Performance Comparison of Downlink Channel Estimation in FDD Massive MIMO using CS-Aided and Bayesian Compressed Sensing Methods for 5G Systems
T.Ravibabu1, C. Dharmaraj2 

1T.Ravi Babu, Research Scholar, Department of ECE, GITAM Institute of Technology, (GIT) GITAM, Andhra Pradesh Visakhapatnam, India.
2C.Dharmaraj, Professor, Department of ECE, GITAM Institute of Technology, (GIT) GITAM, Andhra Pradesh Visakhapatnam, India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 10 June 2019 | Manuscript published on 30 June 2019 | PP: 97-101 | Volume-8 Issue-8, June 2019 | Retrieval Number: G5995058719/19©BEIESP
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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.
Keyword: Bayesian Learning, Compressed sensing, Channel estimation, Frequency Division Duplex, Gaussian Mixture, Massive MIMO.
Scope of the Article: Cyber Physical Systems (CPS)