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.
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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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Ji, Y. et al. (2020). Robust Multi-user Detection Based on Hybrid Grey Wolf Optimization. In: Lu, H. (eds) Cognitive Internet of Things: Frameworks, Tools and Applications. ISAIR 2018. Studies in Computational Intelligence, vol 810. Springer, Cham. https://doi.org/10.1007/978-3-030-04946-1_23
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DOI: https://doi.org/10.1007/978-3-030-04946-1_23
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