We propose novel compression algorithms to time-varying channel state information (CSI) for 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 vector. Our algorithm chooses a suitable compander in an intuitively simple way whenever empirical data are available. Then, we compress the quantised index sequences 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.

翻译：我们提出新的压缩算法,用于无线通信的时间变化频道状态信息(CSI) 。 拟议的方案结合了( 损失) 矢量量化和( 损失) 压缩。 首先, 新的矢量量化技术基于对正常矢量的每个组成部分应用的一类假相折算方计算器。 我们的算法选择了在有经验数据时以直观的简单方式进行适当的折算器。 然后, 我们使用基于上下文的树本方法压缩量化的指数序列。 基本上, 我们更新了每个瞬间源有条件分布的估计数, 并将当前符号编码为估计分布。 算法的复杂度低, 空间维度和时间长都是线性时间, 并且可以在线方式实施。 我们进行模拟, 以展示在这种情景下拟议的算法的有效性 。