Performance Enhancement of MmWave MIMO Systems Using Deep Learning Framework

• Published in: IEEE access Vol. 9; p. 1
• Main Authors: Faragallah, Osama S., El-sayed, Hala S., El-Mashed, Mohamed G.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antennas; Array signal processing; Beamforming; Covariance matrix; Decoder; Decoders; Deep learning; Digital precoders; Hardware; HybridPrecodingNet; Millimeter waves; mmWave; MmWave and Spectral Efficiency; Neural networks; Optimization; Performance enhancement; Precoding; Radio frequency; Spectral efficiency
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3092709

In order to obtain beamforming gains and prevent high pathloss in millimeter wave (mmWave) systems, large number of antennas is employed. Digital precoders are difficult to implement with many antennas because of hardware constraints, while analog precoders have limited performance. In this paper, hybrid precoding based on a deep learning framework, HybridPrecodingNet, is proposed, which uses large-scale information to predict the parameters of hybrid precoders and decoders. The statistics of the channel covariance matrix are applied to design the hybrid precoders and decoders. The proposed HybridPrecodingNet at the receiver is applied for the channel estimation and design of hybrid decoders. In our proposed framework, the structure of HybridPrecodingNet is trained to learn how to optimize the hybrid precoder and decoder for maximum spectral efficiency. Comparison between different precoding techniques is provided. Results show that HybridPrecodingNet approaches the sub-optimal solution and gives significant spectral efficiency enhancement.