Dilated Convolution Based CSI Feedback Compression for Massive MIMO Systems

doi:10.1109/TVT.2022.3183596

Dilated Convolution Based CSI Feedback Compression for Massive MIMO Systems

2022-10-01

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DOI：10.1109/TVT.2022.3183596

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Although the frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system can offer high spectral and energy efficiency, it requires to feedback the downlink channel state information (CSI) from users to the base station (BS), in order to fulfill the precoding design at the BS. However, the large dimension of CSI matrices in the massive MIMO system makes the CSI feedback very challenging, and it is urgent to compress the feedback CSI. To this end, this paper proposes a novel dilated convolution based CSI feedback network, namely Dilated Channel Reconstruction Network (DCRNet). Specifically, the dilated convolutions are used to enhance the receptive field (RecF) of the proposed DCRNet without increasing the convolution size. Moreover, advanced encoder and decoder blocks are designed to improve the reconstruction performance and reduce computational complexity as well. Numerical results are presented to show the superiority of the proposed DCRNet over the conventional networks. In particular, compared to the state-of-the-arts (SOTA) networks, the proposed DCRNet can achieve almost the same performance while reduce floating point operations (FLOPs) by about 30%.

Keywords:

Convolution

Decoding

Massive MIMO

Feature extraction

Sparse matrices

Delays

Precoding

CSI feedback

deep learning

dilated convolutions

massive MIMO

Dilated Convolution Based CSI Feedback Compression for Massive MIMO Systems

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