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Although the frequency-division duplex (FDD) massive multiple-inputmultiple-output (MIMO) system can offer high spectral and energy efficiency, itrequires to feedback the downlink channel state information (CSI) from users tothe 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 makesthe CSI feedback very challenging, and it is urgent to compress the feedbackCSI. To this end, this paper proposes a novel dilated convolution based CSIfeedback network, namely DCRNet. Specifically, the dilated convolutions areused to enhance the receptive field (RF) of the proposed DCRNet withoutincreasing the convolution size. Moreover, advanced encoder and decoder blocksare designed to improve the reconstruction performance and reduce computationalcomplexity as well. Numerical results are presented to show the superiority ofthe proposed DCRNet over the conventional networks. In particular, the proposedDCRNet can achieve almost the state-of-the-arts (SOTA) performance with muchlower floating point operations (FLOPs). The open source code and checkpoint ofthis work are available at https://github.com/recusant7/DCRNet.

Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems