@inproceedings{discovery10198390,
         journal = {IEEE International Symposium on Information Theory - Proceedings},
       publisher = {IEEE},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           pages = {2460--2465},
       booktitle = {IEEE International Symposium on Information Theory - Proceedings},
           title = {An Achievable and Analytic Solution to Information Bottleneck for Gaussian Mixtures},
         address = {Athens, Greece},
           month = {August},
            year = {2024},
            issn = {2157-8095},
        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.},
          author = {Song, Y and Wan, K and Liao, Z and Xu, H and Caire, G and Shamai, S},
             url = {https://doi.org/10.1109/ISIT57864.2024.10619077},
        keywords = {Quantization (signal), 
Closed-form solutions, 
AWGN, 
Phase modulation, 
Source coding, 
Approximation algorithms, 
Binary phase shift keying}
}