Robust multi‐user detection based on hybrid Grey wolf optimization. (1st May 2019)

• Record Type: Journal Article
• Authors: Sun, Xiyan; Fan, Zhuo; Ji, Yuanfa; Wang, Shouhua; Yan, Suqing; Wu, Sunyong; Fu, Qiang; Ghazali, Kamarul Hawari
• Is Part Of: Concurrency and computation. Volume 33:Number 15(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-01
• Subjects: differential evolution algorithm -- Grey wolf optimization algorithm -- hybrid optimization -- multi‐user detection -- impulse noise; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5273
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: The search for an effective nature‐inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi‐user detection algorithm based on Grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi‐user detection under an impulse noise environment. The simulation results show that the iteration times of the multi‐user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, Grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.