In wireless communication Multiple Input Multiple Output (MIMO) technology has brought significant improvement in service by adopting Orthogonal Frequency Division Multiplexing (OFDM), a digital modulation technique. To achieve great performance with MIMO efficiently gathering channel state information (CSI) plays a vital role. Among different approach of channel estimation techniques data-aided channel estimation is more reliable. The existing methods of data-aided channel estimation are Least Square (LS) and Minimum Mean Square Error (MMSE) methods which do not achieve a great performance. Moreover, MMSE is little complex and has higher computational cost. That is why many attempts have been done previously to optimize the methods with help of meta heuristics and also other ways. In this paper we have tried to optimize LS estimation with a combined algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed algorithm has outperformed LS and MMSE. And it gives similar result if we optimize LS with standard PSO but in less numbers of iteration.

翻译：在无线通信中，多输入多输出（MIMO）技术通过采用正交频分复用（OFDM）数字调制方式显著提升了服务质量。为实现MIMO的优异性能，高效获取信道状态信息（CSI）至关重要。在多种信道估计方法中，数据辅助信道估计具有更高的可靠性。现有数据辅助信道估计方法（如最小二乘LS和最小均方误差MMSE）性能表现有限，且MMSE算法复杂度较高、计算代价更大。因此，先前已有大量研究尝试通过元启发式算法及其他途径优化这些方法。本文提出一种融合遗传算法（GA）与粒子群优化（PSO）的混合算法对LS估计进行优化。实验表明，该算法性能优于LS和MMSE方法，且在与标准PSO优化LS方法取得相似结果时，所需的迭代次数更少。