Deep Learning for Parametric Channel Estimation in Massive MIMO Systems

Zia, Muhammad Umer, Xiang, Wei, Vitetta, G.M., and Huang, Tao (2022) Deep Learning for Parametric Channel Estimation in Massive MIMO Systems. IEEE Transactions on Vehicular Technology. (In Press)

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Massive Multiple-Input Multiple-Output (MIMO) communication with a low bit error rate depends upon the availability of accurate Channel State Information (CSI) at the base station. The massive MIMO systems can be either deployed using time division duplexing with channel reciprocity assumption or by availing frequency division duplexing, which requires closed-loop feedback for CSI acquisition. The channel reciprocity simplifies transmission in time division duplexing; however, it suffers a bottleneck due to pilot contamination, whereas transmission in frequency division duplexing is challenged by channel estimation complexity, CSI feedback, and overall delay in CSI transfer. This paper proposes a simplified parametric channel model, its deep neural network aided estimation along with pilot decontamination for time division duplexing and a low rate parametric feedback and improved precoding for frequency division duplexing based massive MIMO systems. This novel framework integrates the massive MIMO parametric estimation and deep learning for improved estimation and precoding. Our proposed model also offers a unified approach for CSI acquisition with a performance bound on channel correlation in fast time-varying conditions. A theoretical model has been presented using Gaussian assumptions and validated by Monte-Carlo simulations. The results show total nullification of pilot contamination and high-performance gains when the proposed technique is employed for estimation.

Item ID: 77047
Item Type: Article (Research - C1)
ISSN: 1939-9359
Keywords: Massive MIMO, Channel estimation, Estimation, Deep learning, Precoding, Decontamination, Contamination
Copyright Information: © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Funders: Australian Research Council (ARC)
Projects and Grants: ARC Discovery Projects Funding Scheme (Grant Number: DP220101634)
Date Deposited: 14 Dec 2022 02:27
FoR Codes: 40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 70%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 30%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220107 Wireless technologies, networks and services @ 100%
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