Performance comparison of downlink channel estimation in FDD massive MIMO using CS-aided and bayesian compressed sensing methods for 5G systems

Abstract

Future mobile communications involves high data rates across large coverage area, latency, reliability and large number of devices in small area. To achieve this, the systems are categorized based on their abilities and their potential at work such as higher Mobile Bandwidth, Ultra-Reliable Low Latency Communication systems employed with reduced latency. These connections require efficient resources based on time and frequency or Frequency-division duplexing (FDD) systems, and many antennas imply high pilot overhead. For eliminatory this problem, compressed sensing based channel estimation provides a suitable. Moreover, Bayesian method gives overtime in matter of estimation of channel performance for attaining desired achievable rates. The results of simulation have proved the effectiveness of proposed Bayesian compressed sensing based estimation of channel having minimum pilot overhead. Comparison of various techniques compressed with sensing based and traditional LS methods have been presented.