  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.
