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

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. [ABSTRACT FROM AUTHOR]