A hybrid meta-heuristic approach for channel estimation in OFDM MIMO

TL;DR

This paper proposes a hybrid meta-heuristic algorithm combining GA and PSO to optimize LS channel estimation in OFDM MIMO systems, outperforming traditional LS and MMSE methods with fewer iterations.

Contribution

A novel hybrid GA-PSO algorithm for data-aided channel estimation in OFDM MIMO systems, reducing computational complexity and improving accuracy.

Findings

Outperforms LS and MMSE in channel estimation accuracy.

Achieves similar results to standard PSO with fewer iterations.

Reduces computational complexity of channel estimation.

Abstract

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.