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.

在这项研究中，通过使用粒子群优化（PSO）和Nelder Mead方法（NMM）的集成计算范例研究了MIMO网络中的最佳信道估计。通过PSO利用全局优化的功效，同时为了微调信道系数，利用NMM的强度作为一种有效的局部优化技术。混合群智能框架适用于在发送器和接收器端具有相同阵列元素的方阵矩阵。应用不同的信噪比来分析来自平坦衰落加性高斯白噪声信道的复杂接收信号的响应。进行了数值模拟，以评估真实和估计信道系数之间的均方误差。