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 language | English (US) |
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Pages (from-to) | 403-407 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 30 |
DOIs | |
State | Published - 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