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 OPTIMISING NETWORK PERFORMANCE BASED ON COMPUTATION TIME OF COMPUTATIONAL UNITS”
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 OPTIMISING NETWORK PERFORMANCE BASED ON COMPUTATION TIME OF COMPUTATIONAL UNITS
FIELD OF THE DISCLOSURE
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[0001] The present disclosure relates generally to the field of wireless communication
systems. More particularly, the present disclosure relates to methods and systems for
measuring one or more Network Function/ Nodes (NFs) performance and providing
insights on performance of computational unit(s) of such network node(s), which helps in
10 optimization of the Node performance.
BACKGROUND
[0002] The following description of related art is intended to provide background
15 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 be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of 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 services became possible, and text messaging
25 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
30 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.
2

[0004] Further, over the period of time various solutions have been developed to improve
the performance of various nodes in a wireless communication network (referred herein as
network nodes). However, there are certain challenges with existing solutions for instance
the existing solutions are not efficient in measurement of performance of computational
5 unit in a multi-threaded environment. It is to be noted that the computational units
(sometimes also referred to as computation units) refer to various components and devices involved in processing and managing network traffic, data, and operations. They play a crucial role in enabling the functionalities and performance requirements of communication network (e.g., 5G networks). The computational units may include but not
10 limited to Baseband Processing Units (BBUs) that handle modulation/ demodulation,
encoding/decoding, error correction; Central Processing Units (CPUs) that handle session management, mobility management and authentication etc.; Graphics Processing Units (GPUs) that handle video transcoding, multimedia content delivery and augmented reality (AR)/ virtual reality (VR) applications etc.; and Network Processing Units (NPUs) that
15 handle high-speed packet routing, traffic shaping, Quality of Service (QoS) management
etc. It is further noted that a multi-threaded environment may include an environment in the communication network where the concurrent execution of multiple threads within various network elements such as base stations, core network nodes, and computing platforms takes place. The multi-threading allows parallel processing of tasks that may
20 include packet processing, signal modulation/demodulation, resource allocation, enhancing
network throughput, latency, and scalability. The existing systems proved to be inaccurate in multi-threaded environment due to inability to collect varied performance measurement data and are more generic thereby leading to a need to provide a solution for a more tailored performance evaluation for each computational unit. The existing systems had limitations
25 in collecting sample performance measurement data of the computational units for a long
duration during testing of the network components.
[0005] Thus, there exists an imperative need in the art for measuring network node
performance, generating insights for network node performance, and optimising network
30 performance based on computation time of computational units, which helps in optimizing
network node performance, which the present disclosure aims to address.
OBJECTS OF THE DISCLOSURE
3

[0006] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
5 [0007] It is an object of the present disclosure to provide a system and a method for
measuring performance of network node(s) present in a network via one or more computational units associated with the network for generating insights and for optimizing performance of the network node(s).
10 [0008] It is another object of the present disclosure to provide a solution that allocates
unique ID for each computational unit in a network and measures a computation time associated with said each computational unit.
[0009] It is yet another object of the disclosure to determine and measure performance of
15 each computational unit in a network and perform performance optimization.
[0010] It is yet another object of the present disclosure to provide a solution to determine at a pre-defined interval of time an average computation time of the computational units.
20 SUMMARY OF THE DISCLOSURE
[0011] 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.
25
[0012] An aspect of the present disclosure relates to a method for optimising network performance based on computation time of one or more computational units. The method comprises registering a set of metric ID associated with the one or more computational units by a registration unit. Each metric ID from the set of metric ID is associated with one
30 of the one or more computational units. The method further comprises determining the
computation time associated with each computational unit from the one or more computational units by a determination unit. The method further comprises transmitting the computation time associated with said each computational unit from the one or more
4

computational units based on the metric ID associated with said each computational unit,
by a transceiver unit to a performance measurement unit. The method further comprises
determining a target computation time associated with said each computational unit from
the one or more computational units based on the computation time associated with said
5 each computational unit, by the determination unit at the performance measurement unit.
[0013] Further according to an aspect of the present disclosure, the computation time comprises at least one of a start time associated with said each computational unit from the one or more computational units and an end time associated with said each computational
10 unit from the one or more computational units. Furthermore, in the method the target
computation time is determined by the determination unit at least at a pre-defined interval of time, and wherein the target computation time associated with said each computational unit is at least one of an average computation time associated with said each computational unit, a minimum computation time associated with said each computational unit, and a
15 maximum computation time associated said each computational unit.
[0014] Further according to an aspect of the present disclosure, the average computation time associated with said each computational unit is determined by the determination unit based on the computation time associated with said each computational unit, the minimum
20 computation time associated with said each computational unit is determined by the
determination unit based on the computation time associated with said each computational unit, and the maximum computation time associated with each computational unit is determined by the determination unit based on the computation time associated with said each computational unit. Also, as per the method the pre-defined interval of time is one of
25 a user defined time interval and a preconfigured time interval.
[0015] Another aspect of the present disclosure relates to a system for optimising network
performance based on computation time of one or more computational units. The system
comprises a registration unit which is configured to register a set of metric ID associated
30 with the one or more computational units. Each metric ID from the set of metric ID is
associated with one of the one or more computational units. The system further comprises a determination unit which is connected to the registration unit and is configured to determine the computation time associated with each computational unit from the one or
5

more computational units. The system further comprises a transceiver unit which is
connected to the determination unit and is configured to transmit the computation time
associated with said each computational unit from the one or more computational units
based on the metric ID associated with said each computational unit, to a performance
5 measurement unit. Further, the determination unit is configured to determine at the
performance measurement unit, a target computation time associated with said each computational unit from the one or more computational units based on the computation time associated with said each computational unit.
10 [0016] Another aspect of the present disclosure relates to a non-transitory computer
readable storage medium storing instruction for optimising network performance based on computation time of one or more computational units. The storage medium comprises executable code which, when executed by one or more units of a system, causes a registration unit of said system to register a set of metric ID associated with the one or more
15 computational units. Each metric ID from the set of metric ID is associated with one of the
one or more computational units. Further, the executable code when executed, causes a determination unit of the system to determine the computation time associated with each computational unit from the one or more computational units. Further, the executable code when executed causes a transceiver unit of the system to transmit to a performance
20 measurement unit, the computation time associated with said each computational unit from
the one or more computational units based on the metric ID associated with said each computational unit. Further, the executable code, when executed, causes the determination unit to determine a target computation time associated with said each computational unit from the one or more computational units based on the computation time associated with
25 said each computational unit.
[0017] Yet another aspect of the present disclosure relates to a user equipment (UE) for
optimising network performance based on computation time of one or more computational
units. The user equipment (UE) comprises a system, wherein the system comprises a
30 registration unit configured to register a set of metric ID associated with the one or more
computational units. Each metric ID from the set of metric ID is associated with one of the one or more computational units. The system further comprises a determination unit which is connected to the registration unit and is configured to determine the computation time
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associated with each computational unit from the one or more computational units. The
system further comprises a transceiver unit which is connected to the determination unit
and is configured to transmit the computation time associated with said each computational
unit from the one or more computational units based on the metric ID associated with said
5 each computational unit, to a performance measurement unit. Further, the determination
unit is configured to determine at the performance measurement unit, a target computation time associated with said each computational unit from the one or more computational units based on the computation time associated with said each computational unit.
10 BRIEF DESCRIPTION OF DRAWINGS
[0018] 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.
15 Components in the drawings are not necessarily to scale, emphasis instead being placed
upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry
20 commonly used to implement such components.
[0019] FIG. 1A illustrates an exemplary block diagram representation of a 5th generation core (5GC) network architecture.
25 [0020] FIG.1B illustrates an exemplary block diagram of a system [100] for optimising
network performance based on computation time of one or more computational units, which helps in optimizing network node performance, in accordance with exemplary embodiments of the present disclosure.
30 [0021] FIG.2 illustrates an exemplary method flow diagram indicating the process [200]
for optimising network performance based on computation time of one or more computational units, which helps in optimizing network performance, in accordance with exemplary embodiments of the present disclosure.
7

[0022] FIG. 3 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. 5
[0023] The foregoing shall be more apparent from the following more detailed description of the disclosure.
DETAILED DESCRIPTION
10
[0024] 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 details. Several features described hereafter can
15 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. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present disclosure are described below, as illustrated in various drawings in which like
20 reference numerals refer to the same parts throughout the different drawings.
[0025] 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
25 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.
[0026] It should be noted that the terms "mobile device", "user equipment", "user device",
30 “communication device”, “device” and similar terms are used interchangeably for the
purpose of describing the disclosure. These terms are not intended to limit the scope of the disclosure or imply any specific functionality or limitations on the described embodiments. The use of these terms is solely for convenience and clarity of description. The disclosure
8

is not limited to any particular type of device or equipment, and it should be understood that other equivalent terms or variations thereof may be used interchangeably without departing from the scope of the disclosure as defined herein.
5 [0027] 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, networks, processes, and other components may be shown as
components in block diagram form in order not to obscure the embodiments in unnecessary
10 detail. In other instances, well-known circuits, processes, algorithms, structures, and
techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0028] Also, it is noted that individual embodiments may be described as a process which
15 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 can 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.
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[0029] 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 necessarily to be construed as
25 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 similar to the term “comprising” as an open transition word—
30 without precluding any additional or other elements.
[0030] As used herein, an “electronic device”, or “portable electronic device”, or “user device”, “user equipment” or “communication device” or “user equipment” or
9

“device” refers to any electrical, electronic, electromechanical and computing device. The
user device is capable of receiving and/or transmitting one or parameters, performing
function/s, communicating with other user devices and transmitting data to the other user
devices. The user equipment may have a processor, a display, a memory, a battery and an
5 input-means such as a hard keypad and/or a soft keypad. The user equipment may be
capable of operating on any radio access technology including but not limited to IP-enabled
communication, Zig Bee, Bluetooth, Bluetooth Low Energy, Near Field Communication,
Z-Wave, Wi-Fi, Wi-Fi direct, etc. For instance, the user equipment may include, but not
limited to, a mobile phone, smartphone, virtual reality (VR) devices, augmented reality
10 (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet
computer, mainframe computer, or any other device as may be obvious to a person skilled in the art for implementation of the features of the present disclosure.
[0031] Further, the user equipment may also comprise a “system” or “system [100]”
15 including a “processor” or “processing unit” includes processing unit, wherein processor
refers to any logic circuitry for processing instructions. The processor may be a general-
purpose processor, a special purpose processor, a conventional processor, a digital signal
processor, a plurality of microprocessors, one or more microprocessors in association with
a digital signal processor (DSP) core, a controller, a microcontroller, Application Specific
20 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 the working of the system according to the present disclosure. More specifically, the processor is a hardware processor.
25 [0032] As portable electronic devices and wireless technologies continue to improve and
grow in popularity, the advancing wireless technologies for data transfer are also expected to evolve and replace the older generations of technologies. In the field of wireless data communications, the dynamic advancement of various generations of cellular technology are also seen. The development, in this respect, has been incremental in the order of second
30 generation (2G), third generation (3G), fourth generation (4G), and now fifth generation
(5G), and more such generations are expected to continue in the forthcoming time.
10

[0033] Radio Access Technology (RAT) refers to the technology used by mobile devices/
user equipment (UE) to connect to a cellular network. It refers to the specific protocol and
standards that govern the way devices communicate with base stations, which are
responsible for providing the wireless connection. Further, each RAT has its own set of
5 protocols and standards for communication, which define the frequency bands, modulation
techniques, and other parameters used for transmitting and receiving data. Examples of
RATs include GSM (Global System for Mobile Communications), CDMA (Code Division
Multiple Access), UMTS (Universal Mobile Telecommunications System), LTE (Long-
Term Evolution), and 5G. The choice of RAT depends on a variety of factors, including
10 the network infrastructure, the available spectrum, and the mobile device's/device's
capabilities. Mobile devices often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.
15 [0034] All modules, units, components used herein or unit(s) that are a part of system
[100] may be software modules configured via hardware modules/processors, or hardware modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a DSP core, a
20 controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable
Gate Array circuits, any other type of integrated circuits, etc.
[0035] As discussed in the background section, the current known solutions for measuring
performance of a network via one or more computational units associated with the network
25 have several shortcomings such as issues with optimisation and in measuring performance.
The existing systems proved to be inaccurate and are more generic thereby leading to a need to provide for a more tailored performance evaluation for each unit.
[0036] The present disclosure aims to overcome the above-mentioned and other existing
30 problems in this field of technology by disclosing a novel solution for measuring and
optimizing network performance. It introduces a system and method that utilizes one or more computational units associated with a network to assess and enhance its performance. The disclosure requires the registration of unique metric IDs for different types of
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computational measurements, ensuring accurate and tailored performance evaluation for
each unit configured in a network. Each of the one or more computational units is assigned
a distinctive ID for each thread, enabling precise identification and tracking. It is important
to note that in the context of optimizing network performance, “thread” may include a
5 sequence of instructions or tasks that can be executed independently or collectively by one
or more computational units. Threads ensures maximizing the utilization of network
resources and thus improving efficiency by enabling parallelism, concurrency, task
offloading, resource sharing, etc. By recording the start and end times of computations and
sharing this data with a dedicated performance measurement unit, the average, minimum,
10 and maximum values of computation time is calculated periodically. These statistics
provide valuable insights into the network's performance and enable further optimization. This innovative solution brings about significant advancements in the field, offering an efficient and effective means of measuring and improving network performance.
15 [0037] Hereinafter, exemplary embodiments of the present disclosure will be described
with reference to the accompanying drawings.
[0038] FIG. 1A illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture. As shown in FIG. 1, the 5GC network architecture [101]
20 includes a user equipment (UE) [101a], a radio access network (RAN) [101b], a 5G Core
Network and a Data Network [101p]. The 5G Core Network includes an access and mobility management function (AMF) [101c], a Session Management Function (SMF) [101d], a Service Communication Proxy (SCP) [101e], an Authentication Server Function (AUSF) [101f], a Network Slice Specific Authentication and Authorization Function
25 (NSSAAF) [101g], a Network Slice Selection Function (NSSF) [101h], a Network
Exposure Function (NEF) [101i], a Network Repository Function (NRF) [101j], a Policy Control Function (PCF) [101k], a Unified Data Management (UDM) [101l], an application function (AF) [101m], a User Plane Function (UPF) [101n].
30 [0039] The User Equipment (UE) [101a] interfaces with the network via the Radio Access
Network (RAN) [101b]. The RAN [101b] in the 5G architecture is also called as New Radio or nG-RAN, and these terms may be interchangeably used herein. Radio Access Network (RAN) [101b] is the part of a mobile telecommunications system that connects user
12

equipment (UE) [101a] to the core network (CN) and provides access to different types of networks (e.g., 5G, LTE). It consists of radio base stations and the radio access technologies that enable wireless communication.
5 [0040] The Access and Mobility Management Function (AMF) [101c] manages
connectivity and mobility. When a UE [101a] is active, i.e. it is interacting with the 5G
network, e.g., by using data/ call functionalities, the AMF [101c] knows and maintains the
location of the UE [101a] within the network. The AMF [101c] is configured to maintain
the tracking area or registration area of the UE [101a], in case the UE is inactive. The AMF
10 [101c] is configured to communicate with other network functions/ elements such as the
Session Management Function (SMF) [101d], etc. to ensure that the UE [101a] is allowed and is able to avail the services by the network.
[0041] Particularly, the Access and Mobility Management Function (AMF) [101c] is a 5G
15 core network function responsible for managing access and mobility aspects, such as UE
registration, connection, and reachability etc. It also handles mobility management procedures like handovers and paging.
[0042] The Session Management Function (SMF) [101d] is a 5G core network function
20 responsible for managing session-related aspects, such as establishing, modifying, and
releasing sessions. It coordinates with the User Plane Function (UPF) for data forwarding and handles IP address allocation and QoS enforcement.
[0043] The Service Communication Proxy (SCP) [101e] is a network function in the 5G
25 core that facilitates communication between other network functions by providing a secure
and efficient messaging service. It acts as a mediator for service-based interfaces.
[0044] The Authentication Server Function (AUSF) [101f] is a network function in the
5G core responsible for authenticating UEs during registration and providing security
30 services. It generates and verifies authentication vectors and tokens.
[0045] The Network Slice Specific Authentication and Authorization Function (NSSAAF) [101g] is a network function that provides authentication and authorization
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services specific to network slices. It ensures that UEs can access only the slices for which they are authorized.
[0046] The Network Slice Selection Function (NSSF) [101h] is a network function
5 responsible for selecting the appropriate network slice for a UE based on factors such as
subscription, requested services, and network policies.
[0047] The Network Exposure Function (NEF) [101i] is a network function that exposes
capabilities and services of the 5G network to external applications, enabling integration
10 with third-party services and applications.
[0048] The Network Repository Function (NRF) [101j] is a network function that acts as a central repository for information about available network functions and services. It facilitates the discovery and dynamic registration of network functions. 15
[0049] The Policy Control Function (PCF) [101k] is a network function responsible for policy control decisions, such as Quality of Service (QoS), charging, and access control, based on subscriber information and network policies.
20 [0050] The Unified Data Management (UDM) [101l] is a network function that centralizes
the management of subscriber data, including authentication, authorization, and subscription information.
[0051] The Application Function (AF) [101m] is a network function that represents
25 external applications interfacing with the 5G core network to access network capabilities
and services.
[0052] The User Plane Function (UPF) [101n] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement. 30
[0053] The Data Network (DN) [101p] represents external networks or services that users connect to through the mobile network, such as the internet or enterprise networks.
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[0054] Referring to Figure 1B, an exemplary block diagram of a system [100], for optimising network performance based on computation time of one or more computational units, is shown, in accordance with the exemplary embodiments of the present disclosure.
5 [0055] It is to be noted that in a communication network the one or more computational
units may work independently or together with other computational unit(s) to process,
manage, and optimize network traffic, data transmission, and service delivery thus enabling
the high-speed, low-latency, and high-capacity capabilities of an ideal communication
network. The one or more computational units may include but not limited to at least one
10 of an Access and Mobility Management Function (AMF) associated with the 5G
communication network and a Session Management Function (SMF) associated with the 5G communication network.
[0056] The system [100] comprises at least one registration unit [102], at least one
15 determination unit [104] connected at least to the at least one registration unit [102], at least
one transceiver unit [106] connected at least to the determination unit [104], and at least
one performance measurement unit [108] connected at least to the determination unit [104].
Also, all of the components/ units of the system [100] are assumed to be connected to each
other unless otherwise indicated below. Also, in Fig. 1 only a few units are shown, however,
20 the system [100] may comprise multiple such units or the system [100] may comprise any
such numbers of said units, as required to implement the features of the present disclosure.
Also, for ease of reference, FIG. 1 depicts units/components of the system [100] by way of
representation of blocks and FIG. 1 do not represent the internal circuitry or connections
of each component/unit of the system [100]. It will be appreciated by those skilled in the
25 art that disclosure of such drawings/block diagrams includes disclosure of electrical
components and connections between said electronic components, and electronic
components or circuitry commonly used to implement such components.
[0057] Further, in an implementation, the system [100] may be present in a user device to
30 implement the features of the present disclosure, wherein the user device is facilitated by
the communication network to implement the features of the present disclosure. The system [100] may be a part of the user device or may be independent of but in communication with the user device (may also referred herein as a UE). In another implementation, the system
15

[100] may reside in a server or a network entity (e.g., 5G network). In yet another implementation, the system [100] may reside partly in the server/ network entity and partly in the user device.
5 [0058] Additionally, the registration unit [102], the determination unit [104], and the
performance measurement unit [108] are processors. The processor may be a general-
purpose processor, a special purpose processor, a conventional processor, a digital signal
processor, a plurality of microprocessors, one or more microprocessors in association with
a DSP (digital signal processor) core, a controller, a microcontroller, Application Specific
10 Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated
circuits, etc.
[0059] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various components/units can be implemented
15 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
20 the intended functionality described herein, are considered to be encompassed within the
scope of the present disclosure.
[0060] The system [100] is configured for optimising the network performance based on
the computation time of the one or more computational units, with the help of the
25 interconnection between the components/units of the system [100].
[0061] In order for optimising the network performance based on the computation time of
the one or more computational units, the registration unit [102] of the system [100] is
configured to register a set of metric ID associated with the one or more computational
30 units, wherein each metric ID from the set of metric ID is associated with one of the one or
more computational units. It is to be noted that the "metric ID" of the computational unit may include a unique identifier assigned to a specific performance metric associated with that computational unit. The metric ID helps to identify and track the performance of the
16

computational units and its impact on the overall network performance. Further, the performance metric are quantitative measures that assess the efficiency and QoS of the network. The performance metric may include throughput, latency, packet loss, memory usage, etc. 5
[0062] Further, the determination unit [104] of the system [100] is configured to determine the computation time associated with each computational unit from the one or more computational units. The computation time comprises at least one of a start time associated with said each computational unit from the one or more computational units and an end
10 time associated with said each computational unit from the one or more computational
units. It is important to note that the start time of the computational unit indicates when a computational unit begins executing a specific task or set of tasks like packet forwarding, routing etc. Thus, the start time relates to an initiation of the start of operation of the computational unit of the network. Further, the end time of the computational units
15 indicates the completion of said specific task or said set of tasks by the computational unit.
The end time relates to the termination of the processing cycle and may also indicate the time when the computational unit becomes idle or moves on to the next task. The start time and the end time of the computational units helps in identifying any delays for improvement and optimize network performance.
20
[0063] Further, the transceiver unit [106] of the system [100] is configured to transmit, to the performance measurement unit [108], the computation time associated with said each computational unit from the one or more computational units based on the metric ID associated with said each computational unit. The metric ID here may include one or more
25 computational IDs which is assigned to the one or more computational units. Further, there
could be multiple metric ID for a single computational unit when it is executed by multiple threads. This metric ID may depend on various factors such as but not limited to core the thread is currently running on, status of said core, execution time of the thread, capacity of the cache, interruption due to other tasks etc.
30
[0064] Further, the determination unit [104] of the system [100] is configured to determine, at the performance measurement unit [108], a target computation time associated with said each computational unit from the one or more computational units
17

based on the computation time associated with said each computational unit. The target
computation time is determined by the determination unit [104] at least at a pre-defined
interval of time, and wherein the target computation time associated with said each
computational unit is at least one of an average computation time associated with said each
5 computational unit, a minimum computation time associated with said each computational
unit, and a maximum computation time associated said each computational unit. The average computation time associated with each computational unit is determined by the determination unit [104] based on the computation time associated with said each computational unit, a minimum computation time associated with each computational unit
10 is determined by the determination unit [104] based on the computation time associated
with said each computational unit, and a maximum computation time associated with each computational unit is determined by the determination unit [104] based on the computation time associated with said each computational unit. The pre-defined interval of time is one of a user defined time interval and a preconfigured time interval. It is to be noted that the
15 user defined time interval is based on iterations whereas the preconfigured time interval is
fixed. To simply put, the user defined time interval may vary as per iterations.
[0065] In an exemplary embodiment of the present disclosure, an optimizer unit (not shown) of the system [100] is configured to optimize the network performance based on
20 the target computation time associated with each computational unit from the one or more
computational units. Optimize here may refer to “improve” the network performance or “enhance” the network performance, and the like. For instance, there are multiple network entities such as but not limited to radio and core network functions which are simultaneously running in the network. The parameters such as but not limited to an
25 operational cost for processing inside the network entities helps in understanding the
network performance which helps in the optimization. It is important to note that this is not configuration-based network optimization, but this optimization is carried on an evaluation tool for network performance. The tool may include a network management tool. In an instance, if a registration request is sent from the device which is trying to connect to the
30 network, this registration request requires some processing which involves a finite time
[let’s assume 5s (seconds)] in order to reduce the cost, thus, the reduced cost is the optimized cost.
18

[0066] The present disclosure also encompasses a non-transitory computer readable
storage medium storing instruction for optimising network performance based on
computation time of one or more computational units is disclosed. The storage medium
comprises executable code which, when executed by one or more units of a system, causes
5 a registration unit of said system to register a set of metric ID associated with the one or
more computational units. Each metric ID from the set of metric ID is associated with one of the one or more computational units. Further, the executable code when executed, causes a determination unit of the system to determine the computation time associated with each computational unit from the one or more computational units. Further, the executable code
10 when executed causes a transceiver unit of the system to transmit to a performance
measurement unit, the computation time associated with said each computational unit from the one or more computational units based on the metric ID associated with said each computational unit. Further, the executable code, when executed, causes the determination unit to determine a target computation time associated with said each computational unit
15 from the one or more computational units based on the computation time associated with
said each computational unit.
[0067] Referring to Figure 2, an exemplary method flow diagram [200] for optimising network performance based on computation time of one or more computational units, in
20 accordance with exemplary embodiments of the present disclosure is shown. In an
implementation the method [200] is performed by the system [100]. Further, in an implementation, the system [100] may be present in a server device or in a user equipment (UE) or partially in the server device and partially in the UE to implement the features of the present disclosure. Also, as shown in Figure 2, the method [200] starts at step [202].
25
[0068] At step [204], the method [200] as disclosed by the present disclosure comprises registering, by a registration unit [102], a set of metric ID associated with the one or more computational units, wherein each metric ID from the set of metric ID is associated with one of the one or more computational units. It is to be noted that the "metric ID" of the
30 computational unit may include a unique identifier assigned to a specific performance
metric associated with that computational unit. The metric ID helps to identify and track the performance of the computational units and its impact on the overall network performance. Further, the performance metric are quantitative measures that assess the
19

efficiency and QoS of the network. The performance metric may include throughput, latency, packet loss, memory usage etc.
[0069] At step [206], the method [200] as disclosed by the present disclosure comprises
5 determining, by a determination unit [104], a computation time associated with each
computational unit from the one or more computational units. The computation time comprises at least one of a start time associated with said each computational unit from the one or more computational units and an end time associated with each computational unit from the one or more computational unit.
10
[0070] At step [208], the method [200] as disclosed by the present disclosure comprises transmitting, by a transceiver unit [106] to a performance measurement unit [108], the computation time associated with said each computational unit from the one or more computational units based on the metric ID associated with said each computational unit.
15 The metric ID here may include one or more computational IDs which is assigned to the
one or more computational units. Further, there could be multiple metric ID for a single computational unit when it is executed by multiple threads. This metric ID may depend on various factors such as but not limited to core the thread is currently running on, status of said core, execution time of the thread, capacity of the cache, interruption due to other
20 tasks, etc.
[0071] At step [210], the method [200] as disclosed by the present disclosure comprises determining, by the determination unit [104] at the performance measurement unit [108], a target computational time associated with said each computational unit from the one or
25 more computational units based on the computation time associated with said each
computational unit. The target computation time is determined by the determination unit [104] at least at a pre-defined interval of time, and wherein the target computation time associated with said each computational unit is at least one of an average computation time associated with said each computational unit, a minimum computation time associated with
30 said each computational unit, and a maximum computation time associated said each
computational unit. The average computation time associated with said each computational unit is determined by the determination unit [104] based on the computation time associated with said each computational unit, the minimum computation time associated with each
20

computational unit is determined by the determination unit [104] based on the computation
time associated with said each computational unit, and the maximum computation time
with each computational unit is determined by the determination unit [104] based on the
computation time associated with said each computational unit. The pre-defined interval of
5 time is one of a user defined time interval and a preconfigured time interval.
[0072] The solution provided by the present disclosure thus optimizes the network performance based on the determined target computational time associated with said each computational unit. 10
[0073] Thereafter, the method terminates at step [212].
[0074] Fig. 3 illustrates an exemplary block diagram of a computing device [300] upon which the features of the present disclosure may be implemented in accordance with
15 exemplary implementation of the present disclosure. In an implementation, the computing
device [300] may also implement a method for optimising network performance based on computation time of one or more computational units in a 5G radio network utilising the system. In another implementation, the computing device [300] itself implements the method for optimising network performance based on the computation time of the one or
20 more computational units in the 5G radio network using one or more units configured
within the computing device [300], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
[0075] The computing device [300] may include a bus [302] or other communication
25 mechanism for communicating information, and a hardware processor [304] coupled with
bus [302] for processing information. The hardware processor [304] may be, for example,
a general-purpose microprocessor. The computing device [300] may also include a main
memory [306], such as a random-access memory (RAM), or other dynamic storage device,
coupled to the bus [302] for storing information and instructions to be executed by the
30 processor [304]. The main memory [306] also may be used for storing temporary variables
or other intermediate information during execution of the instructions to be executed by the processor [304]. Such instructions, when stored in non-transitory storage media accessible to the processor [304], render the computing device [300] into a special-purpose machine
21

that is customized to perform the operations specified in the instructions. The computing device [300] further includes a read only memory (ROM) [308] or other static storage device coupled to the bus [302] for storing static information and instructions for the processor [304]. 5
[0076] A storage device [310], such as a magnetic disk, optical disk, or solid-state drive is provided and coupled to the bus [302] for storing information and instructions. The computing device [300] may be coupled via the bus [302] to a display [312], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED)
10 display, Organic LED (OLED) display, etc. for displaying information to a computer user.
An input device [314], including alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [302] for communicating information and command selections to the processor [304]. Another type of user input device may be a cursor controller [316], such as a mouse, a trackball, or cursor direction keys, for communicating
15 direction information and command selections to the processor [304], and for controlling
cursor movement on the display [312]. This input device typically has two degrees 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.
20 [0077] The computing device [300] may implement the techniques described herein using
customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computing device [300] causes or programs the computing device [300] to be a special-purpose machine. According to one implementation, the techniques herein are performed by the computing device [300] in response to the processor
25 [304] executing one or more sequences of one or more instructions contained in the main
memory [306]. Such instructions may be read into the main memory [306] from another storage medium, such as the storage device [310]. Execution of the sequences of instructions contained in the main memory [306] causes the processor [304] to perform the process steps described herein. In alternative implementations of the present disclosure,
30 hard-wired circuitry may be used in place of or in combination with software instructions.
[0078] The computing device [300] also may include a communication interface [328] coupled to the bus [302]. The communication interface [328] provides a two-way data
22

communication coupling to a network link [320] that is connected to a local network [322].
For example, the communication interface [328] may be an 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,
5 the communication interface [328] 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 implementation, the communication interface [328] sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
10
[0079] The computing device [300] can send messages and receive data, including program code, through the network(s), the network link [320] and the communication interface [328]. In the Internet example, a server [330] might transmit a requested code for an application program through the Internet [332], the ISP [326], the local network [322],
15 the host [324] and the communication interface [328]. The received code may be executed
by the processor [304] as it is received, and/or stored in the storage device [310], or other non-volatile storage for later execution.
[0080] Another aspect of the present disclosure relates to a user equipment (UE) for
20 optimising network performance based on computation time of one or more computational
units. The user equipment (UE) comprises a system [100], wherein the system comprises a
registration unit [102] configured to register a set of metric ID associated with the one or
more computational units. It is important to note that each metric ID from the set of metric
ID is associated with one of the one or more computational units. The system [100] further
25 comprises a determination unit [104] which is connected to the registration unit [102] and
is configured to determine the computation time associated with each computational unit
from the one or more computational units. The system [100] further comprises a transceiver
unit [106] which is connected to the determination unit [104] and is configured to transmit
the computation time associated with said each computational unit from the one or more
30 computational units based on the metric ID associated with said each computational unit to
a performance measurement unit [108]. Further, the determination unit [104] is configured
to determine at the performance measurement unit [108], a target computation time
23

associated with said each computational unit from the one or more computational units based on the computation time associated with said each computational unit.
[0081] As is evident from the above, the present disclosure provides a technically
5 advanced solution for performance measurement and optimization in network modules. A
novel solution is provided, wherein any network module seeking to utilize this performance measurement for a specific computational unit must register a distinct metric ID for each type of computational measurement. This innovative approach ensures accurate and tailored performance assessment for different computational units. Each computational unit
10 is allocated a unique ID for each thread, facilitating precise identification and tracking. To
measure performance, every computational unit is required to record the start and end time of its computation and share this information with the dedicated performance measurement unit [108]. The performance measurement unit [108] then periodically calculates and prints statistics such as average, minimum, and maximum values for each computational unit.
15 This enables a comprehensive performance evaluation and facilitates further optimization
efforts. Overall, this novel solution presents a significant technical effect in the communication technologies, providing an efficient and effective means of measuring performance and enabling targeted performance enhancements.
20 [0082] While considerable emphasis has been placed herein on the disclosed
embodiments, it will be appreciated that many embodiments can be made and that many changes can be made to the embodiments without departing from the principles of the present disclosure. These and other changes in the embodiments of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing
25 descriptive matter to be implemented is illustrative and non-limiting.
24

We Claim:
1. A method [200] for optimising network performance based on a computation time of one or more computational units, the method [200] comprising:
- registering, by a registration unit [102], a set of metric ID associated with the one
5 or more computational units, wherein each metric ID from the set of metric ID is
associated with one of the one or more computational units;
- determining, by a determination unit [104], the computation time associated with
each computational unit from the one or more computational units;
- transmitting, by a transceiver unit [106] to a performance measurement unit [108],
10 the computation time associated with said each computational unit from the one or
more computational units based on the metric ID associated with said each computational unit; and
- determining, by the determination unit [104] at the performance measurement unit
[108], a target computation time associated with said each computational unit
based on the computation time associated with said each computational unit.
The method [200] as claimed in claim 1, wherein the computation time comprises at least one of a start time associated with said each computational unit from the one or more computational units and an end time associated with said each computational unit from the one or more computational units.
The method [200] as claimed in claim 1, wherein the target computation time is determined by the determination unit [104] at least at a pre-defined interval of time, and wherein the target computation time associated with said each computational unit is at least one of an average computation time associated with said each computational unit, a minimum computation time associated with said each computational unit, and a maximum computation time associated said each computational unit.
The method [200] as claimed in claim 3, wherein the average computation time associated with said each computational unit is determined by the determination unit [104] based on the computation time associated with said each computational unit,

the minimum computation time associated with said each computational unit is
determined by the determination unit [104] based on the computation time associated
with said each computational unit, and the maximum computation time associated
with said each computational unit is determined by the determination unit [104] based
5 on the computation time associated with said each computational unit.
5. The method [200] as claimed in claim 3, wherein the pre-defined interval of time is one of a user defined time interval and a preconfigured time interval.
10 6. A system [100] for optimising network performance based on a computation time of
one or more computational units, the system [100] comprises:
- a registration unit [102], the registration unit [102] is configured to register a set of
metric ID associated with the one or more computational units, wherein each
metric ID from the set of metric ID is associated with one of the one or more
15 computational units;
- a determination unit [104] connected to the registration unit [102], the
determination unit [104] is configured to determine the computation time
associated with each computational unit from the one or more computational units;
- a transceiver unit [106] connected to the determination unit [104], the transceiver
20 unit [106] is configured to transmit to a performance measurement unit [108], the
computation time associated with said each computational unit from the one or more computational units based on the metric ID associated with said each computational unit, wherein:
the determination unit [104] is configured to determine, at the performance
25 measurement unit [108], a target computation time associated with said
each computational unit based on the computation time associated with
said each computational unit.
7. The system [100] as claimed in claim 6, wherein the computation time comprises at
30 least one of a start time associated with said each computational unit from the one or
more computational units and an end time associated with said each computational unit from the one or more computational units.

8. The system [100] as claimed in claim 6, wherein the target computation time is
determined by the determination unit [104] at least at a pre-defined interval of time,
and wherein the target computation time associated with said each computational unit
is at least one of an average computation time associated with said each
5 computational unit, a minimum computation time associated with said each
computational unit, and a maximum computation time associated said each computational unit.
9. The system [100] as claimed in claim 8, wherein the average computation time
10 associated with said each computational unit is determined by the determination unit
[104] based on the computation time associated with said each computational unit,
the minimum computation time associated with said each computational unit is
determined by the determination unit [104] based on the computation time associated
with said each computational unit, and the maximum computation time associated
15 with said each computational unit is determined by the determination unit [104] based
on the computation time associated with said each computational unit.
10. The system [100] as claimed in claim 8, wherein the pre-defined interval of time is
20 one of a user defined time interval and a preconfigured time interval.
11. A user equipment (UE) for optimising network performance based on computation
time of one or more computational units, the user equipment (UE) comprising:
- a system [100], wherein the system [100] comprises:
25 o a registration unit [102], the registration unit [102] is configured to register
a set of metric ID associated with the one or more computational units,
wherein each metric ID from the set of metric ID is associated with one of
the one or more computational units;
o a determination unit [104] connected to the registration unit [102], the
30 determination unit [104] is configured to determine the computation time
associated with each computational unit from the one or more computational units; and

o a transceiver unit [106] connected to the determination unit [104], the
transceiver unit [106] is configured to transmit, to a performance
measurement unit [108], the computation time associated with said each
computational unit from the one or more computational units based on the
5 metric ID associated with said each computational unit, wherein:
the determination unit [104] is configured to determine, at the performance measurement unit [108], a target computation time associated with said each computational unit based on the computation time associated with said each computational unit. 10