Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method is provided for handling SRS resource allocation in multi-TRP in wireless networks by a BS or a TRP unit. The method includes configuring CS allocation to at least one port of a UE. The CS allocation uses values from a sub-set of a plurality of allowable CS values. Further, the method includes receiving a SRS using the allocated CS. In an embodiment, per—port CS allocation provides flexibility of using the CS resources efficiently to minimize inter-TRP interference. The method can be used to allocate CS and SRS resources efficiently.

Abstract: The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). Embodiments of present disclosure relates to apparatus and method for enabling optimized decoding of data packet in HARQ based communication in wireless communication network. Initially, unsuccessful decoding of instant data packet received from transmitting unit is identified. If number of previous data packets, received preceding the instant data packet, is greater than one, subsequent decoding is enabled in the receiving unit for the instant data packet. The subsequent decoding includes generating sequentially, modified versions of data packets, for decoding. The modified versions comprises all possible weighted combinations of the data packets with at least one of the instant data packet and one or more of the previous data packets.

Abstract: Embodiments of the disclosure provide a method for provisioning mobility management during congestion in a wireless network by a network apparatus. The method includes: detecting a plurality of parameters of a current cell associated with at least one User Equipment (UE) in the wireless network; predicting an uplink (UL) congestion condition with an uplink radio resource of the current cell by applying at least one machine learning model to the plurality of parameters of the current cell; performing a mobility of the at least one UE from the current cell to at least one target cell in the wireless network based on the predicted UL congestion condition with the uplink radio resource of the current cell associated with the at least one UE.

Abstract: Embodiments herein provide a method for predicting iterations for decoding an encoded data at an electronic device. The method includes: receiving, by the electronic device, the encoded data; detecting, by the electronic device, signal parameters associated with the encoded data; predicting, by the electronic device, one of a cyclic redundancy check (CRC) failure, CRC success, and a CRC uncertainty in iterations for decoding the encoded data based on the signal parameters using a Neural Network (NN) model.

Abstract: A method performed by a Citizens Broadband Radio Service (CBRS) device (CBSD), may comprise, generating a grant request message comprising information on a number of grants and information on operation parameters. Each of the operation parameters is associated with a desired channel identified based on a list of desired channels. The method may comprise sending the grant request message to a Spectrum Access System (SAS).

Abstract: The present disclosure relates to a method and an apparatus for decoding data packets in communication network. The method comprises receiving one or more data packets related to each of one or more data types; and decoding the one or more data packets using a parity check matrix associated with the corresponding data type, wherein the parity check matrix comprises a plurality of layers, arranged according to a combination of layers which is determined using a reinforcement model.

Abstract: A Method and a system for securing a SDN controller from denial of service attack are provided. A SDN controller receives, from a networking device, data packets pertaining to a flow in Packet_IN messages, if the flow does not match flow entries in a first flow table of the networking device. A table miss flow entry pertaining to the flow is created in a second flow table of the networking device for sending the Packet_IN. The SDN controller installs a flood prevention flow entry in the second flow table to enable the networking device to drop subsequent data packets pertaining to the flow until the SDN controller installs, in the first flow table, a flow entry matching the flow. The flood prevention flow entry is deleted from the second flow table after the installation of the flow entry matching the flow.

Abstract: In a method of resource allocation for mobility management of a user equipment (UE) in a wireless network, a base station (BS) in the wireless network receives a plurality of mobility parameters of the UE, provides a service on an active BWP of a plurality of BWPs to the UE, detects a change in at least one mobility parameter from the plurality of mobility parameters, determines whether a change in the at least one mobility parameter meets a QoS/QCI criterion, and varies the resource allocation for the BWP, in response to detecting that the change in the at least one mobility parameter meets the QoS/QCI criterion. The BS may vary the allocation by triggering a switch from the active BWP to an optimal BWP selected from passive BWPs, by changing a current subcarrier spacing (SCS) of the active BWP to a selected optimal SCS, or by updating the passive BWPs.

Abstract: The present disclosure relates to a method and an apparatus for decoding data packets in communication network. The method comprises receiving one or more data packets related to each of one or more data types; and decoding the one or more data packets using a parity check matrix associated with the corresponding data type, wherein the parity check matrix comprises a plurality of layers, arranged according to a combination of layers which is determined using a reinforcement model.

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: The present disclosure discloses methods and systems for efficiently allocating grants for resource allocation based on BSR index. The method includes receiving by a Base-Station (BS) a Buffer Status Report (BSR) index value from a User Equipment (UE) along with buffer size corresponding to an Uplink (UL) data transmission request. Upon determining the buffer size corresponding to the received BSR index value to be greater than a predefined threshold value, resources to the UE is allocated corresponding to a maximum buffer size of (BSR index-n). Upon receiving UL data from the UE based on the allocated resources, resources to the UE are allocated subsequently using a predefined allocation mechanism until one of, detection of padding bits in the received UL data and on detecting maximum allocation of predefined number of allocation.

Abstract: The present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). The present disclosure provides methods and systems for reducing fronthaul bandwidth. Embodiments herein relate to the field of wireless networks and more particularly to reducing fronthaul bandwidth during a transfer of data between at least one radio node and at least one centralized node in a wireless network. A method disclosed herein includes determining parameters specific to at least one User Equipment (UE) using a machine learning method. The method further includes transferring data between the at least one radio node and the at least one centralized node over a fronthaul link by compressing the data using the parameters that are determined using the machine learning method.

Abstract: Provided is a method, performed by a terminal, of controlling access to a base station in a wireless communication system.

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 present disclosure relates to a pre-5th-Generation (5G) or 5G communication system to be provided for supporting higher data rates Beyond 4th-Generation (4G) communication system such as long term evolution (LTE). Methods and systems for optimizing computation of log-likelihood ratio (LLR) for decoding modulated symbols. A method disclosed herein involves receiving at least one symbol transmitted from at least one device, wherein the received at least one symbol is encoded and modulated symbol including a plurality of data bits. The method further includes computing a log-likelihood ratio (LLR) of each bit in the received at least one symbol for decoding the received at least one symbol using a centroid method that involves exploiting a symmetry of a constellation of code words and/or a uncertainty region defined on a constellation of code words.

Abstract: A method of enabling multi-connectivity in a wireless network includes predicting at least one of a RLF, a call drop, and a jitter based on a plurality of Key Performance Indicators (KPIs) associated with the UE and the wireless network, determining whether the UE is in one of a Dual Connectivity (DC) mode and a carrier aggregation (CA) mode, performing one of: adding a new secondary gNodeB (gNB) in response to determining that the UE is not in both the DC mode and the CA mode and converting the new gNB to a master gNB, converting an existing secondary gNodeB to a master gNodeB with one of an existing Master Cell Group (MCG) gNodeB and another gNodeB as the secondary gNodeB, in response to determining that the UE is in the DC mode, and converting an existing secondary cell to a primary cell.

Abstract: Provided is a method of operating a user equipment (UE) in a wireless network for managing physical downlink control channel (PDCCH) data. The method includes: obtaining a plurality of network parameters, predicting at least one aggregation level (AL) used by a base station (BS) associated with the UE to transmit the PDCCH data in the wireless network based on the plurality of received network parameters, and decoding the PDCCH data based on the at least one predicted AL.

Abstract: Accordingly, the embodiment herein is to provide a method for resource allocation, by a server (100a), in multiple Band Width Part (BWP) system. The method includes receiving a plurality of data packets from a network device for transmission to a UE (100b). Further, the method includes determining a plurality of network parameters associated with the network device, the UE (100b), and the plurality of received data packets. Further, the method includes grouping the plurality of received data packets based on the plurality of network parameters. Further, the method includes allocating a BWP and/or sub-carrier spacing to each grouped data packets based on the plurality of network parameters. Further, the method includes sending the plurality of group data packets using the allocated BWP and/or the allocated subcarrier spacing.

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.