GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks

Lingwei Xu,Han Wang,Wen Lin,Thomas Aaron Gulliver,Khoa N Le

doi:10.1109/access.2019.2948475

Abstract

The complexity and variability of wireless channels makes reliable mobile multiuser communications challenging. As a consequence, research on mobile multiuser communication networks has increased significantly in recent years. The outage probability (OP) is commonly employed to evaluate the performance of these networks. In this paper, exact closed-form OP expressions are derived and an OP prediction algorithm is presented. Monte-Carlo simulation is used to evaluate the OP performance and verify the analysis. Then, a grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is proposed. Theoretical results are used to generate training data. We also examine the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), BP neural network, and wavelet neural network methods. Compared to the wavelet neural network, LWLR, SVM, BP, and ELM methods, the results obtained show that the GWO-BP method provides the best OP performance prediction.

Highlights

The explosive growth in the number of mobile users has motivated research on fifth generation (5G) mobile communication systems [1]–[5]
Cooperative communications is widely used in 5G mobile multiuser communication systems [8]–[11]
Simulation is used to evaluate the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), wavelet neural network, grey wolf optimization back-propagation (GWO-BP) neural network, and BP neural network methods. These results show that compared to the LWLR, SVM, BP, wavelet neural network, and ELM methods, the GWO-BP method provides the best outage probability (OP) performance prediction results

Summary

INTRODUCTION

The explosive growth in the number of mobile users has motivated research on fifth generation (5G) mobile communication systems [1]–[5]. Rayleigh and Nakagami-m fading channels were considered in [8]–[17] These channel models may not be suitable to characterize the complexities of practical mobile communication systems. L. Xu et al.: GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks. The outage probability (OP) can be used to characterize the performance of mobile multiuser communication systems over N-Nakagami fading channels. OP performance prediction of mobile multiuser communication systems has not been considered. 1. The OP performance of mobile multiuser communication networks is investigated considering transmit antenna selection (TAS). A grey wolf optimization back-propagation (GWO-BP) neural network based OP performance prediction algorithm is presented.

THE OP OF TAS SCHEME II

OP PERFORMANCE PREDICTION BASED ON GWO-BP NEURAL NETWORK

PERFORMANCE RESULTS

CONCLUSION