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.

翻译：获取并利用精确的信道状态信息（CSI）可显著提升传输性能，因此在发挥大规模多输入多输出（MIMO）技术的潜在优势中扮演着关键角色。当前主流的CSI反馈方法通过采用先进深度学习技术学习代表性CSI特征以进行后续压缩处理，从而提升精度。与以往工作不同，我们将CSI压缩问题置于隐式神经表示的框架下处理。具体而言，每个CSI矩阵被视为一个将CSI坐标（天线编号与子信道）映射至对应信道增益的神经函数。我们并非直接传输隐式神经函数的参数，而是传输基于元学习算法从CSI矩阵导出的调制信息。这些调制信息随后被应用于共享基础网络，以生成CSI矩阵的元素。与CSI矩阵对应的调制信息经过量化和熵编码进一步降低通信带宽，从而实现极端CSI压缩比。数值结果表明，我们提出的方法达到了最佳性能，并展现出在反馈策略上的灵活性。