Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems

Meiyi Zhu, Chunyan Feng, Caili Guo, Nan Jiang, Osvaldo Simeone

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Type-basedmultiple 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.

Original languageEnglish (US)
Pages (from-to)403-407
Number of pages5
JournalIEEE Signal Processing Letters
Volume30
DOIs
StatePublished - 2023

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Type-based multiple access
  • information bottleneck
  • machine learning
  • semantic communi cation

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