Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Abstract
Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword being assigned to a different observation value. Existing TBMA protocols are based on fixed shared codebooks and on conventional maximum-likelihood or Bayesian decoders, which require knowledge of the distributions of observations and channels. In this letter, we propose a novel design principle for TBMA based on the information bottleneck (IB). In the proposed IB-TBMA protocol, the shared codebook is jointly optimized with a decoder based on artificial neural networks (ANNs), so as to adapt to source, observations, and channel statistics based on data only. We also introduce the Compressed IB-TBMA (CIB-TBMA) protocol, which improves IB-TBMA by enabling a reduction in the number of codewords via an IB-inspired clustering phase. Numerical results demonstrate the importance of a joint design of codebook and neural decoder, and validate the benefits of codebook compression.
Cite
@article{arxiv.2212.09337,
title = {Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems},
author = {Meiyi Zhu and Chunyan Feng and Caili Guo and Nan Jiang and Osvaldo Simeone},
journal= {arXiv preprint arXiv:2212.09337},
year = {2023}
}
Comments
5 pages, 3 figures, accepted by IEEE Signal Processing Letters (SPL)