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Massive MIMO CSI Feedback Based on Generative Adversarial Network
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DOI：10.1109/LCOMM.2020.3017188.png)
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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.
Keyword:
Training
Generators
Generative adversarial networks
Gallium nitride
Decoding
Machine learning
Convolutional codes
CSI feedback
compressed sensing
deep learning
generative adversarial network
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4.4
论文数:
1.3W
被引数:
2.2W
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引用论文
Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis基于卷积神经网络的大规模MIMO CSI反馈多速率压缩感知: 设计、仿真与分析
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