eprintid: 10198390
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/83/90
datestamp: 2024-10-14 14:57:57
lastmod: 2024-10-14 14:57:57
status_changed: 2024-10-14 14:57:57
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Song, Y
creators_name: Wan, K
creators_name: Liao, Z
creators_name: Xu, H
creators_name: Caire, G
creators_name: Shamai, S
title: An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures
ispublished: pub
divisions: UCL
divisions: B04
divisions: F46
keywords: Quantization (signal), 
Closed-form solutions, 
AWGN, 
Phase modulation, 
Source coding, 
Approximation algorithms, 
Binary phase shift keying
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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.
date: 2024-08-19
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ISIT57864.2024.10619077
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2313001
doi: 10.1109/ISIT57864.2024.10619077
lyricists_name: Xu, Hao
lyricists_id: HXUCX90
actors_name: Xu, Hao
actors_id: HXUCX90
actors_role: owner
full_text_status: public
pres_type: paper
publication: IEEE International Symposium on Information Theory - Proceedings
place_of_pub: Athens, Greece
pagerange: 2460-2465
event_title: 2024 IEEE International Symposium on Information Theory (ISIT)
event_dates: 7 Jul 2024 - 12 Jul 2024
issn: 2157-8095
book_title: IEEE International Symposium on Information Theory - Proceedings
citation:        Song, Y;    Wan, K;    Liao, Z;    Xu, H;    Caire, G;    Shamai, S;      (2024)    An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures.                     In:  IEEE International Symposium on Information Theory - Proceedings.  (pp. pp. 2460-2465).  IEEE: Athens, Greece.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10198390/1/ISIT_2023-YI_version_final_submission.pdf