%0 Generic
%A Song, Y
%A Wan, K
%A Liao, Z
%A Xu, H
%A Caire, G
%A Shamai, S
%C Athens, Greece
%D 2024
%F discovery:10198390
%I IEEE
%K Quantization (signal),   Closed-form solutions,   AWGN,   Phase modulation,   Source coding,   Approximation algorithms,   Binary phase shift keying
%P 2460-2465
%T An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures
%U https://discovery.ucl.ac.uk/id/eprint/10198390/
%X In this paper, we consider a remote source coding problem with binary phase shift keying (BPSK) modulation sources, where observations are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs further compression to find the optimal trade-off between complexity and relevance. This problem can be formulated as an information bottleneck (IB) problem with Bernoulli sources and Gaussian mixture observations, for which no closed-form solution is known. To address this challenge, we propose a unified achievable scheme that employs three different compression strategies for intermediate node processing, i.e., two-level quantization, multi-level deterministic quantization, and soft quantization with tanh function. Comparative analyses with existing methods, such as the Blahut-Arimoto (BA) algorithm and the Information Dropout approach, are performed through numerical evaluations. The proposed analytic scheme is observed to consistently approach the (numerically) optimal performance over a range of signal-to-noise ratios (SNRs), confirming its effectiveness in the considered setting.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.