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Robust Multi-user Detection Based on Hybrid Grey Wolf Optimization

  • Yuanfa Ji
  • Zhuo Fan
  • Xiyan SunEmail author
  • Shouhua Wang
  • Suqing Yan
  • Sunyong Wu
  • Qiang Fu
  • Kamarul Hawari Ghazali
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 810)

Abstract

The search for an effective nature-inspired optimization technique has certainly continued for decades. In this paper, a novel hybrid Grey wolf optimization and differential evolution algorithm robust multi-user detection algorithm is proposed to overcome the problem of high bit error rate (BER) in multi-user detection under impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than that of genetic algorithm, differential evolution algorithm and Grey wolf optimization algorithm, and has the lower BER.

Keywords

Grey wolf optimization algorithm Differential evolution algorithm Hybrid optimization Multi-user detection Impulse noise 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61561016, 11603041), Innovation Project of GUET Graduate Education (2018YJCX19), Guangxi Information Science Experiment Center funded project, Department of Science and Technology of Guangxi Zhuang Autonomous Region (AC16380014, AA17202048, AA17202033).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuanfa Ji
    • 1
  • Zhuo Fan
    • 1
  • Xiyan Sun
    • 1
    Email author
  • Shouhua Wang
    • 1
  • Suqing Yan
    • 1
  • Sunyong Wu
    • 1
  • Qiang Fu
    • 1
  • Kamarul Hawari Ghazali
    • 2
  1. 1.Guangxi Key Laboratory of Precision Navigation Technology and ApplicationGuilin University of Electronic TechnologyGuilinChina
  2. 2.University Malaysia PahangGambangMalaysia

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