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arXiv · 2004.07576

Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems

Hongyuan Ye·Feifei Gao·Jing Qian·Hao Wang·Geoffrey Ye Li
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Abstract

Channel state information (CSI) feedback is critical for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional algorithms are based on compressive sensing (CS) and are highly dependent on the level of channel sparsity. To address the issue, a recent approach adopts deep learning (DL) to compress CSI into a codeword with low dimensionality, which has shown much better performance than the CS algorithms when feedback link is perfect. In practical scenario, however, there exists various interference and non-linear effect. In this article, we design a DL-based denoise network, called DNNet, to improve the performance of channel feedback. Numerical results show that the DL-based feedback algorithm with the proposed DNNet has superior performance over the existing algorithms, especially at low signal-to-noise ratio (SNR).

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Hongyuan Ye, Feifei Gao, Jing Qian, Hao Wang, Geoffrey Ye Li. 2020-04-16. Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems. https://arxiv.org/abs/2004.07576

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