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 PROACTIVE ENERGY 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. 2 METHOD AND SYSTEM FOR PROACTIVE ENERGY OPTIMIZATION IN A CELLULAR NETWORK FIELD OF INVENTION 5 [0001] Embodiments of the present disclosure generally relate to network performance management systems. More particularly, embodiments of the present disclosure relate to methods and systems for proactive energy optimization in a cellular network. 10 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 15 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. 20 [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 25 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 3 (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. 5 [0004] In wireless communication technologies, it is important to optimize the energy of cells and therefore several solutions have been developed for energy optimization. Existing solutions often rely on reactive mechanisms and include triggering sleep modes based on observed low traffic conditions, wherein in a 10 sleep mode one or more functionalities of a cell or a radio unit of a cellular network are turned off or disabled for a specific time period. These reactive approaches of the existing solutions can lead to inefficiencies and latency in network performance, as they must wait for low usage periods to trigger energy-saving modes (i.e., sleep modes). 15 [0005] Conventional energy-saving techniques frequently use fixed thresholds or timers to transition between active and sleep states. In the active state (or active mode) the one or more functionalities of a cell or a radio unit of a cellular network is turned on or enabled. This fixed thresholds or timers-based transition between 20 active and sleep states can lead to sub-optimal energy savings, as these thresholds / timers may not adapt to dynamic network conditions and traffic patterns. Further, the energy-saving modes in existing arts typically lack granularity. They often offer a binary choice between a fully active state and a sleep state, without considering intermediate stages that could offer more balanced energy savings 25 and performance. Currently known techniques fail to efficiently and effectively identify the most appropriate times to activate or deactivate sleep modes, potentially leading to inefficient use of resources. Moreover, the prior solutions may not be adequately adaptable to changes in network conditions, such as fluctuations in network load, varying Quality of Service (QoS) requirements, or 4 changing user behaviour patterns. This lack of adaptability could lead to energy wastage and reduced network performance. [0006] Thus, there exists an imperative need in the art for a technical solution that 5 aims to address at least the above-mentioned technical issues by managing an energy consumption in cellular networks in an efficient and effective manner. SUMMARY 10 [0007] 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. 15 [0008] An aspect of the present disclosure may relate to a method for proactive energy optimization in a cellular network. The method comprising monitoring, by a monitoring unit, a network traffic load associated with a plurality of cells of the cellular network. Further, the method encompassing predicting, by a predicting unit using a trained model, a traffic pattern for at least one cell of the plurality of 20 cells based on the monitored network traffic load. Further, the method encompassing identifying, by an identifying unit, one or more radio units (RUs) for transitioning into one or more sleep modes based on the predicted traffic pattern. Furthermore, the method comprises initiating, by a processing unit, at least one of the one or more sleep modes for each of the identified one or more RUs to 25 optimize power consumption. [0009] In an exemplary aspect of the present disclosure, the method further comprises quantifying, by the processing unit, an energy saving achieved by the at 5 least one of the one or more sleep modes by mapping a power utilization to a physical resource block (PRB) usage. [0010] In an exemplary aspect of the present disclosure, the method further 5 comprises reactivating, by the processing unit, the one or more RUs from the one or more sleep modes based at least on one of a traffic load breaching a predefined traffic threshold, and a set of predefined triggers. [0011] In an exemplary aspect of the present disclosure, the method further 10 comprises predicting, by the predicting unit, one or more coverage holes within the cellular network based on the one or more RUs being in the at least one of the one or more sleep modes. [0012] In an exemplary aspect of the present disclosure, the method further 15 comprises utilizing, by the processing unit, one or more neighbour cells for balancing the network traffic load and maintaining coverage during the one or more sleep modes. [0013] In an exemplary aspect of the present disclosure, the method further 20 comprises providing, by the processing unit, a neighbour management profile based on at least one of a geographical location and a handover (HO) count for each of the one or more sleep modes for each of the one or more RUs, to facilitate in managing neighbour relationship and provide a list of targeted neighbours for coverage hole analysis and a load balancing. 25 [0014] In an exemplary aspect of the present disclosure, the trained model is one of a Support Vector Machine (SVM), and a neural network. 6 [0015] In an exemplary aspect of the present disclosure, the model is trained based on a dataset comprising a historical network performance data and a corresponding traffic load pattern observed within the cellular network. 5 [0016] In an exemplary aspect of the present disclosure, the one or more sleep modes are selected from a group comprising of a radio unit (RU) Hibernation mode, a Deep Sleep mode, a Light Sleep mode, an Uplink (UL) Transmission Only mode, and a Downlink (DL) Transmission Only mode. 10 [0017] In an exemplary aspect of the present disclosure, the one or more sleep modes pertain to one or more levels of one or more energy-saving techniques without causing one or more service interruptions in the at least one cell. [0018] In an exemplary aspect of the present disclosure, the method further 15 comprises creating, by the processing unit, at least one profile for each of the one or more sleep modes for one or more cells, where each of the at least one profile comprises a profile mode for an advanced tilt optimization, a power optimization, and a scheduling methodology to meet a desired quality of service (QoS), and wherein each of the one or cells comprises at least a set of RUs of the one or more 20 RUs. [0019] In an exemplary aspect of the present disclosure, the method further comprises receiving, by the processing unit, an input from a power management system at a base station to activate the one or more sleep modes, wherein the 25 input comprises at least one of a battery output and an alternating current (AC). [0020] In an exemplary aspect of the present disclosure, the method further comprises identifying, by the identifying unit, a power emergency with the one or 7 more cells, the power emergency comprises at least one of low battery and operating on diesel generator. [0021] In an exemplary aspect of the present disclosure, the method further 5 comprises activating, by the processing, the one or more sleep modes based on the identified power emergency. [0022] In an exemplary aspect of the present disclosure, the method further comprises using self-optimizing network performance, a load balancing, and 10 coverage hole prediction to meet one or more QoS requirements. [0023] In an exemplary aspect of the present disclosure, the network traffic load comprises at least one of a data transmission volume, a user activity, and one or more resource utilization metrics within the cellular network. 15 [0024] In an exemplary aspect of the present disclosure, the traffic pattern corresponds to a forecast period of low activity and a potential for an energy saving. 20 [0025] Another aspect of the present disclosure may relate to a system for proactive energy optimization in a cellular network. The system comprises a monitoring unit that is configured to monitor a network traffic load associated with a plurality of cells of the cellular network. Further, the system comprises a predicting unit that is configured to predict, using a trained model, a traffic pattern 25 for at least one cell of the plurality of cells based on the monitored network traffic load. Further, the system comprises an identifying unit configured to identify one or more radio units (RUs) for transitioning into one or more sleep modes based on the predicted traffic pattern. Furthermore, the system comprises a processing unit 8 that is configured to initiate at least one of the one or more sleep modes for the identified one or more RUs to optimize power consumption. [0026] Yet another aspect of the present disclosure may relate to a non-transitory 5 computer readable storage medium storing instructions for proactive energy optimization in a cellular network, the instructions include executable code which, when executed by one or more units of a system, causes: a monitoring unit of the system to monitor a network traffic load associated with a plurality of cells of the cellular network; a predicting unit of the system to predict, using a trained model, 10 a traffic pattern for at least one cell of the plurality of cells based on the monitored network traffic load; an identifying unit of the system to identify one or more radio units (RUs) for transitioning into one or more sleep modes based on the predicted traffic pattern; and a processing unit of the system to initiate at least one of the one or more sleep modes for the identified one or more RUs to optimize power 15 consumption. OBJECTS OF THE DISCLOSURE [0027] Some of the objects of the present disclosure, which at least one 20 embodiment disclosed herein satisfies are listed herein below. [0028] It is an object of the present disclosure to provide a system and a method for proactive energy optimization in a cellular network. 25 [0029] It is another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that improve energy efficiency in 5G networks, particularly during periods of low or no data transmission. 9 [0030] It is another object of the present disclosure to achieve improved energy efficiency in cellular networks through a proactive identification and management of multi-level advanced sleep modes. 5 [0031] It is another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that reduce the operational costs associated with energy usage in 5G networks by optimizing power consumption. 10 [0032] It is another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that by taking into account load conditions and Quality of Service (QoS) requirements, seeks to ensure that network performance remains optimal even while energy consumption is reduced. 15 [0033] It is another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that provide a precise quantification of power saving, and mapping of power utilization to physical resource block (PRB) usage. 20 [0034] It is another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that aims to employ a proactive approach in identifying cells for action, relying on machine learning techniques for traffic prediction. 25 [0035] It is another object of the present disclosure to provide a solution that takes into account variations in traffic patterns over time, day of the week, and seasonal changes etc., to provide a more efficient and responsive cellular network. 10 [0036] It is another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that seeks to implement a range of advanced sleep modes, such as radio unit (RU) Hibernation, Deep Sleep, Light Sleep, and Uplink (UL) Transmission Only etc., providing a 5 granular approach to energy savings and ensuring an efficient response to diverse network conditions. [0037] It is yet another object of the present disclosure to provide a system and method for proactive energy optimization in cellular networks that aims to be 10 easily adoptable into standard technology, offering improvements in energy efficiency and network performance without necessitating significant infrastructural changes. DESCRIPTION OF THE DRAWINGS 15 [0038] 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 20 necessarily to scale, 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 25 that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components. 11 [0039] Figure 1 illustrates a multi-vendor self-organizing network (SON) for proactive energy optimization in a cellular network, in accordance with exemplary implementations of the present disclosure. 5 [0040] Figure 1A illustrates an exemplary block diagram of a radio access network (RAN) management layer and the RAN layer, in accordance with exemplary implementations of the present disclosure. [0041] Figure 1B illustrates another exemplary block diagram of a radio access 10 network (RAN) management layer and the RAN layer, in accordance with exemplary implementations of the present disclosure. [0042] Figure 2 illustrates an exemplary block diagram of a system for proactive energy optimization in a cellular network in accordance with exemplary 15 implementations of the present disclosure. [0043] Figure 3 illustrates a method flow diagram for proactive energy optimization in cellular networks in accordance with exemplary implementations of the present disclosure. 20 [0044] Figure 4 illustrates a timeline for implementing the method as per the present disclosure in accordance exemplary implementations of the present disclosure. 25 [0045] Figure 5 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. 12 [0046] The foregoing shall be more apparent from the following more detailed description of the disclosure. DETAILED DESCRIPTION 5 [0047] In the following description, for the purposes of explanation, various 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 10 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 address any of the problems discussed above or might address only some of the problems discussed above. 15 [0048] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, 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 20 function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth. [0049] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of 25 ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. 13 [0050] 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 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 5 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. [0051] The word “exemplary” and/or “demonstrative” is used herein to mean 10 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 necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and 15 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 similar to the term “comprising” as an open transition word—without precluding any additional or other elements. 20 [0052] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a 25 plurality 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, input/output processing, and/or any other functionality that enables 14 the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor. [0053] As used herein, “a user equipment”, “a user device”, “a smart-user5 device”, “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 10 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. 15 [0054] 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 computerreadable medium includes read-only memory (“ROM”), random access memory 20 (“RAM”), 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. 25 [0055] As used herein “interface” or “user interface refers to a shared boundary across which two or more separate components of a system exchange information 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 15 each other, which also includes the methods, functions, or procedures that may be called. [0056] All modules, units, components used herein, unless explicitly excluded 5 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 microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field 10 Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc. [0057] As used herein the transceiver unit include at least one receiver and at least one transmitter configured respectively for receiving and transmitting data, 15 signals, information, or a combination thereof between units/components within the system and/or connected with the system. [0058] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various the 20 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. 25 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. 16 [0059] As discussed in the background section, the current known solutions have several shortcomings. The present disclosure aims to overcome the abovementioned and other existing problems in this field of technology by providing method and system for proactive energy optimization in a cellular network. 5 [0060] The present disclosure relates to a method and a system for proactive energy optimization in a cellular network. The present disclosure provides a system and method for proactive energy optimization in cellular networks that aims to be easily adoptable into standard technology, offering improvements in 10 energy efficiency and network performance without necessitating significant infrastructural changes. Further the present disclosure provides precise quantification of power saving, mapping power utilization to physical resource block (PRB) usage. This enables an accurate assessment of the energy-saving impacts. Further the present disclosure provides implementation of a range of 15 advanced sleep modes, such as radio unit (RU) Hibernation, Deep Sleep, Light Sleep, and Uplink (UL) Transmission Only, providing a granular approach to energy savings and ensuring an efficient response to diverse network conditions. Furthermore, the present disclosure provides proactive energy optimization in cellular networks by taking into account load conditions and Quality of Service 20 (QoS) requirements, seeks to ensure that network performance remains optimal even while energy consumption is reduced, and reduces the operational costs associated with energy usage in cellular networks such as 5G networks by optimizing power consumption. 25 [0061] More specifically, the present disclosure provide a solution that at least encompasses: 1) monitoring a network traffic load associated with a plurality of cells of the cellular network, 2) predicting, using a trained model, a traffic pattern for at least one cell of the plurality of cells based on the monitored network traffic load, 3) identifying, one or more radio units (RUs) for transitioning into one or 17 more sleep modes based on the predicted traffic pattern, and 4) initiating, at least one of the one or more sleep modes for each of the identified one or more RUs to optimize power consumption. 5 [0062] Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the accompanying drawings. [0063] Referring to Figure 1, that illustrates a multi-vendor self-organizing network (SON) for proactive energy optimization in a cellular network, in 10 accordance with exemplary implementations of the present disclosure. Particularly, the multi-vendor SON provides proactive energy optimization in the cellular network with the help of the system [200] as depicted in Figure 2. [0064] As indicated in the Figure 1, the multi-vendor SON architecture for 15 proactive energy optimization in the cellular network comprises at least one Intelligent Platform/Non-Real Time Radio Intelligent Controller (NRT-RIC) layer [102], at least one service management and orchestration (SMO) layer/ a selforganizing network (SON) layer [104], at least one network management layer [106], and at least one Radio Access Network (RAN) Layer [114]. Further, the RAN 20 layer [114] is connected to the SMO layer/ SON layer [104] by a Centralized SON (CSON) interface [156]. Also, in Figure 1 only a few units are shown, however, the multi-vendor SON architecture may comprise multiple such units or the multivendor SON architecture may comprise any such numbers of said units, as required to implement the features of the present disclosure. 25 [0065] Further, the RAN layer [114] comprises a combined centralized and distributed unit (CCDU) [116] and an open radio access network radio unit (O-RU) [136]. The CCDU [116] is a unit which performs the operation for both a centralized unit and a decentralized unit and handles both the higher layers and the lower 18 layers of a protocol stack, which includes the higher/upper physical layer [128], a media access control (MAC) layer [126], and a radio link control (RLC) layer [124] and also a service data adaptation protocol (SDAP) layer, a packet data convergence protocol (PDCP) layer, and a radio resource control (RRC) layer. The 5 O-RU [136] is a component of open radio access network (O-RAN) that perform the function of radio access nodes and connects the user equipment with the wireless communication network. The OFH (ORAN Front Haul)/ M-Plane [134] as depicted in the Figure 1 is an interface which is responsible for managing the ORU. 10 [0066] The CCDU [116] comprises of a Radio Resource Control (RRC) Packet Data Convergence Protocol - Control (PDCP-C) [118] (referred herein as RRC PDCP-C [118]), a Service Data Adaptation Protocol - User (SDAP PDCP-U) [120] (referred herein as SDAP PDCP-U [120]), a distributed self-organizing network (DSON) [122], 15 the Radio Link Control (RLC) layer [124], the Medium Access Control (MAC layer) [126], the higher physical layer [128], an Open Radio Access Network - Centralized Unit - User-Plane (ORAN-C-U-S Plane) [130], and an Open Radio Access Network Management Plane (O-RAN M-Plane) [132]. The Distributed Self-Organizing Network (DSON) [122] allows the network to automatically optimize itself without 20 manual intervention. Further the RRC PDCP-C [118] comprises of the Radio Resource Control protocol (RRC) and the Packet Data Convergence Protocol - Control (PDCP-C). The RRC manages radio resources like assigning channels and power. Further, the Packet Data Convergence Protocol - Control (PDCP-C) prepares data for transmission (PDCP-C for control data, PDCP-U for user data). 25 The PDCP-C is located in the air interface on the top of the RLC layer [124]. The Radio Link Control (RLC) layer [124] ensures reliable data delivery over the radio link. 19 [0067] Further, the Medium Access Control (MAC) layer [126], manages how different devices share the radio channel. Further, the higher physical layer [128], transmits and receives raw radio signals. Furthermore, the ORAN-C-U-S Plane (Centralized Unit - User-Plane) [130], splits the processing between a central unit 5 and distributed units for user data handling. Moreover, the O-RAN M-Plane (Management Plane) [132] manages the overall network configuration. [0068] The O-RU [136] comprises at least one of the ORAN-C-U-S Plane [130], the O-RAN M-Plane [132], an Inverse Fast Fourier Transform / Preamble Format 10 (PRACH) (IFFT/PRACH) Precoding [138], a Channel Frequency Response (CFR) [146], a Cyclic Prefix (CP) Addition [140], a digital Beamforming (digital BF) [142], a digital pre-distortion (DPD) [144], a digital up converter (DUC)/ a digital down converter (DDC) [148], a Power Amplifier (PA)/ a low-noise amplifier (LNA) [150], an analog-digital converter (ADC)/ a digital-analog converter (DAC) [152], a 15 duplexer/ a circulator [154]. Furthermore, the IFFT/PRACH Precoding [138] comprises of the Inverse Fast Fourier Transform (IFFT) and the Preamble Format (PRACH) Precoding. The Inverse Fast Fourier Transform (IFFT) converts frequencydomain data into time-domain signals, essential for OFDM (Orthogonal Frequency Division Multiplexing) transmission. Further, the Preamble Format (PRACH) 20 Precoding, prepares the random-access preamble for transmission, enabling initial access and synchronization between the UE and the network. Further, the Channel Frequency Response (CFR) [146] measures and characterizes how the transmitted signal is altered by the propagation channel. This information is used to compensate for channel impairments and to improve signal quality. The Cyclic 25 Prefix (CP) Addition [140], adds a cyclic prefix to each OFDM symbol to mitigate inter-symbol interference (ISI) caused by multipath propagation. This enhances the robustness of the transmitted signal. Moreover, the digital BF (Beamforming) [142] utilizes digital signal processing to direct the transmission and reception of 20 signals in specific directions, improving signal strength and reducing interference. This enhances overall network performance and coverage. [0069] The DPD [144] is a baseband signal processing technique that corrects the 5 impairments in RF power amplifiers (PAs). The digital up converter (DUC) [148] is a device which translates a signal from baseband to intermediate frequency (IF) band, and the digital down converter (DDC) [148] is a device which converts a signal from intermediate frequency band to baseband. The PA [150] is a type of electronic amplifier that converts a low-power radio-frequency (RF) signal into a 10 higher-power signal. The LNA [150] is a component at the front-end of a radio receiver circuit which reduces the unwanted noises in the radio signal. The ADC [152] is a device that converts an analogue signal into a limited number of digital output codes and the DAC [152] is a device that converts a limited number of digital output codes into an analogue signal. The duplexer [154] is an electronic 15 device that allows bi-directional (duplex) communication over a single path and isolates the receiver from the transmitter while permitting them to share a common antenna. The circulator [154] is a passive, non-reciprocal three-port or four-port device that only allows a microwave or radio-frequency (RF) signal to exit through the port directly after the one it entered. 20 [0070] Moreover, for proactive energy optimization in a cellular network the system [200] works in conjunction with at least one of the Intelligent Platform/NRT-RIC layer [102], the service management and orchestration (SMO) layer/ the self-organizing network (SON) layer [104], and the network 25 management layer [106]. [0071] Particularly, the Intelligent Platform/NRT-RIC layer [102] provides efficient control and optimization / regulation of network functions and services in realtime or near-real-time through fine-grained data collection and actions. The 21 intelligent platform layer/ the NRT-RIC layer [102] performstasks such as including but not limited to scenario identification, predictive analysis of one or more traffic levels, parameter optimization for example, regulating energy consumption by optimizing power usage of the one or more cell sites, training of an artificial 5 intelligence / machine learning (AI/ML) model based on AI/ML techniques and data aggregations. The intelligent platform layer/ the NRT-RIC layer [102] collects data from a site data base, configuration parameters from a configuration management (CM) module [112], performance counters from a performance management (PM) module [110] and alarms from the fault management (FM) and 10 alarm handling module [108] for all elements from the RAN layer via the network management layer [106]. [0072] The service management and orchestration (SMO) layer/ self-organizing network (SON) layer [104] manages the components and network functions of an 15 open RAN. The self-organizing network (SON) layer [104] comprises an automation technology designed to make the planning, configuration, management, optimization and healing of mobile radio access networks in a simpler and faster way. The SMO layer/SON layer is responsible for making/implementing policies, modelling and slicing the network, and also performing the functions for user 20 session management, medium access/radio management, device downlink/uplink management, and data acquisition. The SON layer [104] acts as a central intelligence controller, communicating with the network management layer [106], CCDU [116], and O-RU [136] through standardized interfaces and incorporates above mentioned AI/ML policies to make intelligent decisions for optimizing 25 network performance, resource allocation, and energy efficiency. The macro gNodeBs /outdoor small cell (ODSC)/indoor small cell (IDSC) integrate with the SON/SMO layer [104] directly. The SON layer [104] houses SON techniques for network optimization, acting as the brain. It leverages a network operator platform for other vendor gNodeBs and interfaces directly with such gNodeBs 22 from other vendors network operator through its network management layer [106] for its nodes. [0073] The present disclosure provides a solution that is implemented in the 5 intelligent platform layer/ the NRT-RIC layer [102]. It communicates with the network management layer [106] to gather details such as network information, performance metrics, and configuration data. It exchanges control signals and receives instructions from the network management layer [106] to orchestrate and optimize the RAN operations. 10 [0074] Referring to Figure 1A and Figure 1B that illustrates an exemplary block diagram of a RAN management layer [158] and the RAN layer [114], the RAN management layer [158] (i.e., Intelligent Platform/NRT-RIC Layer [102] and the Network Management Layer [106]) collects data for monitoring the performance 15 of the one or more cell sites and implement one or more sleep modes. The performance monitoring includes monitoring at least the one or more current or past traffic levels, and the predicted one or more traffic levels, a coverage map, etc. For model training, the RAN management layer [158] utilizes the AI/ML techniques for effective and efficient functioning of the RAN management layer 20 [158]. For predictive analysis, the RAN management layer [158] utilizes the historic data associated with one or more traffic levels at the one or more cell sites in the past. The RAN management layer [158] is responsible for triggering the one or more sleep modes, if the predefined thresholds for the one or more traffic levels is satisfied by the selected cell site. For instance, Figure 1A depicts that if for a 25 monitoring period a Threshold T1>LTS threshold (long-term statistical threshold,), then the RAN management layer [158] will trigger the RU Hibernation mode and Figure 1B depicts that if for a monitoring period say M1 the T1