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Integrated Swarming Computing Paradigm for Efficient Estimation of Channel Parameters in MIMO System

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Abstract

In this study, optimum channel estimation in MIMO network is investigated by an integrated computing paradigm using Particle Swarm Optimization (PSO) and Nelder Mead Method (NMM). The efficacy of global optimization is exploited through PSO while for fine-tuning of channel coefficients, the strength of NMM as an efficient local optimization technique is utilized. Hybrid swarm intelligence framework is applied for square channel matrix having identical array elements at both transmitter and receiver end. Different signal–noise ratios are applied for analyzing the response of complex received signal from flat fading Additive White Gaussian Noise channel. Numerical simulations are done for evaluating Mean Square Error among true and estimated channel coefficients. Performance analysis is conducted not only for a single run of proposed hybrid swarm intelligence but also on extensive simulations on multiple independent trials to prove the worth of the scheme.

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Funding

This research is supported in part by the National Natural Science Foundation of China (NSFC) Grant Nos. 11574250 and 11874302.

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Wasiq Ali & Yaan Li

  2. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan

    Shujaat Ali Khan Tanoli & Muhammad Asif Zahoor Raja

Authors
  1. Wasiq Ali
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  2. Yaan Li
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  3. Shujaat Ali Khan Tanoli
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  4. Muhammad Asif Zahoor Raja
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Correspondence to Wasiq Ali.

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Ali, W., Li, Y., Tanoli, S.A.K. et al. Integrated Swarming Computing Paradigm for Efficient Estimation of Channel Parameters in MIMO System. Wireless Pers Commun 115, 77–102 (2020). https://doi.org/10.1007/s11277-020-07562-1

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  • DOI: https://doi.org/10.1007/s11277-020-07562-1

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