Massive MIMO CSI Feedback Based on Generative Adversarial Network

IEEE Communications Letters, vol. 24, pp. 2805–2808

Abstract

Massive multiple-input multiple-output (M-MIMO) is one of the main 5G-enabling technologies that promise to increase cell throughput and reduce multiuser interference. However, these abilities rely on exploiting the channel state information (CSI) feedback at base stations (BSs). One critical challenge is that the user equipment (UE) needs to return a large amount of channel information to the base station, creating a large signaling overhead. In this letter, we propose a framework based on deep learning, which is able to efficiently compress and recover the feedback CSI. The encoder learns the most suitable compressed codeword corresponding to the CSI. The decoder decompresses this codeword at the receiving BS end using a Generative Adversarial Network (GAN). A novel objective function is proposed and used to train the Deep Convolutional Generative Adversarial Network (DCGAN) to improve the performance of our proposed framework. Simulation results demonstrate that the proposed framework outperforms traditional compressive sensing-based methods and provides remarkably robust performance for the outdoor channels.

Authors 5

1. Bassant Tolba

   Egypt-Japan University of Science and Technology

   Affiliation as printed

   Egypt–Japan University of Science and Technology (E-JUST), Alexandria, Egypt

2. Maha Mohamed Elsabrouty

   Egypt-Japan University of Science and Technology

   Affiliation as printed

   Egypt–Japan University of Science and Technology (E-JUST), Alexandria, Egypt

3. Mubarak G. Abdu-Aguye

   Ahmadu Bello University

   Affiliation as printed

   Ahmadu Bello University, Zaria, Nigeria

4. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany

5. Hossam Mohamed Kasem

   Tanta University

   Affiliation as printed

   Faculty of Engineering, Tanta University, Tanta, Egypt