Advanced 5G Channel Estimation in mmWave MIMO Systems: Leveraging Compressive Sensing for Enhanced Performance

Abstract

Pilot overhead poses a significant challenge in mmWave massive multiple-input multiple-output (MIMO) systems, as it fundamentally limits the accurate acquisition of channel state information (CSI). In this paper, we propose an enhanced adaptive channel estimation technique that leverages compressive sensing (CS) principles to effectively mitigate pilot overhead while maintaining high estimation accuracy. The proposed approach combines compressive sampling matching pursuit (CoSaMP) and sparsity adaptive matching pursuit (SAMP) algorithms, augmented by a novel iterative reweighting strategy and adaptive thresholding mechanism. The simulation results demonstrate that the proposed method achieves superior normalized mean square error (NMSE) performance compared to traditional CS-based techniques. Furthermore, the proposed technique achieves a substantial reduction in computational complexity and pilot overhead compared to traditional channel estimation methods, offering significant improvements in the performance of mmWave MIMO systems.

Keywords

• mmWave MIMO
• compressive sensing (CS)
• channel state information (CSI)
• sparsity adaptive matching pursuit (SAMP)
• normalized mean square error (NMSE)