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

arXiv: https://arxiv.org/abs/2212.09337 · HTML: https://mlsanity.com/paper/2212.09337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2022.

## Built on by (most cited first, 1 of 1)

- [Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction](https://mlsanity.com/paper/2308.04237/) (2023)

## Abstract (arXiv)

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.