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Deep learning (DL)-based channel state information (CSI) feedback methodscompressed the CSI matrix by exploiting its delay and angle featuresstraightforwardly, while the measure in terms of information contained in theCSI matrix has rarely been considered. Based on this observation, we introduceself-information as an informative CSI representation from the perspective ofinformation theory, which reflects the amount of information of the originalCSI matrix in an explicit way. Then, a novel DL-based network is proposed fortemporal CSI compression in the self-information domain, namely SD-CsiNet. Theproposed SD-CsiNet projects the raw CSI onto a self-information matrix in thenewly-defined self-information domain, extracts both temporal and spatialfeatures of the self-information matrix, and then couples these two featuresfor effective compression. Experimental results verify the effectiveness of theproposed SD-CsiNet by exploiting the self-information of CSI. Particularly forcompression ratios 1/8 and 1/16, the SD-CsiNet respectively achieves 7.17 dBand 3.68 dB performance gains compared to state-of-the-art methods.

Self-information Domain-based Neural CSI Compression with Feature Coupling