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.

翻译：类型多址接入（TBMA）是一种用于远端推断的语义感知多址接入协议。在TBMA中，码字在发射传感器之间复用，每个码字被分配给不同的观测值。现有的TBMA协议依赖于固定的共享码本以及传统的最大似然或贝叶斯解码器，这些解码器需要已知观测分布和信道分布。本文提出了一种基于信息瓶颈（IB）的TBMA新型设计原则。在所提出的IB-TBMA协议中，共享码本与基于人工神经网络（ANN）的解码器联合优化，从而仅依据数据自适应地适应信源、观测和信道统计特性。我们还引入了压缩IB-TBMA（CIB-TBMA）协议，该协议通过基于IB启发的聚类阶段减少码字数量，从而改进了IB-TBMA。数值结果验证了联合设计码本与神经解码器的重要性，并证明了码本压缩的益处。