Context-tree-based CSI compression
What is it?
We propose a novel context-tree-based approach to compressing time-varying CSI for wireless communications. The proposed scheme combines lossy vector quantisation, by means of data-adapted companders, with lossless compression, based on symbol probabilities estimated by a context-tree model.
How does it work?
- Vector quantisation. The new vector quantisation technique is based on a class of parametrised companders applied on each component of the normalised vectors. In particular, we propose the β-law compander, inspired by the beta distribution. Our algorithm optimises the compander parameters whenever empirical data are available.
- Data compression. Then, we compress the sequence of quantisation indices using a context-tree-based approach. Essentially, we build and regularly update the context-tree maximising (CTM) model, and encode the current symbol at each instant with the estimated distribution.
H. K. Miyamoto and S. Yang, “Context-Tree-Based Lossy Compression and Its Application to CSI Representation”, arXiv:2110.14748, 2021.