Performance Enhancement of MmWave MIMO Systems Using Deep Learning Framework

Osama S Faragallah,Mohamed G El-Mashed,Hala S El-Sayed

doi:10.1109/access.2021.3092709

Osama S Faragallah, Mohamed G El-Mashed + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3092709

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Abstract

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.

Highlights

MmWave massive communication system is one of the fifth-generation (5G) technologies
The results indicated that the deep network-based approach minimizes the ratio of bit error and enhances the mmWave massive multiple input multiple output (MIMO) spectrum efficiency, with better performance and lower computational complexity
The design of hybrid precoding based on the deep learning framework has been structured

Summary

Introduction

MmWave massive communication system is one of the fifth-generation (5G) technologies. It can give multiple gigabits per second and can achieve high data rates for devices. It has many applications in high communications mission, business, wireless fusion networks, and processing internet of things (IoT) [1]–[5]. MmWave systems employ antenna arrays at the mobile stations (MSs) and the base station (BS), which can extend the distance of communication links and can give high-quality links. The hardware defects of the radio frequency stage in mmWave communication systems can affect the signal transmission performance.

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Conclusion