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The minimum frequency-time unit that can be allocated to User Equipments(UEs) in the fifth generation (5G) cellular networks is a Resource Block (RB).A RB is a channel composed of a set of OFDM subcarriers for a given time slotduration. 5G New Radio (NR) allows for a large number of block shapes rangingfrom 15 kHz to 480 kHz. In this paper, we address the problem of RBs allocationto UEs. The RBs are allocated at the beginning of each time slot based on thechannel state of each UE. The problem is formulated based on the GeneralizedProportional Fair (GPF) scheduling. Then, we model the problem as a 2-DimensionHopfield Neural Networks (2D-HNN). Finally, in an attempt to solve the problem,the energy function of 2D-HNN is investigated. Simulation results show theefficiency of the proposed approach.

Radio Resource Allocation in 5G New Radio: A Neural Networks Based Approach)