%C Athens, Greece
%J IEEE International Symposium on Information Theory - Proceedings
%B IEEE International Symposium on Information Theory - Proceedings
%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.
%A Y Song
%A K Wan
%A Z Liao
%A H Xu
%A G Caire
%A S Shamai
%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
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%D 2024
%L discovery10198390
%I IEEE