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

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

[0004] With each generation, wireless communication technologies have become more advanced, sophisticated, and capable of delivering more services to its users. Further, reducing call drops and latency is of paramount importance in the telecommunications industry. Call drops can be frustrating for users, and they can also result in lost revenue for service providers. Latency, on the other hand, refers to the time it takes for data to travel from one device to another and can cause delays and disruptions in communication network. The introduction of the 5G technology promises to address these issues by delivering ultra-low latency and high-speed data transmission. With the 5G, call drops are going to be minimized, and users are going to experience seamless, uninterrupted communication. Additionally, the 5G technology may enable the development of new applications and services that require high-speed, low-latency communication, such as remote surgeries, autonomous vehicles, and virtual reality. The reduction of call drops, and latency is crucial in ensuring that users have access to reliable and efficient communication services, and the 5G technology is a significant step towards achieving this goal.
[0005] In a 5G communication system, a number of functional modules are provided, for example an Access and Mobility Management Function (AMF), a Network Slice Selection Function (NSSF), and/or a Network Repository Function (NRF), a Network Data Analytics Function (NWDAF), etc. The functional modules interact with each other to implement multiple operations of the 5G communication system.
[0006] Particularly, the NWDAF is one of the key components of 5G communication system. The NWDAF is responsible to receive a network function data from different Network functions (NFs) for performing a data analytics. Thereafter, based on the data analytics, a data analytics report is generated by the NWDAF. The data analytics report may include parameters, such as a total traffic data on NFs, a real-time traffic on the NFs, and utilization of the NFs.

[0007] Also, a user may study the data analytics report, and take one or more actions on the NFs if required. For example, the NWDAF receives slice data from the NSSF, and performs the data analytics on the received slice data. Further, based on the data analytics, a data slice analytics report is generated by the NWDAF.
[0008] Additionally, network slicing refers to a concept in the 5G technology which allow an operator to divide a single physical network into one or more virtual networks or network slices. In the field of telecommunication, one or more standards are defined for slice creation, slice management and resource allocation for ensure optimal performance and scalability.
[0009] Conventionally, in communication network system, a slice load analytics provides an information that how much network slice is utilised for providing the service in the communication network. The network slice may be implemented for providing various service applications such as an Enhanced Mobile Broadband (eMBB), a Massive Machine Type Communications (mMTC) and an Ultra-Reliable Low Latency Communication (uRRLC), and the like. The eMBB provides high speed and high-capacity mobile internet access for one or more application such video streaming. The mMTc provides a connectivity support to a large number of Internet of Things (IoT) devices. The uRRLC provide extremely high levels of reliability and low latency, for supporting one or more critical applications such as automation.
[0010] For example, if any network function, such as Network Slice Selection Function (NSSF) needs to know how much of a network slice has been utilised for providing service as per configured policy and status of the network slice such as underutilised or overutilized, the NSSF may communicate with Network Data Analytics Function (NWDAF). However, the existing solution for slice management is highly complex to implement, due to an intricate

process of configuring and managing multiple slices according to different service requirements such as eMBB, mMTC, uRLLC. Moreover, the configuration of the policy across diverse slices requires extensive planning and coordination among multiple network components, which makes the configuration process complex.
[0011] Additionally, the currently available solutions fail to efficiently allocate one or more resources among the network slices according to one or more real time demands and one or more dynamic network traffic patterns. This inefficiency negatively impacts an overall network efficiency and user experience.
[0012] Furthermore, the currently available solutions fail to provide one or more corrective actions in response to one or more dynamic changes in one or more network conditions such as overutilization of slices.
[0013] Additionally, the currently available solutions have limited adaptive capabilities, and require a continuous monitoring and fine-tuning of one or more slice configurations for maintaining an optimal performance. The requirement of continuous monitoring and fine-tuning of one or more slice configurations raises issues for one or more network operations to manage and optimize the network slices across an entire communication network infrastructure, especially in large-scale deployments. Hence, the currently available solutions for slice management provides degraded service quality, and cause resource wastage, and a negative impact on a customer experience.
[0014] Thus, there exists an imperative need in the art to provide an enhanced solution for slice management in the communication network.
SUMMARY

[0015] 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.
[0016] An aspect of the present disclosure may relate to a method for slice management in a network. The method comprises receiving, by a Network Data Analytics Function (NWDAF) unit, a network function data from at least one of one or more consumer devices and one or more network functions. The method further comprises computing, by the NWDAF unit, a load on one or more slices in the network based on the received network function data. The method further comprises identifying, by the NWDAF unit, a breach of one or more policies based on the computed load on the one or more slices. The method further comprises provisioning, by a Fulfilment management System (FMS) using a trained NWDAF model, one or more new slices based on the one or more policies and the computed load on the one or more slices.
[0017] In an exemplary aspect of the present disclosure, the identified breach of the one or more policies is reported to the one or more consumer devices.
[0018] In an exemplary aspect of the present disclosure, the present disclosure further comprising receiving, at the NWDAF unit, the one or more policies from the one or more consumer devices.
[0019] In an exemplary aspect of the present disclosure, the one or more policies are consumer-defined policies.
[0020] In an exemplary aspect of the present disclosure, the present disclosure further comprises training the trained NWDAF model for forecasting a network performance based on the computed load on the one or more slices, and displaying, by a NWDAF UI, a visualization of the forecasted network

performance, wherein the one or more policies are modified on the NWDAF UI.
[0021] In an exemplary aspect of the present disclosure, the present disclosure further comprises generating, by the NWDAF unit, a slice management report comprising a breach information, and one or more actions for managing slice consumption. The present disclosure further comprises transmitting, by the NWDAF unit, the slice management report to the one or more consumer devices.
[0022] Another aspect of the present disclosure may relate to a communication system for slice management in a network. The communication system comprises a Network Data Analytics Function (NWDAF) unit configured to receive a network function data from at least one of one or more consumer devices and one or more network functions. The NWDAF unit is further configured to compute a load on one or more slices in the network based on the received network function data. The NWDAF unit further configured to identify a breach of on one or more policies based on the computed load on the one or more slices. The system further comprises a Fulfilment System (FMS) connected to at least the NWDAF unit, the FMS configured to provision, using a trained NWDAF model, one or more new slices based on the one or more policies and the computed load on the one or more slices.
[0023] Another aspect of the present disclosure may relate to a user equipment (UE) for slice management in a network. The UE comprises a memory, a processor in connection with the memory. The processor is configured to transmit, to a communication system, a network function data associated with at least one of one or more consumer devices and one or more network functions. The processor is further configured to receive, from the communication system, a response comprising provisioning of one or more new

slices based on the network function data. The one or more new slices are provisioned based on computing, by the communication system, a load on one or more slices in the network based on the received network function data. The one or more new slices are provisioned is further based on identifying, by the communication system, a breach of one or more policies based on the computed load on the one or more slices.
[0024] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing one or more instructions for slice management in a network, the one or more instructions include executable code which, when executed by one or more units of a communication system, causes a Network Data Analytics Function (NWDAF) unit of the communication system, to receive a network function data from at least one of one or more consumer devices and one or more network functions. Further, the one or more instructions when further executed causes the NWDAF unit of the communication system to compute, a load on one or more slices in the network based on the received network function data. Further, the one or more instructions when further executed causes the NWDAF unit of the communication system to identify a breach of on one or more policies based on the computed load on the one or more slices. Further, the one or more instructions when further executed causes a Fulfilment System (FMS) of the communication system, to provision, using a trained NWDAF model, one or more new slices based on the one or more policies and the computed load on the one or more slices.
OBJECTS OF THE DISCLOSURE
[0025] Some of the objects of the present disclosure, which at least one embodiment disclosed herein satisfies are listed herein below.
[0026] It is an object of the present disclosure to provide a system and a method for slice management in a network.

[0027] It is another object of the present disclosure to provide a real time solution for historical slice load analytics visualization.
5 [0028] It is another object of the present disclosure to provide real time historical
slice load analytics visualization based on a specific load policy applied as per a need of a consumer.
[0029] It is another object of the present disclosure to provide a solution to optimize
10 a resource utilization and scaling effortlessly without any involvement of
manual intervention.
[0030] It is yet another object of the present disclosure to provide a solution that
forecast a resource usage and sends a trigger for provisioning of one or more
15 new slices if a particular slice is overloaded.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] The accompanying drawings, which are incorporated herein, and constitute
20 a part of this disclosure, illustrate exemplary embodiments of the disclosed
methods and systems in which like reference numerals refer to the same parts
throughout the different drawings. Components in the drawings are not
necessarily to scale, emphasis instead being placed upon clearly illustrating the
principles of the present disclosure. Also, the embodiments shown in the figures
25 are not to be construed as limiting the disclosure, but the possible variants of
the method and system according to the disclosure are illustrated herein to
highlight the advantages of the disclosure. It will be appreciated by those skilled
in the art that disclosure of such drawings includes disclosure of electrical
components or circuitry commonly used to implement such components.
30
9

[0032] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture.
[0033] FIG. 2 illustrates an exemplary block diagram of a computing device upon
5 which the features of the present disclosure may be implemented in accordance
with exemplary implementation of the present disclosure.
[0034] FIG. 3 illustrates an exemplary block diagram of a communication system
for slice management in a network, in accordance with exemplary
10 implementations of the present disclosure.
[0035] FIG. 4 illustrates flow diagram of a method for slice management in a
network, in accordance with exemplary implementations of the present
disclosure. 15
[0036] FIG. 5 illustrates flow diagram of an exemplary method of a method for
slice management in a network, in accordance with exemplary implementations
of the present disclosure.
20 [0037] The foregoing shall be more apparent from the following more detailed
description of the disclosure.
DETAILED DESCRIPTION
25 [0038] 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 may each be used independently of
30 one another or with any combination of other features. An individual feature
10

may not address any of the problems discussed above or might address only some of the problems discussed above.
[0039] The ensuing description provides exemplary embodiments only, and is not
5 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 function and arrangement of elements without departing from the
10 spirit and scope of the disclosure as set forth.
[0040] 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
15 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.
[0041] Also, it is noted that individual embodiments may be described as a process
20 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 concurrently. In addition, the order of the operations may be re¬
arranged. A process is terminated when its operations are completed but could
25 have additional steps not included in a figure.
[0042] 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
30 aspect or design described herein as “exemplary” and/or “demonstrative” is not
necessarily to be construed as preferred or advantageous over other aspects or
11

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
5 inclusive—in a manner similar to the term “comprising” as an open transition
word—without precluding any additional or other elements.
[0043] As used herein, a “processing unit” or “processor” or “operating processor” includes one or more processors, wherein processor refers to any logic circuitry
10 for processing instructions. A 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 Processing (DSP) core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array
15 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 or processing unit is a hardware processor.
20
[0044] As used herein, “a user equipment”, “a user device”, “a smart-user-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
25 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 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
30 disclosure. Also, the user device may contain at least one input means
12

configured to receive an input from unit(s) which are required to implement the features of the present disclosure.
[0045] As used herein, “storage unit” or “memory unit” refers to a machine or
5 computer-readable medium including any mechanism for storing information
in a form readable by a computer or similar machine. For example, a computer-
readable medium includes read-only memory (“ROM”), random access
memory (“RAM”), magnetic disk storage media, optical storage media, flash
memory devices or other types of machine-accessible storage media. The
10 storage unit stores at least the data that may be required by one or more units of
the system to perform their respective functions.
[0046] As used herein “interface” or “user interface refers to a shared boundary
across which two or more separate components of a system exchange
15 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 each other, which also includes the methods, functions, or procedures that may be called.
20 [0047] All modules, units, components used herein, unless explicitly excluded
herein, may be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a
25 microcontroller, Application Specific Integrated Circuits (ASIC), Field
Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.
[0048] As used herein the transceiver unit include at least one receiver and at least
30 one transmitter configured respectively for receiving and transmitting data,
13

signals, information or a combination thereof between units/components within the system and/or connected with the system.
[0049] As discussed in the background section, that current solution for slice
5 management is highly complex to implement, less efficient and fails to provide
any corrective or required actions. For example, during one or more overutilized slice conditions, there is no effective solution for assisting the service operation based on slice condition. Hence, the current known solutions have several shortcomings. The present disclosure aims to overcome the above-mentioned
10 and other existing problems in this field of technology by providing a novel
solution for slice management in a network. The novel solution of the present disclosure involves receiving a network function data from one of consumer devices and network functions associated with the network. Further, a load on one or more slices are computed in the network based on the received network
15 function data. Thereafter, a breach of policies is identified based on the
computed load on the one or more slices. Further, new slices are provisioned based on the policies and the computed load on the one or more slices. Hence, the present solution provides an efficient way for slice management in the network by provision the new slices which enhances a service quality, customer
20 experience and reduce resource wastage in the network.
[0050] FIG. 1 illustrates an exemplary block diagram representation of 5th generation core (5GC) network architecture, in accordance with exemplary implementation of the present disclosure. As shown in fig. 1, the 5GC network
25 architecture [100] includes a user equipment (UE) [102], a radio access network
(RAN) [104], an access and mobility management function (AMF) [106], a Session Management Function (SMF) [108], a Service Communication Proxy (SCP) [110], an Authentication Server Function (AUSF) [112], a Network Slice Specific Authentication and Authorization Function (NSSAAF) [114], a
30 Network Slice Selection Function (NSSF) [116], a Network Exposure Function
(NEF) [118], a Network Repository Function (NRF) [120], a Policy Control
14

Function (PCF) [122], a Unified Data Management (UDM) [124], an
application function (AF) [126], a User Plane Function (UPF) [128], and a data
network (DN) [130], wherein all the components are assumed to be connected
to each other in a manner as obvious to the person skilled in the art for
5 implementing features of the present disclosure.
[0051] Radio Access Network (RAN) [104] is the part of a mobile
telecommunications system that connects user equipment (UE) [102] to the core
network (CN) and provides access to different types of networks (e.g., 5G
10 network). It consists of radio base stations and the radio access technologies that
enable wireless communication.
[0052] Access and Mobility Management Function (AMF) [106] is a 5G core
network function responsible for managing access and mobility aspects, such
15 as UE registration, connection, and reachability. It also handles mobility
management procedures like handovers and paging.
[0053] Session Management Function (SMF) [108] is a 5G core network function
responsible for managing session-related aspects, such as establishing,
20 modifying, and releasing sessions. It coordinates with the User Plane Function
(UPF) for data forwarding and handles IP address allocation and QoS
enforcement.
[0054] Service Communication Proxy (SCP) [110] is a network function in the 5G
25 core network that facilitates communication between other network functions
by providing a secure and efficient messaging service. It acts as a mediator for service-based interfaces.
[0055] Authentication Server Function (AUSF) [112] is a network function in the
30 5G core responsible for authenticating UEs during registration and providing
security services. It generates and verifies authentication vectors and tokens.
15

[0056] Network Slice Specific Authentication and Authorization Function
(NSSAAF) [114] is a network function that provides authentication and
authorization services specific to network slices. It ensures that UEs can access
5 only the one or more slices for which they are authorized.
[0057] Network Slice Selection Function (NSSF) [116] is a network function responsible for selecting the appropriate network slice for a UE based on factors such as subscription, requested services, and network policies. 10
[0058] Network Exposure Function (NEF) [118] is a network function that exposes capabilities and services of the 5G network to external applications, enabling integration with third-party services and applications.
15 [0059] Network Repository Function (NRF) [120] 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.
20 [0060] Policy Control Function (PCF) [122] is a network function responsible for
policy control decisions, such as QoS, charging, and access control, based on subscriber information and network policies.
[0061] Unified Data Management (UDM) [124] is a network function that
25 centralizes the management of subscriber data, including authentication,
authorization, and subscription information.
[0062] Application Function (AF) [126] is a network function that represents
external applications interfacing with the 5G core network to access network
30 capabilities and services.
16

[0063] User Plane Function (UPF) [128] is a network function responsible for handling user data traffic, including packet routing, forwarding, and QoS enforcement.
5 [0064] Data Network (DN) [130] refers to a network that provides data services to
user equipment (UE) in a telecommunications system. The data services may include but are not limited to Internet services, private data network related services.
10 [0065] FIG. 2 illustrates an exemplary block diagram of a computing device [200]
upon which the features of the present disclosure may be implemented in accordance with exemplary implementation of the present disclosure. In an implementation, the computing device [200] may also implement a method for slice management in a network utilising the communication system [300]. In
15 another implementation, the computing device [200] itself implements the
method for slice management in the network using one or more units configured within the computing device [200], wherein said one or more units are capable of implementing the features as disclosed in the present disclosure.
20 [0066] The computing device [200] may include a bus [202] or other
communication mechanism for communicating information, and a processor [204] coupled with bus [202] for processing information. The processor [204] may be, for example, a general-purpose microprocessor. The computing device [200] may also include a main memory [206], such as a random-access memory
25 (RAM), or other dynamic storage device, coupled to the bus [202] for storing
information and instructions to be executed by the processor [204]. The main memory [206] also may be used for storing temporary variables or other intermediate information during execution of the instructions to be executed by the processor [204]. Such instructions, when stored in non-transitory storage
30 media accessible to the processor [204], render the computing device [200] into
a special-purpose machine that is customized to perform the operations
17

specified in the instructions. The computing device [200] further includes a read only memory (ROM) [208] or other static storage device coupled to the bus [202] for storing static information and instructions for the processor [204].
5 [0067] A storage device [210], such as a magnetic disk, optical disk, or solid-state
drive is provided and coupled to the bus [202] for storing information and instructions. The computing device [200] may be coupled via the bus [202] to a display [212], such as a cathode ray tube (CRT), Liquid crystal Display (LCD), Light Emitting Diode (LED) display, Organic LED (OLED) display, etc. for
10 displaying information to a computer user. An input device [214], including
alphanumeric and other keys, touch screen input means, etc. may be coupled to the bus [202] for communicating information and command selections to the processor [204]. Another type of user input device may be a cursor controller [216], such as a mouse, a trackball, or cursor direction keys, for communicating
15 direction information and command selections to the processor [204], and for
controlling cursor movement on the display [212]. The input device typically has two degrees 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 [0068] The computing device [200] 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 [200] causes or programs the computing device [200] to be a special-purpose machine. According to one implementation, the techniques herein are
25 performed by the computing device [200] in response to the processor [204]
executing one or more sequences of one or more instructions contained in the main memory [206]. Such instructions may be read into the main memory [206] from another storage medium, such as the storage device [210]. Execution of the sequences of instructions contained in the main memory [206] causes the
30 processor [204] to perform the process steps described herein. In alternative
18

implementations of the present disclosure, hard-wired circuitry may be used in place of or in combination with software instructions.
[0069] The computing device [200] also may include a communication interface
5 [218] coupled to the bus [202]. The communication interface [218] provides a
two-way data communication coupling to a network link [220] that is connected to a local network [222]. For example, the communication interface [218] may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a
10 corresponding type of telephone line. As another example, the communication
interface [218] may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface [218] sends and receives electrical, electromagnetic or optical signals that carry digital
15 data streams representing various types of information.
[0070] The computing device [200] can send messages and receive data, including program code, through the network(s), the network link [220] and the communication interface [218]. In the Internet example, a server [230] might
20 transmit a requested code for an application program through the Internet [228],
the ISP [226], the local network [222], the host [224] and the communication interface [218]. The received code may be executed by the processor [204] as it is received, and/or stored in the storage device [210], or other non-volatile storage for later execution.
25
[0071] Referring to FIG. 3, an exemplary block diagram of a communication system [300] for slice management in a network, is shown, in accordance with the exemplary implementations of the present disclosure. The communication system [300] comprises at least one Network Data Analytics Function
30 (NWDAF) unit [302], at least one Fulfilment management System (FMS) [304],
at least one NWDAF model [306] and at least one NWDAF UI [308] and at
19

least one storage unit [310]. Also, all of the components/ units of the
communication system [300] are assumed to be connected to each other unless
otherwise indicated below. As shown in the figures all units shown within the
communication system [300] should also be assumed to be connected to each
5 other. Also, in FIG. 3 only a few units are shown, however, the communication
system [300] may comprise multiple such units or the communication system [300] may comprise any such numbers of said units, as required to implement the features of the present disclosure. Further, in an implementation, the communication system [300] may be present in a user device/ user equipment
10 to implement the features of the present disclosure. The communication system
[300] 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 communication system [300] may reside in a server or a network entity. In yet another implementation, the communication system
15 [300] may reside partly in the server/ network entity and partly in the user
device.
[0072] The communication system [300] is configured for slice management in the
network, with the help of the interconnection between the components/units of
20 the communication system [300].
[0073] In order to manage the one or more slices in the network, the NWDAF unit [302] is configured to receive a network function data from at least one of one or more consumer devices [502] and one or more network functions.
25
[0074] As used herein, “NWDAF unit [302]” refers to a unit which is configured to collect, analyse and provide one or more actionable insights from the network function data. The NWDAF unit [302] aggregates the network function data from a plurality of network functions and plurality of entities for monitoring
30 network performance, one or more traffic patterns and resource utilization in
real time.
20

[0075] As used herein, “network function data” refers to an information generated
or processed by the one or more network functions within the network. The
network function data may include but not limited to one or more operational
5 metrics such as a traffic load, a latency, a bandwidth usage, error rates, that may
be specific to each network function.
[0076] The traffic load refers to an amount of data being processed by the network
function within a specified time frame. The traffic load indicates a current
10 workload or demand on a particular network function.
[0077] The latency refers to a time delay which is experienced in a data transmission between one or more network node or from a user device to the network function. 15
[0078] The bandwidth usage refers to an amount of network capacity required by the network function for transmitting the data over a pre-define time frame.
[0079] The error rates refer to frequency of errors encountered by the network
20 function during the data transmission.
[0080] As used herein, “one or more network functions” refers to one or more
components of a network infrastructure which have a defined functional
behaviour and defined interfaces such as an access and mobility management
25 function (AMF) [106],a Session Management Function (SMF) [108], etc.
[0081] As used herein, “consumer device [502]” is a device which is accessed or
operated by an operator of the network. The consumer device [502] may include
but not limited to a smartphone, a tablet, an Internet of Things (IoT) device or
30 any other device which is capable of a network connectivity and/or that may be
21

obvious to a person skilled in the art to implement the solution of the present disclosure.
[0082] Further, the NWDAF unit [302] is further configured to compute a load on
5 one or more slices in the network based on the received network function data.
[0083] As used herein, “slice” refers to a virtual network segment defined to meet
a specific service requirement for one or more applications such as an enhanced
mobile broadband (eMBB), massive machine-type communications (mMTC),
10 and ultra-reliable low latency communications (uRLLC).
[0084] The present disclosure encompasses that the NWDAF unit [302] may
compute the load on the one or more slices in the network by one or more
operations such as a data analysis operation, a loading parameter operation, a
15 real-time monitoring operation, etc.
[0085] Further, the data analysis operation refers to processing of the received
network function data via one or more data processing techniques. The one or
more data processing techniques may be prestored and/or predefined by the
20 operator in the storage unit [310]. Also, the one or more data processing
techniques may be a data processing technique that is obvious to the person skilled in the art, to implement the solution of the present disclosure.
[0086] Further, the loading parameter operation refers to calculation of a matrix
25 related to utilization of the one or more slices such as an amount of a data traffic
passing through the one or more slices, the number of an active sessions and the resource consumption, etc.
[0087] Furthermore, the real-time monitoring operation may refer to monitoring
30 one or more attributes of the one or more slices in the network in real-time or
22

near-real-time such as the data traffic. The real-time monitoring operation may indicate a load status of the one or more slices in the network.
[0088] Furthermore, the NWDAF unit [302] is further configured to identify a
5 breach of on one or more policies based on the computed load on the one or
more slices.
[0089] The present disclosure encompasses that the NWDAF unit [302] may identify the breach of the one or more policies by utilizing one or more protocols
10 which may be pre-defined by the operator and/or pre-stored in the storage unit
[310]. The NWDAF unit [302] may compare computed load values associated with the one or more slices with predefined load values of the one or more slices to detect the breach of the one or more policies associated with the one or more slices. Further, in an implementation of the present disclosure, the one or more
15 policies associated with the one or more slices comprises at least the predefined
load values associated with the one or more slices. Furthermore, the breach of the one or more policies associated with the one or more slices are detected in an event the computed load values associated with the one or more slices are greater than the predefined load values of the one or more slices.
20
[0090] For example, a maximum bandwidth utilization allocated to a particular slice is 1,000 bits per second, however the computed load on the one or more slices may surpass the maximum bandwidth utilization (i.e., a value of the computed load exceeds a value of the maximum bandwidth utilization), in this
25 event, the NWDAF unit [302] identifies it as the breach of policy. For another
example, in an event, a computed latency load on the one or more slices surpasses a latency threshold of the one or more slices, then the NWDAF unit [302] may identify this event as the breach of the policy.
30
23

[0091] The present disclosure encompasses that the NWDAF unit [302] is further configured to receive the one or more policies from the one or more consumer devices [502].
5 [0092] The present disclosure encompasses that the one or more policies are
consumer-defined policies.
[0093] As used herein, “one or more policies” may refer to a set of rules or a set of guidelines or a set of conditions that may be pre-defined by the operator and/or
10 pre-stored in the storage unit [310]. The one or more policies may define one or
more operation parameters such as quality of service, a predefined load value and bandwidth allocation associated with a particular slice in the network. The one or more policies may ensure that the slice operate within the set of rules or the set of guidelines or the set of conditions.
15
[0094] The present disclosure encompasses that the identified breach of the one or more policies is reported to the one or more consumer devices [502].
[0095] The present disclosure encompasses that the NWDAF unit [302] may
20 generate one or more alerts or one or more notification to the operator or
administrator or any other concerned authority responsible for managing the one or more slices of the network.
[0096] The present disclosure encompasses that the NWDAF unit [302] may
25 provide one or more automated response such as reallocation of resources,
adjusting one or more parameters, scaling up/down capacity for mitigating the breach of the one or more policies.
[0097] The FMS [304] is connected to at least the NWDAF unit [302]. The FMS
30 [304] is configured to provision, using a trained NWDAF model [306], one or
24

more new slices based on the one or more policies and the computed load on the one or more slices.
[0098] As used herein, “FMS [304]” refers to a module that manages one or more
5 network services and one or more resources. The FMS [304] interfaces with one
or more network components and a management system for ensuring a deployment and a maintenance of the one or more network services.
[0099] The present disclosure encompasses that the trained NWDAF model [306]
10 may be based on a machine learning model or an artificial intelligence model
that is trained on a historical and a real time data associated with the one or
more slices, the one or more policies associated with the slice and the computed
load on the one or more slices for forecasting a network performance based on
the computed load on the one or more slices. Further, the trained NWDAF
15 model may be a supervised learning model, an unsupervised learning model, a
deep learning model, a fine-tuned model or any other data model which may be known to the person skilled in the art, to implement the solution of the present disclosure.
20 [0100] The present disclosure encompasses that the trained NWDAF model [306]
is trained for forecasting a network performance based on the computed load on the one or more slices.
[0101] The present disclosure encompasses that the forecasting of the network
25 performance may be done via one or more machine learning forecasting
techniques or artificial intelligence forecasting techniques. For example, the trained NWDAF model [306] may analyse the computed load on the one or more slices and predict one or more congestion points, one or more resource shortages, etc. 30
25

[0102] The present disclosure encompasses that the communication system [300] further comprises NWDAF User Interface (UI) [308] configured to display a visualization of the forecasted network performance. Further, the one or more policies are modified on the NWDAF UI [308]. 5
[0103] As used herein, “NWDAF UI” may refer to a user interface which is designed to visualize and present one or more analytics and one or more insights derived from the network function data, including the forecasted network performance based on the trained NWDAF model [306]. 10
[0104] The visualization of the forecasted network performance may include displaying of one or more graphical representations or one or more charts that illustrate the forecasted network performance.
15 [0105] The visualization of the forecasted network performance may also include
displaying of one or more metrics such as a predicted latency, a bandwidth utilization, or other performance indicators across different slices of the network.
20 [0106] The visualization of the forecasted network performance may also include
displaying of one or more temporal views for depicting a network performance under one or more scenarios or one or more load conditions.
[0107] The present disclosure encompasses that the one or more policies are
25 modified on the NWDAF UI [308], based on one or more user interactions. For
example, the operator or the administrator may interact with the NWDAF UI [308] for modifying the one or more policies. Also, one or more policies may include one or more thresholds which may be amended, adjusted or updated by the operator or the administrator. 30
26

[0108] The present disclosure encompasses that the NWDAF unit [302] is further
configured to generate a slice management report comprising a breach
information, and one or more actions for managing slice consumption. The
NWDAF unit [302] is further configured to transmit the slice management
5 report to the one or more consumer devices [502].
[0109] As used herein, “breach information” may refer to one or more details about one or more breaches or deviations from the one or more policies or one or more thresholds defined in the one or more policies. 10
[0110] The present disclosure encompasses that the one or more actions may
include one or more recommendations from the NWDAF unit [302] based on
the forecasted network performance. The one or more actions may include
scaling up/down the one or more resources, a reallocation of the one or more
15 resources, and modification in the one or more parameters.
[0111] The present disclosure encompasses that the slice management report may
be generated by the NWDAF unit [302] based on one or more report generation
protocols. The one or more report generation protocols may be predefined
20 and/or pre-stored in the storage unit [310]. Further the slice management report
may be structured according to one or more predefined templates and/or one or more formats.
[0112] Referring to FIG. 4, wherein flow diagram of a method [400] for slice
25 management in a network, in accordance with exemplary implementations of
the present disclosure is shown. In an implementation the method [400] is
performed by the communication system [300]. Further, in an implementation,
the communication system [300] may be present in a server device to implement
the features of the present disclosure. Also, as shown in FIG. 4, the method
30 [400] starts at step [402].
27

[0113] At step [404], the method comprises receiving, by a Network Data Analytics Function (NWDAF) unit [302], a network function data from at least one of one or more consumer devices [502] and one or more network functions.
5 [0114] As used herein, “NWDAF unit [302]” refers to a unit which is configured
to collect, analyse and provide one or more actionable insights from the network
function data. The NWDAF unit [302] aggregates the network function data
from a plurality of network functions and plurality of entities for monitoring
network performance, one or more traffic patterns and resource utilization in
10 real time.
[0115] As used herein, “network function data” refers to an information generated
or processed by the one or more network functions within the network. The
network function data may include but not limited to one or more operational
15 metrics such as a traffic load, a latency, a bandwidth usage, error rates, that may
be specific to each network function.
[0116] The traffic load refers to an amount of data being processed by the network
function within a specified time frame. The traffic load indicates a current
20 workload or demand on a particular network function.
[0117] The latency refers to a time delay which is experienced in a data transmission between one or more network node or from a user device to the network function. 25
[0118] The bandwidth usage refers to an amount of network capacity required by the network function for transmitting the data over a pre-define time frame.
[0119] The error rates refer to frequency of errors encountered by the network
30 function during the data transmission.
28

[0120] As used herein, “one or more network functions” refers to one or more components of a network infrastructure which have a defined functional behaviour and defined interfaces such as an access and mobility management function (AMF) [106],a Session Management Function (SMF) [108], etc. 5
[0121] As used herein, “consumer device [502]” is a device which is accessed or
operated by an operator of the network. The consumer device [502] may include
but not limited to a smartphone, a tablet, an Internet of Things (IoT) device or
any other device which is capable of a network connectivity and/or that may be
10 obvious to a person skilled in the art to implement the solution of the present
disclosure.
[0122] At step [406], the method comprises computing, by the NWDAF unit [302],
a load on one or more slices in the network based on the received network
15 function data.
[0123] As used herein, “slice” refers to a virtual network segment defined to meet
a specific service requirement for one or more applications such as enhanced
mobile broadband (eMBB), massive machine-type communications (mMTC),
20 and ultra-reliable low latency communications (uRLLC).
[0124] The present disclosure encompasses that the NWDAF unit [302] may
compute the load on the one or more slices in the network by one or more
operations such as a data analysis operation, a loading parameter operation, a
25 real-time monitoring operation, etc.
[0125] Further, the data analysis operation refers to processing of the received
network function data via one or more data processing techniques. The one or
more data processing techniques may be prestored and/or predefined by the
30 operator in the storage unit [310]. Also, the one or more data processing
29

techniques may be a data processing technique that is obvious to the person skilled in the art, to implement the solution of the present disclosure.
[0126] Further, the loading parameter operation refers to calculation of a matrix
5 related to utilization of the one or more slices such as an amount of data traffic
passing through the one or more slices, the number of an active sessions and the resource consumption, etc.
[0127] Furthermore, the real-time monitoring operation may refer to monitoring of
10 the one or more slices in the network in real-time or near-real-time such as the
data traffic. The real-time monitoring operation may indicate a load status of the one or more slices in the network.
[0128] At step [408], the method comprises identifying, by the NWDAF unit [302],
15 a breach of one or more policies based on the computed load on the one or more
slices.
[0129] The present disclosure encompasses that the NWDAF unit [302] may identify the breach of the one or more policies by utilizing one or more protocols
20 which may be pre-defined by the operator and/or pre-stored in the storage unit
[310]. The NWDAF unit [302] may compare computed load values associated with the one or more slices with predefined load values of the one or more slices to detect the breach of the one or more policies associated with the one or more slices. Further, in an implementation of the present disclosure, the one or more
25 policies associated with the one or more slices comprises at least the predefined
load values associated with the one or more slices. Furthermore, the breach of the one or more policies associated with the one or more slices are detected in an event the computed load values associated with the one or more slices are greater than the predefined load values of the one or more slices.
30
30

[0130] For example, a maximum bandwidth utilization allocated to a particular
slice is 1,000 bits per second, however the computed load on the one or more
slices may surpass the maximum bandwidth utilization (i.e., a value of the
computed load exceeds a value of the maximum bandwidth utilization), in this
5 event, the NWDAF unit [302] identifies it as the breach of policy. For another
example, in an event, a computed latency load on the one or more slices surpasses a latency threshold of the one or more slices, then the NWDAF unit [302] may identify this event as the breach of the policy.
10 [0131] The present disclosure encompasses that the identified breach of the one or
more policies is reported to the one or more consumer devices [502].
[0132] The present disclosure encompasses that the NWDAF unit [302] may
generate one or more alerts or one or more notification to the operator or
15 administrator or any other concerned authority responsible for managing the
one or more slices of the network.
[0133] The present disclosure encompasses that the NWDAF unit [302] may
provide one or more automated response such as reallocation of resources,
20 adjusting one or more parameters, scaling up/down capacity for mitigating the
breach of the one or more policies.
[0134] The present disclosure encompasses that the receiving, at the NWDAF unit [302], the one or more policies from the one or more consumer devices [502]. 25
[0135] The present disclosure encompasses that the one or more policies are consumer-defined policies.
[0136] As used herein, “one or more policies” may refer to a set of rules or a set of
30 guidelines or a set of conditions that may be pre-defined by the operator and/or
pre-stored in the storage unit [310]. The one or more policies may define one or
31

more operation parameters such as quality of service, a predefined load value, and bandwidth allocation associated with a particular slice in the network. The one or more policies may ensure that the one or more slices operate within the set of rules or the set of guidelines or the set of conditions. 5
[0137] At step [410], the method comprises provisioning, by a Fulfilment management System (FMS) [304] using a trained NWDAF model [306], one or more new slices based on the one or more policies and the computed load on the one or more slices.
10
[0138] As used herein, “FMS [304]” refers to a module, that manages one or more network services and one or more resources. The FMS [304] interfaces with one or more network components and a management system for ensuring a deployment and a maintenance of the one or more network services.
15
[0139] The present disclosure encompasses that the trained NWDAF model [306] may be based on a machine learning model or an artificial intelligence model that is trained on a historical and a real time data associated with the one or more slices, the one or more policies associated with the slice and the computed
20 load on the one or more slices for forecasting a network performance based on
the computed load on the one or more slices. Further, the trained NWDAF model may be a supervised learning model, an unsupervised learning model, a deep learning model, a fine-tuned model or any other data model which may be known to the person skilled in the art, to implement the solution of the present
25 disclosure.
[0140] The present disclosure encompasses that the method further comprises
training the trained NWDAF model [306] for forecasting a network
performance based on the computed load on the one or more slices. The method
30 further comprises displaying, by a NWDAF UI [308], a visualization of the
32

forecasted network performance, wherein the one or more policies are modified on the NWDAF UI [308].
[0141] The present disclosure encompasses that the forecasting of the network
5 performance may be done via one or more machine learning FORECASTING
techniques or artificial intelligence forecasting techniques. For example, the trained NWDAF model [306] may analyse the computed load on the one or more slices and predict one or more congestion points, one or more resource shortages, etc. 10
[0142] As used herein, “NWDAF UI” may refer to a user interface which is designed to visualize and present one or more analytics and one or more insights derived from the network function data, including the forecasted network performance based on the trained NWDAF model [306]. 15
[0143] The visualization of the forecasted network performance may include displaying of one or more graphical representations or one or more charts that illustrate the forecasted network performance.
20 [0144] The visualization of the forecasted network performance may also include
displaying of one or more metrics such as a predicted latency, a bandwidth utilization, or other performance indicators across different slices of the network.
25 [0145] The visualization of the forecasted network performance may also include
displaying of one or more temporal views for depicting a network performance under one or more scenarios or one or more load conditions.
[0146] The present disclosure encompasses that the one or more policies are
30 modified on the NWDAF UI [308], based on one or more user interactions. For
example, the operator or the administrator may interact with the NWDAF UI
33

[308] for modifying the one or more policies. Also, one or more policies may include one or more thresholds which may be amended, adjusted or updated by the operator or the administrator.
5 [0147] The present disclosure encompasses that the method further comprises
generating, by the NWDAF unit [302], a slice management report comprising a breach information, and one or more actions for managing slice consumption. The method further comprises transmitting, by the NWDAF unit [302], the slice management report to the one or more consumer devices [502]. 10
[0148] As used herein, “breach information” may refer to one or more details about one or more breaches or deviations from the one or more policies or one or more thresholds defined in the one or more policies.
15 [0149] The present disclosure encompasses that the one or more actions may
include one or more recommendations from the NWDAF unit [302] based on the forecasted network performance. The one or more actions may include scaling up/down the one or more resources, a reallocation of the one or more resources, and modification in the one or more parameters.
20
[0150] The present disclosure encompasses that the slice management report may be generated by the NWDAF unit [302] based on one or more report generation protocols. The one or more report generation protocols may be predefined and/or pre-stored in the storage unit [310]. Further the slice management report
25 may be structured according to one or more predefined templates and/or one or
more formats.
[0151] Thereafter, at step [412], the method [400] is terminated.
30 [0152] Referring to FIG. 5, wherein flow diagram of an exemplary method [500]
of a method for slice management in a network, in accordance with exemplary
34

implementations of the present disclosure is shown. In an implementation the method [500] is performed by the communication system [300]. Further, in an implementation, the communication system [300] may be present in a server device to implement the features of the present disclosure. 5
[0153] Also, as shown in FIG. 5, at step S1, one or more data consumers subscribe for a slice load analytics towards a Network Data Analytics Function (NWDAF) unit [302] according to one or more define threshold policies (i.e. one or more policies).
10
[0154] At step S2, the NWDAF unit [302] collects a slice load information from a specific data point such as one or more data consumers and compute the slice load analytics as per the one or more defined policies. Further, the NWDAF User Interface (UI) utilizes the slice load analytics for a dashboarding. The
15 dashboarding refers to visualization or report generation which enhance a
monitoring of the slice in the network and improve a decision making.
[0155] At step S3, one or more NWDAF model [306] may consume the slice load
analytics for training the one or more NWDAF model [306]and for forecasting
20 of a network performance. Also, step S3 enables a proactive network
management and a resource planning.
[0156] Also, one more reporting or one or more actions are rapidly communicated
to the one or more data consumers. Additionally, rapid communication of one
25 or more reports and/or one or more actions to the data consumers ensures timely
responsiveness to one or more network conditions and one or more operational
requirements.
[0157] Thereafter, the method [500] terminates. 30
35

[0158] The present disclosure further discloses a user equipment (UE) for slice
management in a network. The UE comprises a memory, a processor in
connection with the memory. The processor is configured to transmit to a
communication system [300], a network function data associated with at least
5 one of one or more consumer devices [502] and one or more network functions.
The processor is further configured to receive, from the communication system
[300], a response comprising provisioning of one or more new slices based on
the network function data. The one or more new slices are provisioned based on
computing, by the communication system [300], a load on one or more slices in
10 the network based on the received network function data. The one or more new
slices are provisioned is further based on identifying, by the communication system [300], a breach of one or more policies based on the computed load on the one or more slices.
15 [0159] The present disclosure further discloses a non-transitory computer readable
storage medium storing one or more instructions for slice management in a network, the one or more instructions include executable code which, when executed by one or more units of a communication system [300], causes: a Network Data Analytics Function (NWDAF) unit [302] of the communication
20 system [300], to receive a network function data from at least one of one or
more consumer devices and one or more network functions. Further, the one or more instructions when further executed causes the NWDAF unit [302] of the communication system [300] to compute, a load on one or more slices in the network based on the received network function data. Further, the one or more
25 instructions when further executed causes the NWDAF unit [302] of the
communication system [300] to identify a breach of on one or more policies based on the computed load on the one or more slices. Further, the one or more instructions when further executed causes a Fulfilment System (FMS) [304] of the communication system [300], to provision, using a trained NWDAF model
30 [306], one or more new slices based on the one or more policies and the
computed load on the one or more slices.
36

[0160] As is evident from the above, the present disclosure provides a technically advanced solution for slice management in a network. The present solution efficiently monitors and optimize slice performance across a network infrastructure. The present solution ensures a proactive management of slice in the network, by automating a provision of one or more new slices that reduces a manual intervention. The present solution provides one or more actions for manging the one or more slices in the network, which facilitate in decision making for an optimizing slice consumption and maintaining a service quality. The present solution also provides visualization of a forecasted network performance for better monitoring and analysis of the one or more slices in the network. The present solution allows an administrator or an operator of the network to modify one or more policies through a user interface. Hence, the present solution provides an efficient way for slice management in the network which enhances a service quality, customer experience and reduce resource wastage in the network.
[0161] While considerable emphasis has been placed herein on the disclosed implementations, it will be appreciated that many implementations can be made and that many changes can be made to the implementations without departing from the principles of the present disclosure. These and other changes in the implementations of the present disclosure will be apparent to those skilled in the art, whereby it is to be understood that the foregoing descriptive matter to be implemented is illustrative and non-limiting.
[0162] Further, in accordance with the present disclosure, it is to be acknowledged that the functionality described for the various components/units can be implemented interchangeably. While specific embodiments may disclose a particular functionality of these units for clarity, it is recognized that various configurations and combinations thereof are within the scope of the disclosure. The functionality of specific units as disclosed in the disclosure should not be

construed as limiting the scope of the present disclosure. Consequently, alternative arrangements and substitutions of units, provided they achieve the intended functionality described herein, are considered to be encompassed within the scope of the present disclosure.

We Claim:
1. A method for slice management in a network, the method comprising:
- receiving, by a Network Data Analytics Function (NWDAF) unit [302], a network function data from at least one of one or more consumer devices [502] and one or more network functions;
- computing, by the NWDAF unit [302], a load on one or more slices in the network based on the received network function data;
- identifying, by the NWDAF unit [302], a breach of one or more policies based on the computed load on the one or more slices; and
- provisioning, by a Fulfilment management System (FMS) [304] using a trained NWDAF model [306], one or more new slices based on the one or more policies and the computed load on the one or more slices.

2. The method as claimed in claim 1, wherein the identified breach of the one or more policies is reported to the one or more consumer devices [502].
3. The method as claimed in claim 1, the method further comprising:
- receiving, at the NWDAF unit [302], the one or more policies from the one
or more consumer devices [502].
4. The method as claimed in claim 1, wherein the one or more policies are consumer-defined policies.
5. The method as claimed in claim 1, the method further comprising:

- training the trained NWDAF model [306] for forecasting a network performance based on the computed load on the one or more slices; and
- displaying, by a NWDAF UI [308], a visualization of the forecasted network performance, wherein the one or more policies are modified on the NWDAF UI [308].

6. The method as claimed in claim 1, the method further comprising:
- generating, by the NWDAF unit [302], a slice management report comprising a breach information, and one or more actions for managing slice consumption; and
- transmitting, by the NWDAF unit [302], the slice management report to the one or more consumer devices [502].
7. A communication system [300] for slice management in a network, the
communication system [300] comprises:
- a Network Data Analytics Function (NWDAF) unit [302] configured to:
o receive a network function data from at least one of one or more consumer devices [502] and one or more network functions,
o compute a load on one or more slices in the network based on the received network function data, and
o identify a breach of on one or more policies based on the computed load on the one or more slices; and
- a Fulfilment System (FMS) [304] connected to at least the NWDAF unit
[302], the FMS [304] configured to provision, using a trained NWDAF
model [306], one or more new slices based on the one or more policies and
the computed load on the one or more slices.
8. The communication system [300] as claimed in claim 7, wherein the identified breach of the one or more policies is reported to the one or more consumer devices [502].
9. The communication system [300] as claimed in claim 7, wherein the NWDAF unit [302] is further configured to receive the one or more policies from the one or more consumer devices [502].
10. The communication system [300] as claimed in claim 7, wherein the one or more policies are consumer-defined policies.

11. The communication system [300] as claimed in claim 7, further comprising:
- the trained NWDAF model [306] is trained for forecasting a network performance based on the computed load on the one or more slices; and
- a NWDAF User Interface (UI) [308] configured to display a visualization of the forecasted network performance, wherein the one or more policies are modified on the NWDAF UI [308].
12. The communication system [300] as claimed in claim 7, wherein the NWDAF
unit [302] is further configured to:
- generate a slice management report comprising a breach information, and one or more actions for managing slice consumption; and
- transmit the slice management report to the one or more consumer devices [502].