such
25 as a traffic data recorded in last 30 days or last 1 year and the real-time traffic data
such as T0 to T3 (as explained in the above examples) and further the prediction
unit [302] may forecast that during the late night hours such as 1 AM to 5 AM, the
network usage is consistently low due to fewer active users or fewer activities or
lower amount of data transmission. Further, if the current network traffic falls below
30 30% (i.e., pre-defined value), then the power optimization unit [304] may trigger
the power reduction or may sends a command to reduce the PA power output and
26

to optimize TRX symbol allocation. Upon sending the command, the PA may operate at a reduced power level for conserving energy without compromising network coverage or quality of service.
5 [0095] The updation unit [306] is connected to at least the power optimization unit
[304]. The updation unit [306] is configured to update a system status to reflect the reduction in the power output of the one or more components.
[0096] In continuation with the above explained examples, the updation unit [306]
10 may update a status of a network management system [602] (i.e. system) for
indicating that the PA are operating at a reduced power level during the late night
hours such as 1 A.M to 3 A,M., and adjust the status of the TRX symbol allocation
such as TL may be a symbol for lowering the power output and TM may be a symbol
for increasing the power output. Hence, if a current status of a PA (serial no: 890)
15 in the system is TM, then the updation unit [306] may update/change the respective
status to TL. The updation unit [306] may update the status is real time for ensuring an efficient network management and resource allocation.
[0097] The analysis unit [308] is connected to at least the updation unit [306]. The
20 analysis unit [308] is configured to calculate an amount of power saved by reducing
the power output of the one or more components. The analysis unit [308] is further configured to facilitate at least one of a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low power mode. 25
[0098] The present disclosure encompasses that the analysis unit [308] may utilize
one or more data processing techniques for calculating the amount of power saved
by reducing the power output of the one or more components. The one or more data
processing techniques may include any such technique that may be appreciated by
30 a person skilled in the art to implement the solution of the present disclosure.
Moreover, the analysis unit [308] may compare a data related to historic power
27

consumption with a real time power consumption after implementing the present
solution. For example, the analysis unit [308] may compare the past power
consumption data such as last year data with a data related to ongoing power
consumption (T0 to T1, wherein T0 is a current instance of time and T1 is one hour
5 after T0). Furthermore, the analysis unit [308] may utilize the one or more data
processing techniques for conducting the comparison explained above.
[0099] For example, the analysis unit [308] monitors the real-time traffic data and predicts a period of low user activity during late night hours (such as 1 A.M to 5
10 A.M.). Based on this prediction and a predefined threshold of reduced traffic, the
analysis unit [308] may trigger the power optimization unit [304] to direct power amplifiers (PAs) and other components to enter into the low power mode. The low power mode may reduce the energy consumption (by lowering the power output of the PA) while maintaining sufficient service levels for remaining users.
15 Additionally, the data traffic with less stringent requirements is excluded in low
power mode. Further, this exemplary scenario is explained in FIG. 5.
[0100] For another example, after entering into the low power mode during off-peak hours such as 12 P.M. to 1 P.M., the analysis unit [308] continuously monitors
20 one or more network conditions through real-time data. The analysis unit [308]
maintains the reduced power output of PAs and TRX symbols, adjust the one or more settings such as lowering the operating power of the PAs as necessary to adapt one or more dynamic traffic patterns. For example, the analysis unit [308] may identify that the user A is generally not active during late night hours such as 12
25 A.M to 2 A.M, however, at any instance of time, the user A may remain active
during the late night hours, hence, this dynamic change in the network usage by the user A may lead to the one or more dynamic traffic patterns.
[0101] For another example, during early morning hours such as 5 A.M to 6 A.M.,
30 the analysis unit [308] detects a significant rise in network traffic as the one or more
users start to access one or more services provided by the cellular network. The
28

analysis unit [308] may then trigger the power optimization unit [304] to exit the low power mode. This action restores the one or more PAs and the one or more TRX symbols to their regular power output levels.
5
[0102] The present disclosure encompasses that the analysis unit [308], for facilitating the exit from the existing low power mode, is configured to determine one of a positive reactivation and a negative reactivation based on the continuous monitoring of the real-time traffic data.
10
[0103] The positive reactivation is determined in an event of at least one of the determination of the threshold breach status and a specific TRX symbol need. In an event, when the cellular network may experience a high traffic that breaches the threshold breach or if the specific TRX symbol require more power to maintain
15 service quality. The specific TRX symbol need occurs when one or more specific
transmission/reception (TRX) symbols require more power to maintain optimal service quality.
[0104] With reference to the above explained examples, in the event, the user A is
20 performing 20 tasks in the pre-define time period such 12 P. M. to 2 P.M., the
threshold value related to the traffic in the cellular network is determined as 10 task, and for the 20 tasks, 10 tasks are considered as on priority or important tasks, then in this kind of scenario, the positive reactivation is determined.
25 [0105] The negative reactivation is determined in an event of the determination of
the threshold keep status. In the event, when the cellular network may experience a low traffic is below the threshold breach.
[0106] With reference to the above explained examples, in the event, the user A is
30 performing 05 tasks in the pre-define time period such 12 P. M. to 2 P.M., the
threshold value related to the traffic in the cellular network is determined as 10
29

tasks, hence the traffic is below the threshold breach. In this type of scenario, the negative reactivation is determined.
[0107] The present disclosure encompasses that the positive reactivation occurs
5 when one or more conditions trigger an increase in the power output. The one or
more conditions used herein refers to the threshold breach or the specific TRX
symbol need. The threshold breach is an event when the network traffic exceeds the
predefined threshold value which may indicate a high demand or a network
congestion. The specific TRX symbol need occurs when one or more specific
10 transmission/reception (TRX) symbols require more power to maintain optimal
service quality.
[0108] The present disclosure encompasses that the negative reactivation occurs
when one or more conditions prompt the reduction or maintenance of current power
15 output or activity levels within the cellular network. The negative reactivation
happens in a scenario, where the network traffic remains below the predefined threshold, which may also indicate a low demand or a reduced activity.
[0109] The positive reactivation is associated with increase in power output of one
20 or more target components, and the negative reactivation is associated with
maintenance of reduced power output of the one or more target components.
[0110] The analysis unit [308] is further configured to identify a set of one or more
of target TRX symbols and target power amplifiers for positive reactivation, in an
25 event of determination of the positive reactivation.
[0111] For example, the user A is connected with the cellular network such as 5G
network and a specific PA (such as PA223) along with a specific TRX symbol (such
as TRX-ABC) is allocated to the user A for performing one or more
30 operations/actions, such as a data transmission, a service access. Hence, the analysis
30

unit may identify the specific PA (such as PA223), TRX symbol (such as TRX-ABC) in the event of determination of the positive reactivation.
[0112] The present disclosure encompasses that the analysis unit [308] may utilize
5 the one or more data processing techniques for identification of the set of one or
more target TRX symbols and the target power amplifiers for positive reactivation. The one or more data processing techniques may include any such technique that may be appreciated by a person skilled in the art to implement the solution of the present disclosure.
10
[0113] The power optimization unit [304] is further configured to increase the power output of the identified set of one or more target TRX symbols and target power amplifiers, for reactivation. For example, the analysis unit [308] predicts a surge in the cellular network based on the real time data. Thereafter, the analysis
15 unit [308] may identifies the set of one or more target TRX symbols and the set of
target power amplifiers for increasing the power output to maintain a quality of services provided by the cellular network. Additionally, the increase in the power output refers to adjustment of one or more parameters of the set of one or more TRX symbols and the set of target power amplifiers for delivering one or more
20 stronger signals or handle the higher traffic volume in the cellular network.
[0114] The updation unit [306] is further configured to update a system status to
reflect the increase in the power output of the set of one or more target TRX symbols
and target power amplifiers. The updation unit [306] may update a status of a
25 network management system [602] (i.e., system) for indicating the increase in the
power output of the set of one or more target TRX symbols and target power amplifiers. The updation unit [306] may update the status is real time for ensuring an efficient network management and resource allocation.
30 [0115] The analysis unit [308] is further configured to calculate an amount of
power consumed by the set of one or more target TRX symbols and target power
31

amplifiers by reactivation. The present disclosure encompasses that the analysis
unit [308] may utilize the one or more data processing techniques for calculating
the amount of power consumed by the set of one or more target TRX symbols and
target power amplifiers upon their reactivation. The one or more data processing
5 techniques may include any such technique that may be appreciated by a person
skilled in the art to implement the solution of the present disclosure.
[0116] Further to facilitate the at least one of the entering the low power mode and
the maintenance of the reduced power output of one or more components, the
10 analysis unit [308] is further configured to detect, a low latency traffic data based
on the continuous monitoring of the real-time traffic data. The analysis unit [308] is further configured to prioritise, a transmission of the low latency traffic data to the one or more users of the cellular network.
15 [0117] For example, the analysis unit [308] continuously monitors real-time traffic
data and during off-peak hours, the analysis unit [308] detects the low traffic conditions and identifies specific data packets which require a low latency transmission, thereafter the specific data packets are prioritized by the analysis unit [308].
20
[0118] The present disclosure encompasses that the low latency traffic data refers
to the network traffic that requires a minimal delay or latency in the data
25 transmission.
[0119] Referring to FIG. 4, an exemplary method flow diagram [400] for power
optimization in a cellular network, in accordance with exemplary implementations
of the present disclosure is shown. In an implementation the method [400] is
30 performed by the system [300]. Further, in an implementation, the system [300]
32

may be present in a server device to implement the features of the present disclosure. Also, as shown in Fig. 4, the method [400] starts at step [402].
[0120] At step [404], the method comprises predicting, by a prediction unit [302],
5 one or more time periods for receiving a low traffic data, the prediction based on a
historical traffic data and a real-time traffic data associated with one or more users in the cellular network.
[0121] As used herein, “low traffic data” refers to a data associated with a lower
10 amount of data that is communicated by the one or more users across the cellular
network at a pre-defined instance. For example, in a time period i.e., 12 A.M. to 2 P.M., lower amount of data is communicated by the one or more users, within the cellular network, hence, the data at this time periods may consider as low traffic data. 15
[0122] The present disclosure encompasses that the prediction unit [302] may
utilize one or more machine learning techniques for predicting the one or more time
periods. The one or more machine learning techniques may be pre-stored in the
storage unit [310] and/or pre-defined by an administrator. The one or more machine
20 learning techniques may comprise a technique that is appreciated by a person
skilled in the art to implement the solution of the present disclosure.
[0123] The present disclosure encompasses that the historical traffic data refers to one or more past records or one or more logs of network usage patterns over a period
25 of time such as last 30 days or 3 months. Further, the present disclosure
encompasses that the real-time traffic data refers to current or ongoing amount of data that is communicated by the one or more users across the cellular network. For example, an amount of request or the amount data communicated by the user in the cellular network from a time period T0 to T3, wherein T0 is an instant time and T3
30 is 3 hours after T0.
33

[0124] The present disclosure encompasses that prior to the predicting, by the
prediction unit [302], the one or more time periods for receiving the low traffic data,
the method comprises monitoring continuously, by the prediction unit [302], the
5 real-time traffic data associated with the one or more users of the cellular network.
The method further comprises storing, at the storage unit [310], the continuous monitoring of the real-time traffic data based on a predefined criteria.
[0125] For example, the prediction unit [302] may receiving the low traffic data
10 (i.e., the traffic data between the time period 12 A.M to 3 A.M.) for a user A who is
associated with the cellular network. Further, the prediction unit [302] is configured to monitor the real-time traffic data (i.e., T0 to T1, as explained in the above example) of the user A of the cellular network such as 5g network, based on the predefined criteria such as low traffic conditions, specific symbols not required, etc. 15
[0126] The present disclosure encompasses that the predefined criteria comprises
one or more of a total traffic data, a radio release control (RRC) connected user
data, a cell throughput data, a physical resource block (PRB) utilization data, an
application layer data, a crowd sourced data, a low traffic conditions data, and a
20 specific TRX symbols requirement data.
[0127] The present disclosure encompasses that the total traffic data refers to an overall amount of data transmitted over the cellular network within a specific time frame such as last 3 months.
25
[0128] The present disclosure encompasses that the radio release control (RRC) connected user data refers to an information about one or more users currently connected to the cellular network via a RRC protocol which manage an establishment, a maintenance, and a release of radio connections between a user
30 device and the cellular network.
34

[0129] The present disclosure encompasses that the cell throughout data refers to an amount of data that is transferred through a specific cell in the cellular network over a period of time.
5 [0130] The present disclosure encompasses that the physical resource block (PRB)
utilization data refers to a data related to allocation of PRBs to the one or more user equipment within the cellular network.
[0131] The present disclosure encompasses that the application layer data refers to
10 an information about type of applications or one or more services utilized by the
one or more users.
[0132] The present disclosure encompasses that the crowd sourced data refers to a
data collected from the one or more user equipment associated with the cellular
15 network.
[0133] The present disclosure encompasses that the low traffic conditions data
refers to an information about one or more conditions and/ or the one or more time
periods and/or one or more locations within the cellular network where the network
20 traffic is lower than a threshold value.
[0134] The present disclosure encompasses that specific TRX symbols requirement
data refers to data stipulating a number, or a type of Time Division Multiple Access
(TDMA) symbols required for transmission and reception within the cellular
25 network.
[0135] The present disclosure encompasses that method further comprises
determining, by the prediction unit [302], one of a threshold breach status and a
threshold keep status based on the continuous monitoring of the real-time traffic
30 data. The threshold breach status is determined in an event the amount of the real-
time traffic is above a second pre-defined threshold value, and the threshold keep
35

status is determined in an event the amount of the real-time traffic is below the second pre-defined threshold value.
5 [0136] As explained in the above example, the prediction unit [302] may detect that
during a peak hour such as 12 P.M. to 2 P.M. the amount of real time traffic such as
10 request or 10 tasks related to the user A, exceeds the second pre-defined
threshold value such as 9 request or 9 tasks. For another example, during late night
hours 2 A.M. to 3 A.M., the prediction unit [302] may determine that the amount of
10 real time traffic such as 5 request or 5 tasks falls below the second pre-defined
threshold value. Additionally, the request and tasks used in the example may include but not limited to a resource allocation, a service access, a data transmission, a location update, a voice transmission, a message transmission.
15 [0137] The present disclosure encompasses that the second pre-defined threshold
value is determined by the prediction unit [302] based on continuously monitoring of the real-time traffic data associated with the one or more users in the cellular network. Such as, in an event the real time traffic data indicates that the user A is performing 10 tasks within a pre-define time period such 1 hour, then the prediction
20 unit may determine the second pre-define threshold value as 9 tasks.
[0138] The present disclosure encompasses that the method further comprises
determining, by the analysis unit [308], one of a positive reactivation and a negative
25 reactivation based on the continuous monitoring of the real-time traffic data.
[0139] The positive reactivation is determined in an event of at least one of the
determination of the threshold breach status and a specific TRX symbol need. In an
event, when the cellular network may experience a high traffic that breaches the
30 threshold breach or if the specific TRX symbol require more power to maintain
service quality. The specific TRX symbol need occurs when one or more specific
36

transmission/reception (TRX) symbols require more power to maintain optimal service quality.
[0140] With reference to the above explained examples, in the event, the user A is
5 performing 20 tasks in the pre-define time period such 12 P. M. to 2 P.M., the
threshold value related to the traffic in the cellular network is determined as 10 task, and for the 20 tasks, 10 tasks are considered as on priority or important tasks, then in this kind of scenario, the positive reactivation is determined.
10
[0141] Further the negative reactivation is determined in an event of the determination of the threshold keep status. In the event, when the cellular network may experience a low traffic is below the threshold breach.
15 [0142] With reference to the above explained examples, in the event, the user A is
performing 05 tasks in the pre-define time period such 12 P. M. to 2 P.M., the threshold value related to the traffic in the cellular network is determined as 10 tasks, hence the traffic is below the threshold breach. In this type of scenario, the negative reactivation is determined.
20
[0143] The present disclosure encompasses that the positive reactivation occurs when one or more conditions trigger an increase in the power output. The one or more conditions used herein refers to the threshold breach or the specific TRX symbol need. The threshold breach is an event when the network traffic exceeds the
25 predefined threshold value which may indicate a high demand or a network
congestion. The specific TRX symbol need occurs when one or more specific transmission/reception (TRX) symbols require more power to maintain optimal service quality.
30 [0144] The present disclosure encompasses that the negative reactivation occurs
when one or more conditions prompt the reduction or maintenance of current power
37

output or activity levels within the cellular network. The negative reactivation happens in a scenario, where the network traffic remains below the predefined threshold, which may also indicate a low demand or a reduced activity.
5 [0145] The positive reactivation is associated with increase in power output of one
or more target components, and the negative reactivation is associated with maintenance of reduced power output of the one or more target components.
[0146] The method further comprises identifying, by the analysis unit [308], a set
10 of one or more of target TRX symbols and target power amplifiers for positive
reactivation, in an event of determination of the positive reactivation.
[0147] For example, the user A is connected with the cellular network such as 5G
network and a specific PA (such as PA223) along with a specific TRX symbol (such
15 as TRX-ABC) is allocated to the user A for performing one or more
operations/actions, such as a data transmission, a service access. Hence, the analysis unit may identify the specific PA (such as PA223), TRX symbol (such as TRX-ABC) in the event of determination of the positive reactivation.
20
[0148] The present disclosure encompasses that the analysis unit [308] may utilize the one or more data processing techniques for identification of the set of one or more target TRX symbols and the target power amplifiers for positive reactivation. The one or more data processing techniques include any such technique that may
25 be appreciated by to a person skilled in the art to implement the solution of the
present disclosure.
[0149] The method further comprises increasing, by the power optimization unit
[304], the power output of the identified set of one or more target TRX symbols and
30 target power amplifiers, for reactivation. For example, the analysis unit [308]
predicts a surge in the cellular network based on the real time data. Thereafter, the
38

analysis unit [308] may identify the set of one or more target TRX symbols and the
set of target power amplifiers for increasing the power output to maintain a quality
of services provided by the cellular network. Additionally, the increase in the power
output refers to adjustment of one or more parameters of the set of one or more
5 TRX symbols and the set of target power amplifiers for delivering one or more
stronger signals or handle the higher traffic volume in the cellular network.
[0150] With reference to the above explained examples, in an event, the real time
traffic data of the user A during the time period 12 A.M. to 2 A.M is 2 tasks or 2
10 request, which is lower than the second threshold value, then the power
optimization unit [304] may reduce the power output of the power amplifier (PA) and the respective TRX symbol.
[0151] The method further comprises updating, by the updation unit [306], a system
15 status to reflect the increase in the power output of the set of one or more target
TRX symbols and target power amplifiers. The updation unit [306] may update a
status of a network management system [602] (i.e., system) for indicating the
increase in the power output of the set of one or more target TRX symbols and target
power amplifiers. The updation unit [306] may update the status is real time for
20 ensuring an efficient network management and resource allocation.
[0152] The method further comprises calculating, by the analysis unit [308], an amount of power consumed by the set of one or more target TRX symbols and target power amplifiers by reactivation. The present disclosure encompasses that the
25 analysis unit [308] may utilize the one or more data processing techniques for
calculating the amount of power consumed by the set of one or more target TRX symbols and target power amplifiers upon their reactivation. The one or more data processing techniques include any such technique that may be appreciated by a person skilled in the art to implement the solution of the present disclosure.
30
39

[0153] At step [406], the method comprises reducing, by a power optimization unit
[304], a power output of one or more components in the cellular network, the one
or more components including a power amplifier (PA) and one or more transmission
and reception (TRX) symbols. Further, the reduction of the power output of the PA
5 is based on the predicted one or more time periods and a pre-defined threshold value
related to the traffic data.
[0154] The present disclosure encompasses that the power amplifier (PA) refers to
a component in the cellular network that is used to increase (i.e., amplify) a power
10 level of signals before a data transmission.
[0155] The present disclosure encompasses that the Transmission and Reception (TRX) symbols refers to one or more symbols used in the downlink transmission of data. The TRX symbols are derived based on one or more modulation schemes
15 that transform blocks of scrambled bits into complex symbols for transmission. The
LTE downlink supports modulation schemes such as QPSK (Quadrature Phase Shift Keying), 16QAM (16-Quadrature Amplitude Modulation), and 64QAM (64-Quadrature Amplitude Modulation), which correspond to encoding two, four, and six bits per symbol, respectively. Additionally, the cellular network may optionally
20 supports 256QAM (256-Quadrature Amplitude Modulation), which allow for eight
bits per symbol, for increasing the data rate.
[0156] For example, the reduction in the power output of the PA may be done via
one or more operations such as a signal adjustment, a PA setting adjustment, a mode
25 switching operation.
[0157] For example, the prediction unit [302] may analyse the historical data such
as a traffic data recorded in last 30 days or last 1 year and the real-time traffic data
such as T0 to T3 (as explained in the above examples) and further the prediction
30 unit [302] may forecast that during the late nigh hours such as 1 AM to 5 AM, the
network usage is consistently low due to fewer active users or few activities or
40

lower amount of data transmission. Further, if the current network traffic falls below
30% (i.e., pre-defined value), then the power optimization unit [304] may trigger
the power reduction or may sends a command to reduce the PA power output and
to optimize TRX symbol allocation. Upon sending the command, the PA may
5 operate at a reduced power level for conserving energy without compromising
network coverage or quality of service.
[0158] At step [408], the method comprises updating, by an updation unit [306], a
10 system status to reflect the reduction in the power output of the one or more
components.
[0159] In continuation with the above explained examples, the updation unit [306] may update a status of a network management system [602] (i.e. system) for
15 indicating that the PA are operating at a reduced power level during the late night
hours such as 1 A.M to 3 A,M., and adjust the status of the TRX symbol allocation such as TL may be a symbol for lowering the power output and TM may be a symbol for increasing the power output. Hence, if a current status of a PA (serial no: 890) in the system is TM, then the updation unit [306] may update/change the respective
20 status to TL. The updation unit [306] may update the status is real time for ensuring
an efficient network management and resource allocation.
[0160] At step [410], the method comprises calculating, by an analysis unit [308],
an amount of power saved by reducing the power output of the one or more
25 components.
[0161] At step [412], the method comprises facilitating, by the analysis unit [308],
at least one of a trigger for entering a low power mode, a maintenance of the reduced
power output of one or more components, and an exit from an existing low power
30 mode. The present disclosure encompasses that the analysis unit [308] may utilize
one or more data processing techniques for calculating the amount of power saved
41

by reducing the power output of the one or more components. The one or more data
processing techniques to include any such technique that may be appreciated by a
person skilled in the art to implement the solution of the present disclosure.
Moreover, the analysis unit [308] may compare a data related to historic power
5 consumption with a real time power consumption after implementing the present
solution. For example, the analysis unit [308] may compare the past power
consumption data such as last year data with a data related to ongoing power
consumption (T0 to T1, wherein T0 is a current instance of time and T1 is one hour
after T0). Furthermore, the analysis unit [308] may utilize the one or more data
10 processing techniques for conducting the comparison explained above.
[0162] For example, the analysis unit [308] monitors the real-time traffic data and predicts a period of low user activity during late night hours (such as 1 A.M to 5 A.M.). Based on this prediction and a predefined threshold of reduced traffic, the
15 analysis unit [308] may trigger the power optimization unit [304] to direct power
amplifiers (PAs) and other components to enter into the low power mode. The low power mode may reduce the energy consumption ((by lowering the power output of the PA) while maintaining sufficient service levels for remaining users. Further, this exemplary scenario is explained in FIG. 5.
20
[0163] For another example, after entering into the low power mode during off-peak hours such as 12 P.M to 1 P.M, the analysis unit [308] continuously monitors one or more network conditions through real-time data. The analysis unit [308]
25 maintains the reduced power output of PAs and TRX symbols, adjust the one or
more settings such as lowering the operating power of the Pas, as necessary to adapt one or more dynamic traffic patterns. Hence, the analysis unit [308] helps to conserves one or more resources without compromising a user experience. For example, the analysis unit [308] may identify that the user A is generally not active
30 during late night hours such as 12 A.M to 2 A.M, however, at any instance of time,
the user A may remain active during the late night hours, hence, this dynamic
42

change in the network usage by the user A may lead to the one or more dynamic traffic patterns.
[0164] For another example, during early morning hours such as 5 A.M to 6 A.M.,
5 the analysis unit [308] detects a significant rise in network traffic as the one or more
users start to access one or more services provided by the cellular network. The analysis unit [308] may then trigger the power optimization unit [304] to exit the low power mode. This action restores the one or more PAs and the one or more TRX symbols to their regular power output levels.
10
[0165] The present disclosure encompasses that the facilitating at least one of the entering the low power mode and the maintenance of the reduced power output of one or more components further comprises detecting, by the analysis unit [308], a low latency traffic data based on the continuous monitoring of the real-time traffic
15 data. Further, the facilitating at least one of the entering the low power mode and
the maintenance of the reduced power output of one or more components further comprises prioritising, by the analysis unit [308], a transmission of the low latency traffic data to the one or more users of the cellular network.
20 [0166] For example, the analysis unit [308] continuously monitors real-time traffic
data and during off-peak hours, the analysis unit [308] detects the low traffic conditions and identifies specific data packets which require a low latency transmission, thereafter the specific data packets are prioritized by the analysis unit [308] for ensuring a timely and an efficient communication during the low power
25 mode.
[0167] The present disclosure encompasses that the low latency traffic data refers to the network traffic that requires a minimal delay or latency in the data transmission. 30
[0168] The method [400] terminates at step [414].
43

[0169] Referring to FIG. 5, an exemplary modulation diagram of a normal mode (mode A) and a low power mode (mode B) in accordance with exemplary implementations of the present disclosure is depicted. 5
[0170] In an exemplary scenario, where a user A is connected with the cellular network such as 5G network, during a time period T0, a power amplifier is switch ON, the system [300] (i.e. by the analysis unit of the system [300] identifies that during time period T1, the user A remains in-active or detects in real time that no
10 activity is performed by the user A on the cellular network. Thereafter, the PA is
turned off during the time period T1 (such as late hours: 12 A.M. to 3 A.M) by the system [300]. Further, at time period T2, the user A is again active, and the PA is switched on. Likewise, the PA is switched off or switched on, depending upon the activeness (i.e., traffic data) of the user.
15
[0171] Additionally, the normal mode (mode A) may refer to an active or switched on or operative state of the PA, whereas the low power mode (mode B) may refer to an inactive or switched off mode of the PA.
20 [0172] Referring to FIG. 6, an exemplary architecture diagram [600] of
implementation of the system for power optimization in a cellular network, in accordance with exemplary implementations of the present disclosure is shown. As, shown in FIG. 6, the exemplary architecture diagram [600] depicts an association between three entities, i.e. a network management system [602] which is
25 responsible for conducting one or more tasks such as a scenario identification, a
predictive analysis, a parameter optimization, machine learning and/or artificial intelligence techniques, a model training, a data aggregation, a configuration management, a data collection, a traffic monitoring, a deactivation phase, identification of symbols, update configuration and alike.
30
44

[0173] Further, the network management system [602] is connected with a
Centralized Control and Data Unit (CCDU) [604] which is responsible for a high-
level control and data processing. The CCDU further comprises a Packet Routing
Control (PRC) [606], a Packet Data Convergence Protocol - Control plane [608], a
5 Radio Link Control (RLC) [610], a Medium Access Control (MAC) [612], a Higher
layer of Physical layer (Higher PHY) [614], a Service Data Adaptation Protocol (SDAP) [616], a Packet Data Convergence Protocol - User plane (PDCP-U) [618] and Open Radio Access Network Management and Orchestration (O-RAN M-Plane) [620]. 10
[0174] The Packet Routing Control (PRC) [606] manages the routing of one or more packets through the network and ensures that data reaches an intended destination efficiently.
15 [0175] The Packet Data Convergence Protocol - Control plane (PDCP-C) [608]
handles the control plane functions, such as encryption and compression.
[0176] The Radio Link Control (RLC) [610] provides an error correction and a flow control and further ensures a reliable data transfer over the radio link. 20
[0177] The Medium Access Control (MAC) [612] controls access to the physical transmission medium and manages data transmission and data receiving over the network.
25 [0178] The Higher layer of Physical layer (Higher PHY) [614] manages the
processing of physical layer data, such as modulation, coding, and other signal processing tasks.
[0179] The Service Data Adaptation Protocol (SDAP) [616] adapts a service data
30 for transmission to maintain a compatibility between different layers of the network.
45

[0180] The Packet Data Convergence Protocol - User plane (PDCP-U) [618] manages the user plane functions, handles the transmission of user data with features like header compression and integrity protection.
5 [0181] The Open Radio Access Network Management and Orchestration (O-RAN
M-Plane) [620] provides management and orchestration capabilities for the Open Radio Access Network.
[0182] The CCDU is further connected to an Open Radio Unit (O-RU) [622] which
10 is responsible for radio transmission and reception. The ORU [622] further
comprises of an Open Radio Access Network- Control, User, and Synchronization
planes (ORAN-C-U-S Plane) [624], a O-RAN M-Plane [620], an Inverse Fast
Fourier Transform / Physical Random Access Channel / Precoding processes
(IFFT/PRACH/Precoding) [628], a Cyclic Prefix addition (CP Addition) [630], a
15 Digital Beamforming (Digital BF) [632], a Power Amplifier / Low Noise Amplifier
(PA/LNA) [634], a Digital Up conversion / Digital Down conversion (DUC/DDC) [636], Crest Factor Reduction (CFR) [638] and a Digital Predistortion (DPD) [626].
[0183] The Open Radio Access Network - Control, User, and Synchronization
20 planes (ORAN-C-U-S Plane) [624] manage the control, user data, and
synchronization functions.
[0184] The O-RAN M-Plane [620] provides management and orchestration capabilities for the Open Radio Access Network.
25
[0185] The DPD [626] refer to a technique used to compensate for the non-linearities of power amplifiers in radio transmission. The DPD conducts a pre-distorting of the input signal with an inverse of an amplifier's distortion characteristics to improves a linearity and an efficiency of the amplifier.
30
46

[0186] The Inverse Fast Fourier Transform / Physical Random Access Channel /
Precoding processes (IFFT/PRACH/Precoding) [628] refers to one or more
processes which handle a conversion of frequency domain data to a time domain
(IFFT), manage the random access procedures (PRACH), and prepare the data for
5 transmission (Precoding).
[0187] The Cyclic Prefix addition (CP Addition) [630] adds a cyclic prefix to the transmitted data to mitigate inter-symbol interference and enhance signal robustness in multipath environments. 10
[0188] The Digital Beamforming (Digital BF) [632] uses one or more digital signal processing techniques to direct and shape the transmission and reception beams, improving signal quality and coverage.
15 [0189] The Power Amplifier / Low Noise Amplifier (PA/LNA) [634] boosts the
signal power for transmission, while the Low Noise Amplifier amplifies the received signal with minimal added noise, enhances overall signal quality.
[0190] The Digital Up conversion / Digital Down conversion (DUC/DDC) [636]
20 refers to one or more processes that convert the baseband digital signals to higher
frequency for transmission (Up conversion) and convert the received high-frequency signals back to baseband (Down conversion).
[0191] The Crest Factor Reduction (CFR) [638] reduces the peak-to-average power
25 ratio of the transmitted signal.
[0192] Referring to FIG. 7, a flow diagram of an exemplary method [700] for
power optimization in a cellular network, in accordance with exemplary
implementations of the present disclosure is shown. In an implementation the
30 method [700] is performed by the system [300]. Further, in an implementation, the
47

system [300] may be present in a server device to implement the features of the present disclosure.
[0193] Also, as shown in FIG. 7, the method [700] starts at step S1, wherein a
5 network management system [602] continuously acquire a real-time data along with
a historical data from a base station [702]. The real-time data along with the historical data may include a data related to a user activity and a network load.
[0194] At step S2, the acquired data in step S1 is processed to prepare for further
10 analysis. The step S2, ensures that the acquired data is in suitable format for
forecasting one or more traffic patterns.
[0195] At step S3, the one or more traffic patterns are forecasted, moreover one or more time periods where a low traffic is expected are identified based on the
15 analysis on the real-time data and the historical data, i.e. prediction of one or more
time periods for receiving a low traffic data, wherein the prediction is based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network. Further, in an implementation the one or more traffic patterns are forecasted by utilising an artificial intelligence engine (depicted as AI
20 in FIG. 7), wherein artificial intelligence engine may implement one or more
forecasting technique to forecast the one or more traffic patterns. Further, the one or more forecasting technique may be a Delphi method based forecasting technique, a moving average based forecasting technique, a time series analysis based forecasting technique, and any other such like technique that may be appreciated
25 by a person skilled in the art to implement the present disclosure.
[0196] Further, as used herein, the “Delphi Method Based Forecasting Technique”
may refer to a collaborative forecasting approach that leverages an input from an
operator and iterative feedback to predict future traffic patterns. The input may
30 comprise of initial predictions, which are then refined through multiple iterations
of feedback and consensus-building, resulting in a collective forecast.
48

[0197] Further, as used herein, the “Moving Average Based Forecasting Technique”
may utilise a statistical method to calculate an average traffic volume over a
specified time period, using historical data to determine fluctuations in the average
traffic volume and identify trends associated with the average traffic volume.
5 Thereafter the Moving Average Based Forecasting Technique may utilise the
average traffic volume, the fluctuations in the average traffic volume and the identified trends associated with the average traffic volume to predict future traffic patterns.
10 [0198] Further, as used herein, the “Time Series Analysis Based Forecasting
Technique” may refer to a statistical model of examining the historical traffic data to identify patterns associated with the traffic data, trends associated with the traffic data, and like parameters. Thereafter, the Time Series Analysis Based Forecasting Technique may further analyse the identified patterns associated with the traffic
15 data and the trends associated with the traffic data, to train the statistical model for
predicting a future traffic patterns.
[0199] At step S4, one or more timeslots are determined based on the forecasting
20 done at step S3 for one or more energy saving actions, for reducing a power output
during the low traffic, i.e., reduction of a power output of one or more components in the cellular network, the one or more components including a power amplifier (PA) and one or more transmission and reception (TRX) symbols.
25 [0200] At step S5, one or more traffic scenarios are identified, and one or more
threshold values (at step S6) are determined related to the traffic data. Further, the one or more threshold values (at step S6) helps to decide when to reduce or increase the power output. Further, in an implementation the one or more traffic scenarios are identified, and the one or more threshold values are determined by utilising the
30 artificial intelligence engine (depicted as AI in FIG. 7), wherein artificial
intelligence engine may implement one or more identification technique to identify
49

the one or more traffic scenarios and determine the traffic data. Further, the one or
more identification technique may be a network traffic classification based
identification technique, a Deep Packet Inspection (DPI) based identification
technique, a Network Performance Monitoring based identification technique, a
5 Network Protocol Analysis based identification technique, a Signature-Based
Detection technique, a Traffic Profiling based detection technique, and any other such like technique that may be appreciated by a person skilled in the art to implement the present disclosure.
10 [0201] Further, the one or more traffic scenarios may be an internet browsing traffic
scenario may occur when a particular user browses an e-commerce website, adds items to their cart, and makes a purchase, generating HTTP/HTTPS traffic, a streaming traffic scenario which may occur when a particular user watches a video on a streaming platform generating HTTP/HTTPS traffic with high bandwidth
15 requirements, a software upgrade scenario which may occur when a device
downloads software updates or patches, generating HTTP/HTTPS traffic with large file transfers, an Internet of Things (IoT) device communication traffic scenario which may occur when IoT devices, like smart home devices or industrial sensors, communicate with the cloud or a central server, generating machine-to-machine
20 (M2M) traffic, and alike.
[0202] Further, as used herein, the “Deep Packet Inspection (DPI) based
identification technique” may refer to a technique that may examine a traffic data
packets to identify specific traffic scenarios, such as applications, services, or user
25 behaviour. Further, the DPI based identification technique may also analyse packet
payloads and headers to classify traffic and detect anomalies associated with the traffic data packets.
[0203] Further, as used herein, the “Network Performance Monitoring based
30 identification technique” may refer to a technique that analyses network metrics,
such as a latency metric, a jitter metric, a data packet loss metric, and a throughput
50

metric, to identify traffic scenarios that may impact a network performance. Further, the Network Performance Monitoring based identification technique based on said analysis may detect a network congestion scenario, a network bottleneck scenario, and any other such like scenarios. 5
[0204] Further, as used herein, the “Network Protocol Analysis based identification
technique” may refer to a technique that analyses network protocols, such as a
Transmission Control Protocol, a User Datagram Protocol, a Domain Name
System, and a Hypertext Transfer Protocol, to identify specific traffic scenarios.
10 Further, the Network Protocol Analysis based identification technique may further
analyse protocol headers and one or more protocol fields to identify traffic patterns to detect fluctuations associated with the traffic patterns.
[0205] Further, as used herein, the “Signature-Based Detection technique” may
15 refer to a technique that identify traffic data patterns i.e., a signature, associated
with a specific network traffic that can be used to identify and classify different
types of network traffic and to identify specific traffic scenarios, such as malware,
a distributed denial-of-service (DDoS) attack, and alike. Further, the Signature-
Based Detection technique may compare the identified traffic data patterns against
20 a database of known signatures to detect and classify the different types of network
traffic.
[0206] Further, during the identified low traffic time periods, the power output of
one or more components such as power amplifiers (PA) and transmission and
25 reception (TRX) symbols, is reduced based on one or more pre-defined threshold
values (i.e., a second threshold value).
[0207] Thereafter, at step S7, the status associated with the network management
system [602] is updated to reflect one or more changes in the power output. Further
30 an amount of power saved is calculated and one or more actions are facilitated such
51

as maintaining reduced power, triggering low power mode, or exiting low power mode based on real-time traffic conditions.
[0208] The method [700] terminates upon completion of S6. 5
[0209] Another aspect of the present disclosure may relate to a user equipment (UE) for power optimization in a cellular network. The UE comprises a memory; and a processor coupled to the memory. The processor is configured to transmit to a system [300], a prediction of one or more time periods for receiving a low traffic
10 data, wherein the prediction is based on a historical traffic data and a real-time
traffic data associated with one or more users in the cellular network. The processor is configured to receiving from the system [300], a response associated with the prediction. The response is received based on reducing, by the system [300], a power output of one or more components in the cellular network, the one or more
15 components including a power amplifier (PA) and one or more transmission and
reception (TRX) symbols. Further, reducing the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data. Further, the response is received based on updating, by the system [300], a system status to reflect the reduction in the power output of the one or more
20 components. Further, the response is received based on calculating, by the system
[300], an amount of power saved by reducing the power output of the one or more components. Further, the response is received based on facilitating, by the system [300], at least one of a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low
25 power mode.
[0210] Yet another aspect of the present disclosure may relate to a non-transitory
computer readable storage medium storing instructions for power optimization in a
cellular network. The instructions include executable code which, when executed
30 by one or more units of a system [300] causes a prediction unit [302] of the system
[300] to predict, one or more time periods for receiving a low traffic data, the
52

prediction based on a historical traffic data and a real-time traffic data associated
with one or more users in the cellular network. Further, the executable code when
executed causes a power optimization unit [304] of the system [300] to reduce, a
power output of one or more components in the cellular network, the one or more
5 components including a power amplifier (PA) and one or more transmission and
reception (TRX) symbols. Further, reducing the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data. Further, the executable code when executed causes an updation unit [306] of the system [300] to update, by a system status to reflect the reduction in
10 the power output of the one or more components. Further, the executable code
which, when executed causes an analysis unit [308] of the system [300] to calculate, an amount of power saved by reducing the power output of the one or more components. Thereafter, the executable code when executed causes the analysis unit [308] of the system [300] to facilitate, at least one of a trigger for entering a low
15 power mode, a maintenance of the reduced power output of one or more
components, and an exit from an existing low power mode.
[0211] As is evident from the above, the present disclosure provides a technically
advanced solution for power optimization in a cellular network. The present
20 solution provides a holistic approach for saving energy in base station by utilizing
machine learning techniques to predict an optimal energy saving mode, which helps
to reduce the power consumption. Further, the present solution dynamically adjusts
one or more radio frequency (RF) configurations by intelligently switching one or
more power amplifiers (PAs) and one or more Massive Multiple-Input, Multiple-
25 Output (M-MIMO) channels, on and off, based on real-time traffic and channel
conditions. The present solution proactively manages RF configuration states to
optimize energy consumption without compromising network performance through
artificial intelligence and/or machine learning technology. The present solution not
only reduces power consumption through precise PA and symbol management but
30 also minimizes latency by ensuring that critical symbols remain active based on
user behaviour patterns. Hence, the present solution provides a real-time energy-
53

saving approach that enhances operational efficiency and sustainability in the cellular network.
[0212] While considerable emphasis has been placed herein on the disclosed implementations, it will be appreciated that many implementations can be made and that many changes can be made to the implementations without departing from the principles of the present disclosure. These and other changes in the implementations of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.
[0213] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.

We Claim:
1. A method for power optimization in a cellular network, the method comprising:
- predicting, by a prediction unit [302], one or more time periods for receiving a low traffic data, the prediction based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network;
- reducing, by a power optimization unit [304], a power output of one or more components in the cellular network, the one or more components including a power amplifier (PA) and one or more transmission and reception (TRX) symbols,
wherein reducing the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data;
- updating, by an updation unit [306], a system status to reflect the reduction in the power output of the one or more components;
- calculating, by an analysis unit [308], an amount of power saved by reducing the power output of the one or more components; and
- facilitating, by the analysis unit [308], at least one of a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low power mode.
2. The method as claimed in claim 1, wherein prior to the predicting, by the
prediction unit [302], the one or more time periods for receiving the low traffic
data, the method comprises:
- monitoring continuously, by the prediction unit [302], the real-time traffic data associated with the one or more users of the cellular network; and

- storing, at a storage unit [310], the continuous monitoring of the real¬
time traffic data based on a predefined criteria.
3. The method as claimed in claim 2, wherein the predefined criteria comprises one or more of a total traffic data, a radio release control (RRC) connected user data, a cell throughput data, a physical resource block (PRB) utilization data, an application layer data, a crowd sourced data, a low traffic conditions data, and a specific TRX symbols requirement data.
4. The method as claimed in claim 1, the method comprising:

- monitoring continuously, by the prediction unit [302], the real-time traffic data associated with the one or more users; and
- determining, by the prediction unit [302], one of a threshold breach status and a threshold keep status based on the continuous monitoring of the real-time traffic data,
wherein the threshold breach status is determined in an event the amount of the real-time traffic is above a second pre-defined threshold value, and the threshold keep status is determined in an event the amount of the real-time traffic is below the second pre-defined threshold value.
5. The method as claimed in claim 4, wherein for the facilitating, by the analysis
unit [308], the exit from the existing low power mode, the method comprises:
- determining, by the analysis unit [308], one of a positive reactivation and a negative reactivation based on the continuous monitoring of the real-time traffic data,
wherein the positive reactivation is determined in an event of at least one of the determination of the threshold breach status and a specific TRX symbol need, and the negative reactivation is determined in an event of the determination of the threshold keep status, and

wherein the positive reactivation is associated with increase in power output of one or more target components, and the negative reactivation is associated with maintenance of reduced power output of the one or more target components;
- identifying, by the analysis unit [308], a set of one or more of target TRX symbols and target power amplifiers for positive reactivation, in an event of determination of the positive reactivation;
- increasing, by the power optimization unit [304], the power output of the identified set of one or more target TRX symbols and target power amplifiers, for reactivation;
- updating, by the updation unit [306], a system status to reflect the increase in the power output of the set of one or more target TRX symbols and target power amplifiers; and
- calculating, by the analysis unit [308], an amount of power consumed by the set of one or more target TRX symbols and target power amplifiers by reactivation.
6. The method as claimed in claim 1, wherein the facilitating at least one of the
entering the low power mode and the maintenance of the reduced power output
of one or more components further comprises:
- detecting, by the analysis unit [308], a low latency traffic data based on the continuous monitoring of the real-time traffic data; and
- prioritising, by the analysis unit [308], a transmission of the low latency traffic data to the one or more users of the cellular network.
7. A system [300] for power optimization in a cellular network, the system [300]
comprising:
- a prediction unit [302] configured to predict one or more time periods for
receiving a low traffic data, the prediction based on a historical traffic data
and a real-time traffic data associated with one or more users in the cellular
network;

- a power optimization unit [304] connected to at least the prediction unit
[302], the power optimization unit [304] configured to reduce a power
output of one or more components in the cellular network, the one or
more components including a power amplifier (PA) and one or more
transmission and reception (TRX) symbols,
wherein reduction of the power output of the PA is based on the predicted one or more time periods and a pre-defined threshold value related to the traffic data;
- an updation unit [306] connected to at least the power optimization unit [304], the updation unit [306] configured to update a system status to reflect the reduction in the power output of the one or more components; and
- an analysis unit [308] connected to at least the updation unit [306], the analysis unit [308] configured to:
o calculate an amount of power saved by reducing the power output of the one or more components, and
o facilitate at least one of a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low power mode.
8. The system [300] as claimed in claim 7, wherein the prediction unit [302] prior to predicting the one or more time periods for receiving the low traffic data, is configured to:
- monitor continuously, the real-time traffic data associated with the one or more users of the cellular network; and
- store, at a storage unit [310], the continuous monitoring of the real-time traffic data based on a predefined criteria.

9. The system as claimed in claim 8, wherein the predefined criteria comprises one or more of a total traffic data, a radio release control (RRC) connected users’ data, a cell throughput data, a physical resource block (PRB) utilization data, an application layer data, a crowd sourced data, a low traffic conditions data, and a specific TRX symbols requirement data.
10. The system [300] as claimed in claim 7, wherein the prediction unit [302] is further configured to:

- monitor continuously, the real-time traffic data associated with the one or more users; and
- determine one of a threshold breach and a threshold keep status based on the continuous monitoring of the real-time traffic data,
wherein the threshold breach status is determined in an event the amount of the real-time traffic is above a second pre-defined threshold value, and the threshold keep status is determined in an event the amount of the real-time traffic is below the second pre-defined threshold value.
11. The system [300] as claimed in claim 10, wherein the analysis unit [308], for
facilitating the exit from the existing low power mode, is configured to:
- determine one of a positive reactivation and a negative reactivation based on the continuous monitoring of the real-time traffic data,
wherein the positive reactivation is determined in an event of at least one of the determination of the threshold breach status and a specific TRX symbol need, and the negative reactivation is determined in an event of the determination of the threshold keep status, and
wherein the positive reactivation is associated with increase in power output of one or more target components, and the negative reactivation is associated with maintenance of reduced power output of the one or more target components;

wherein the analysis unit [308] is further configured to identify a set of one or more of target TRX symbols and target power amplifiers for positive reactivation, in an event of determination of the positive reactivation, wherein the power optimization unit [304] is further configured to increase the power output of the identified set of one or more target TRX symbols and target power amplifiers, for reactivation;
wherein the updation unit [306] is further configured to update a system status to reflect the increase in the power output of the set of one or more target TRX symbols and target power amplifiers; and
wherein the analysis unit [308] is further configured to calculate an amount of power consumed by the set of one or more target TRX symbols and target power amplifiers by reactivation.
12. The system [300] as claimed in claim 7, wherein to facilitate the at least one of
the entering the low power mode and the maintenance of the reduced power
output of one or more components, the analysis unit [308] is further configured
to:
- detect, a low latency traffic data based on the continuous monitoring of the real-time traffic data, and
- prioritise, a transmission of the low latency traffic data to the one or more users of the cellular network.
13. A user equipment (UE) for power optimization in a cellular network, the UE
comprising:
- a memory; and
- a processor coupled to the memory, wherein the processor is configured to: o transmit to a system [300], a prediction of one or more time periods for
receiving a low traffic data, wherein the prediction is based on a historical traffic data and a real-time traffic data associated with one or more users in the cellular network, and

o receiving from the system [300], a response associated with the prediction;
wherein the response is received based on: reducing, by the system [300], a power output of one or more components in the cellular network, the one or more components including a power amplifier (PA) and one or more transmission and reception (TRX) symbols,
wherein reducing the power output of the PA is based on the
predicted one or more time periods and a pre-defined
threshold value related to the traffic data, updating, by the system [300], a system status to reflect the reduction in the power output of the one or more components,
calculating, by the system [300], an amount of power saved by reducing the power output of the one or more components, and facilitating, by the system [300], at least one of a trigger for entering a low power mode, a maintenance of the reduced power output of one or more components, and an exit from an existing low power mode.