We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation technique is based on a class of parametrised companders applied on each component of the normalised CSI vector. Our algorithm chooses a suitable compander in an intuitively simple way whenever empirical data are available. Then, the sequences of quantisation indices are compressed using a context-tree-based approach. Essentially, we update the estimate of the conditional distribution of the source at each instant and encode the current symbol with the estimated distribution. The algorithms have low complexity, are linear-time in both the spatial dimension and time duration, and can be implemented in an online fashion. We run simulations to demonstrate the effectiveness of the proposed algorithms in such scenarios.

中文翻译：

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基于上下文树的有损压缩及其在CSI表示中的应用


我们提出了无线通信中时变信道状态信息（CSI）的新颖压缩算法。所提出的方案结合了（有损）矢量量化和（无损）压缩。首先，新的矢量量化技术基于应用于归一化CSI矢量的每个分量的一类参数化压缩扩展器。只要经验数据可用，我们的算法就会以直观简单的方式选择合适的压缩扩展器。然后，使用基于上下文树的方法压缩量化索引序列。本质上，我们在每个时刻更新源的条件分布的估计，并用估计的分布对当前符号进行编码。该算法复杂度低，在空间维度和持续时间上都是线性时间的，并且可以以在线方式实现。我们运行模拟来证明所提出的算法在这种情况下的有效性。