Self-information Domain-based Neural CSI Compression with Feature Coupling. (arXiv:2305.07662v1 [cs.IT])

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