MIMO Channel as a Neural Function: Implicit Neural Representations for Extreme CSI Compression in Massive MIMO Systems
arxiv(2024)
摘要
Acquiring and utilizing accurate channel state information (CSI) can
significantly improve transmission performance, thereby holding a crucial role
in realizing the potential advantages of massive multiple-input multiple-output
(MIMO) technology. Current prevailing CSI feedback approaches improve precision
by employing advanced deep-learning methods to learn representative CSI
features for a subsequent compression process. Diverging from previous works,
we treat the CSI compression problem in the context of implicit neural
representations. Specifically, each CSI matrix is viewed as a neural function
that maps the CSI coordinates (antenna number and subchannel) to the
corresponding channel gains. Instead of transmitting the parameters of the
implicit neural functions directly, we transmit modulations based on the CSI
matrix derived through a meta-learning algorithm. Modulations are then applied
to a shared base network to generate the elements of the CSI matrix.
Modulations corresponding to the CSI matrix are quantized and entropy-coded to
further reduce the communication bandwidth, thus achieving extreme CSI
compression ratios. Numerical results show that our proposed approach achieves
state-of-the-art performance and showcases flexibility in feedback strategies.
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