Hybrid Evolutionary-Based Sparse Channel Estimation for IRS-Assisted mmWave MIMO Systems

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize practice due passive elements of without signal processing capabilities. In this paper, we propose compressive estimation techniques IRS-assisted mmWave multi-input multi-output (MIMO) system. To reduce training overhead, inherent sparsity channels exploited. By utilizing properties Kronecker products, converted into sparse recovery problem, involves two competing cost function terms (measurement error term). Existing algorithms solve combined contradictory objectives using regularization parameter, leads suboptimal solution. address concern, hybrid multiobjective evolutionary paradigm developed can overcome difficulty choice parameter value. Simulation results show that under wide range simulation settings, proposed method achieves competitive compared existing methods.