Abstract: A method for decoding user data by an apparatus in a wireless network is provided. The method includes receiving, by the apparatus, user data associated with a plurality of network parameters. The method includes training, by the apparatus, a neural network using the plurality of received network parameters. Further, the method includes computing, by the apparatus, a Log Likelihood Ratio (LLR) using the trained neural network. Further, the method includes decoding, by the apparatus, the received user data using the computed LLR.

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: 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: A method for decoding user data by an apparatus in a wireless network is provided. The method includes receiving, by the apparatus, user data associated with a plurality of network parameters. The method includes training, by the apparatus, a neural network using the plurality of received network parameters. Further, the method includes computing, by the apparatus, a Log Likelihood Ratio (LLR) using the trained neural network. Further, the method includes decoding, by the apparatus, the received user data using the computed LLR.