Least Mean Squares Based Kalman Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems

Millimeter wave massive multiple-input and multiple-output (MIMO) systems support several antennas at the receiver and transmitter sides to serve several users in a communication system. Due to its several antennas, the system cost is very high and faces challenges such as blockage, path loss, and interference. To solve the above interference problem, beamforming technology and LMS Kalman filter-based Hybrid Precoding are used in millimeter wave massive MIMO system by directing the signal in the desired direction and to get a reduced bit error rate. Therefore, proposed hybrid precoding aims to leverage the advantages of both digital and analog beamforming while significantly reducing the number of RF chains compared to the number of antennas, all while maintaining the benefits of spatial multiplexing. This paper compares the performance of various precoding techniques, including Analog-only Beamsteering, Zero Forcing (ZF), Minimum Mean Square Error (MMSE), Kalman, Minimum Square Error Fully digital (MSE Fully digital), and Proposed Least Mean Squares (LMS) Kalman Hybrid Precoding, using performance metric parameters such as spectral efficiency, energy efficiency, bit error rate, and Information rate. The LMS Kalman Hybrid Precoding improved the spectral efficiency by almost 6.228 bps/Hz at 10 dB with ten channel paths concerning the Analog-only Beamsteering and almost 3.748, 2.974, 2.758, and 0.399 bps/Hz for the Zero Forcing (ZF) Hybrid Precoding, MMSE, Kalman, and MSE Fully Digital Hybrid Precoding techniques, respectively, which shows the superiority of the Proposed Least Mean Squares (LMS) Kalman Hybrid Precoding.