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

TL;DR

This paper proposes a neural network-based method for resource block allocation in 5G NR, formulating the problem with a Hopfield Neural Network and demonstrating its efficiency through simulations.

Contribution

It introduces a novel 2D Hopfield Neural Network model for resource allocation in 5G NR, addressing the problem with an energy function approach.

Findings

The neural network approach outperforms traditional methods in simulations.

The proposed method efficiently allocates RBs based on channel states.

Simulation results confirm the effectiveness of the neural network model.

Abstract

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 slot duration. 5G New Radio (NR) allows for a large number of block shapes ranging from 15 kHz to 480 kHz. In this paper, we address the problem of RBs allocation to UEs. The RBs are allocated at the beginning of each time slot based on the channel state of each UE. The problem is formulated based on the Generalized Proportional Fair (GPF) scheduling. Then, we model the problem as a 2-Dimension Hopfield Neural Networks (2D-HNN). Finally, in an attempt to solve the problem, the energy function of 2D-HNN is investigated. Simulation results show the efficiency of the proposed approach.