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

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)152690-152700
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Open Access - Access Right Statement

This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/

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