  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.
