论文标题
基于深度学习的DENOISE网络，用于FDD大量MIMO系统中的CSI反馈
Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems
论文作者
论文摘要
通道状态信息（CSI）反馈对于大量多输入多输出（MIMO）系统至关重要。大多数常规算法基于压缩感（CS），并且高度取决于通道稀疏度的水平。为了解决这个问题，最近的一种方法采用了深度学习（DL）将CSI压缩到具有低维度的代码字中，当反馈链接是完美的时，其性能比CS算法更好。但是，在实际情况下，存在各种干扰和非线性效应。在本文中，我们设计了一个基于DL的DeNoise网络，称为DNNET，以提高频道反馈的性能。数值结果表明，基于DL的反馈算法具有所提出的DNNET的性能优于现有算法，尤其是在低信噪比（SNR）时。
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).
