Context-Tree-Based Lossy Compression and Its Application to CSI Representation

TL;DR

This paper introduces a novel lossy and lossless compression scheme for time-varying CSI in wireless communications, combining vector quantisation with context-tree-based encoding for efficient, online, and low-complexity compression.

Contribution

It presents a new compression algorithm that integrates parametrised companders with context-tree-based encoding, optimized for real-time CSI data compression.

Findings

Effective compression of CSI sequences demonstrated in simulations

Low complexity and linear-time implementation

Suitable for online processing in wireless systems

Abstract

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.