Deep Learning for Radio Resource Allocation in Multi-Cell Networks

TL;DR

This paper explores the use of deep learning to efficiently solve radio resource allocation problems in multi-cell 5G networks, reducing computational complexity and achieving high accuracy in predictions.

Contribution

It introduces a supervised deep learning model trained on genetic algorithm data to optimize sub-band and power allocation in multi-cell networks, demonstrating high accuracy.

Findings

DL model predicts solutions with 86.3% accuracy

Supervised DL reduces online computational load

Comparison of DL architectures for resource allocation

Abstract

Increased complexity and heterogeneity of emerging 5G and beyond 5G (B5G) wireless networks will require a paradigm shift from traditional resource allocation mechanisms. Deep learning (DL) is a powerful tool where a multi-layer neural network can be trained to model a resource management algorithm using network data.Therefore, resource allocation decisions can be obtained without intensive online computations which would be required otherwise for the solution of resource allocation problems. In this context, this article focuses on the application of DL to obtain solutions for the radio resource allocation problems in multi-cell networks. Starting with a brief overview of a deep neural network (DNN) as a DL model, relevant DNN architectures and the data training procedure, we provide an overview of existing state-of-the-art applying DL in the context of radio resource allocation. A qualitative comparison is provided in terms of their objectives, inputs/outputs, learning and data training methods. Then, we present a supervised DL model to solve the sub-band and power allocation problem in a multi-cell network. Using the data generated by a genetic algorithm, we first train the model and then test the accuracy of the proposed model in predicting the resource allocation solutions. Simulation results show that the trained DL model is able to provide the desired optimal solution 86.3% of time.