TY  - GEN
KW  - Quantization (signal)
KW  -  
Closed-form solutions
KW  -  
AWGN
KW  -  
Phase modulation
KW  -  
Source coding
KW  -  
Approximation algorithms
KW  -  
Binary phase shift keying
TI  - An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures
UR  - https://doi.org/10.1109/ISIT57864.2024.10619077
SP  - 2460
EP  - 2465
AV  - public
ID  - discovery10198390
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
SN  - 2157-8095
PB  - IEEE
Y1  - 2024/08/19/
A1  - Song, Y
A1  - Wan, K
A1  - Liao, Z
A1  - Xu, H
A1  - Caire, G
A1  - Shamai, S
CY  - Athens, Greece
N2  - 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.
ER  -