Abstract: Embodiments herein disclose a method and a device for embedding neural networks as a matrix for a network device in wireless networks. The method includes receiving s from the network device. Further, the method also includes determining the KPI among the plurality of KPIs as target KPIs that related to a network anomaly using a ML model. Further, the method also includes determining a correlation of the target KPI with the plurality of KPIs for the network anomaly using the ML model. Further, the method also includes determining the matrix indicating a relation of the target KPI with the plurality of KPIs. Furthermore, the method includes optimizing a resource of the network device by embedding the matrix in the network device.

Abstract: The present disclosure relates to method and resource allocation entity for allocating Physical Uplink Control Channel (PUCCH) resources to User Equipments (UEs) in a communication network. The method comprises identifying one or more PUCCH resource pools to be allocated to cell camped on by one or more UEs, based on one or more first parameters associated with cell, using machine learning model. Further, the method comprises identifying one or more PUCCH resources to be allocated to each UE, based on one or more PUCCH resources pools and one or more second parameters associated with corresponding UE, using machine learning model. The method comprises allocating one or more PUCCH resource sets from plurality of PUCCH resource sets to each UE, based on one or more PUCCH resources identified for each UE and one or more parameters of plurality of PUCCH resource sets.

Abstract: The present disclosure relates to method and resource allocation entity for allocating Physical Uplink Control Channel (PUCCH) resources to User Equipments (UEs) in a communication network. The method comprises identifying one or more PUCCH resource pools to be allocated to cell camped on by one or more UEs, based on one or more first parameters associated with cell, using machine learning model. Further, the method comprises identifying one or more PUCCH resources to be allocated to each UE, based on one or more PUCCH resources pools and one or more second parameters associated with corresponding UE, using machine learning model. The method comprises allocating one or more PUCCH resource sets from plurality of PUCCH resource sets to each UE, based on one or more PUCCH resources identified for each UE and one or more parameters of plurality of PUCCH resource sets.

Abstract: The disclosure describes a method and system for dynamic frequency scaling of multi-core processor in wireless communication networks. The method comprising: transmitting, by a network node (NN), core-load data and a plurality of key indicators of each core group of a plurality of core groups in the multi-core processor to a central management entity (CME); receiving, by the NN, a core-load prediction model associated for each core group from the CME; determining, by the NN, an estimated core-load data for each core group using the associated core-load prediction model and determining, by the NN, a maximum estimated core-load data among the estimated core-load data of each core group; and determining, by the NN, an optimum multi-core processor frequency for the network node based on the maximum estimated core-load data.

Abstract: Embodiments herein provide a method for managing a network slice load in a wireless network. The method includes: sending, by a CN device, a request message for network slice load information to a base station of the wireless network; determining, by the base station, the network slice load based on the parameter; sending, by the base station, the response message comprising the network slice load information to the core network device for managing the network slice load; determining, by a CN device, the network slice load at the base station based on the response message; and managing, by a CN device, the network slice load at the base station.

Abstract: Embodiments herein provide a method for generating a real-time radio coverage map in a wireless network by a network apparatus. The method includes: receiving real-time geospatial information from one or more geographical sources in the wireless network; determining handover information of at least one user equipment (UE) in the wireless network from a plurality of base stations based on the real-time geospatial information; and generating the real-time radio coverage map based on the handover information of at least one UE and the real-time geospatial information.

Abstract: The disclosure describes a method and system for dynamic frequency scaling of multi-core processor in wireless communication networks. The method comprising: transmitting, by a network node (NN), core-load data and a plurality of key indicators of each core group of a plurality of core groups in the multi-core processor to a central management entity (CME); receiving, by the NN, a core-load prediction model associated for each core group from the CME; determining, by the NN, an estimated core-load data for each core group using the associated core-load prediction model and determining, by the NN, a maximum estimated core-load data among the estimated core-load data of each core group; and determining, by the NN, an optimum multi-core processor frequency for the network node based on the maximum estimated core-load data.

Abstract: Embodiments herein disclose a method and a device for embedding neural networks as a matrix for a network device in wireless networks. The method includes receiving s from the network device. Further, the method also includes determining the KPI among the plurality of KPIs as target KPIs that related to a network anomaly using a ML model. Further, the method also includes determining a correlation of the target KPI with the plurality of KPIs for the network anomaly using the ML model. Further, the method also includes determining the matrix indicating a relation of the target KPI with the plurality of KPIs. Furthermore, the method includes optimizing a resource of the network device by embedding the matrix in the network device.

Abstract: A method for decoding data by an electronic device is provided. The method includes receiving, by the electronic device, encoded data, determining, by the electronic device, a sparsity of a plurality of Machine Learning (ML) models of a turbo decoder of the electronic device for decoding the encoded data based on Quality-of-Service (QoS) parameters, and decoding, by the electronic device, the encoded data using the turbo decoder based on the determined sparsity.

Abstract: A method implemented at one or more base stations for a power management of the one or more base stations in a wireless communication network is disclosed. The method includes transmitting, by the one or more base stations, a plurality of Key Performance Indicator (KPI) parameters associated with the one or more base stations to a federal server controller. The method includes receiving, by the one or more base stations from the federal server controller, a KPI relationship predicting a future load based on the plurality of KPI parameters associated with the one or more base stations. The method includes adjusting, by the one or more base stations, a Central Processing Unit (CPU) frequency associated with the one or more base stations autonomously based on the KPI relationship for managing the future load associated with the one or more base stations.

Abstract: The present disclosure relates to method and resource allocation entity for allocating Physical Uplink Control Channel (PUCCH) resources to User Equipments (UEs) in a communication network. The method comprises identifying one or more PUCCH resource pools to be allocated to cell camped on by one or more UEs, based on one or more first parameters associated with cell, using machine learning model. Further, the method comprises identifying one or more PUCCH resources to be allocated to each UE, based on one or more PUCCH resources pools and one or more second parameters associated with corresponding UE, using machine learning model. The method comprises allocating one or more PUCCH resource sets from plurality of PUCCH resource sets to each UE, based on one or more PUCCH resources identified for each UE and one or more parameters of plurality of PUCCH resource sets.

Abstract: Embodiments herein provide a method for generating a real-time radio coverage map in a wireless network by a network apparatus. The method includes: receiving real-time geospatial information from one or more geographical sources in the wireless network; determining handover information of at least one user equipment (UE) in the wireless network from a plurality of base stations based on the real-time geospatial information; and generating the real-time radio coverage map based on the handover information of at least one UE and the real-time geospatial information.

Abstract: A method for decoding data by an electronic device (100) is provided. The method includes receiving, by the electronic device (100), encoded data. The method includes determining, by the electronic device (100), a sparsity of a plurality of Machine Learning (ML) models (301, 302) of a turbo decoder (150) of the electronic device (100) for decoding the encoded data based on Quality-of-Service (QoS) parameters. The method includes decoding, by the electronic device (100), the encoded data using the turbo decoder (150) based on the determined sparsity.

Abstract: Embodiments herein provide a method for managing a network slice load in a wireless network. The method includes: sending, by a CN device, a request message for network slice load information to a base station of the wireless network; determining, by the base station, the network slice load based on the parameter; sending, by the base station, the response message comprising the network slice load information to the core network device for managing the network slice load; determining, by a CN device, the network slice load at the base station based on the response message; and managing, by a CN device, the network slice load at the base station.