Hybrid Beamforming for Dual Functioning MIMO Radar Using EnhancedSocial Ski Drivers Algorithm

Abstract

Inflated communication traffic and expanding radar applications result in frequency congestion and require extra spectrum resources. Dual-functioning multi input multi output radar benefits both sensing and communication operations via real-time cooperation. This decongests the radio frequency environment and allows a single hardware platform for both functionalities. Analog beamforming fails to provide multiplexing gain and digital beamforming is not preferred practically because of heavy power consumption. A reliable hybrid beamforming design is an inherent requirement due to the concerns related to limited RF chains, interference and the cost of fully-digital beamforming. In this paper, Enhanced-Social Ski Driver's algorithm is presented to investigate the performance of hybrid beamforming in dual-functioning MIMO radar. This study investigates location-based direction sensing for Rayleigh and Rician fading channels. This amendment provides improved performance with a minimal bit error rate, maximum gain, and significant normalized array power distribution. The simulation results show 24% reduction in bit error rate, 15% improvement in gain and persistent array power than existing social ski driver's algorithm.