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

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