Adaptive and Sparse Adaptive mmWave Massive MIMO Channel Learning

Abstract

¹ Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India.

shailsulakh076@gmail.com

² Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India

shelej.22390@lpu.co.in

 ³ Chandigarh College of Engineering, Chandigarh Group of Colleges, Jhanjeri, Mohali, Punjab, India

sajjantech@gmail.com

*Corresponding Author: Shailender

*Email: shailsulakh076@gmail.com

The study aims to advance Massive Multiple-Input Multiple-Output (MIMO) technology, which has become an essential component of next-generation wireless communications, by utilising the millimetre wave (mmWave) frequency. The efficient estimation and learning of channels are, however, still a challenge due to the high dimensionality and sparse nature of mmWave channels. A novel set of adaptive and sparse adaptive algorithms for channel learning in massive MIMO systems that operate at the mmWave frequency. The proposed methods enhance the precision and efficacy of channel estimation by utilising the intrinsic sparsity of mmWave channels and dynamically adapting to the evolving channel conditions. An adaptive channel learning algorithm that considers the time-varying character of mmWave channels iteratively refines the estimation process. It decreases the overhead of standard estimation methods while preserving accuracy. In addition, utilize compressed sensing techniques to create a sparse adaptive learning algorithm that optimizes the estimation process by identifying and prioritizing the most significant channel paths. The algorithms that have been proposed are remarkably well-suited for real-time implementation in mmWave massive MIMO systems, as evidenced by the simulation results. This is due to the fact that they outperform current methods in terms of estimate accuracy and processing complexity. The development of more dependable and efficient mmWave communications, particularly in high-mobility and dynamic environments, is facilitated by these developments in channel learning.