论文标题
信息瓶颈风格的类型的多个访问，用于物联网系统中的远程估计
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
论文作者
论文摘要
基于类型的多重访问（TBMA）是用于远程推断的语义感知的多重访问协议。在TBMA中，跨传感器重复使用编码字，每个代码字被分配给不同的观察值。现有的TBMA协议基于固定的共享代码簿以及常规的最大样本或贝叶斯解码器，这些解码器需要了解观测和渠道的分布。在这封信中，我们根据信息瓶颈（IB）提出了TBMA的新颖设计原理。在提出的IB-TBMA协议中，共享代码本可以与基于人工神经网络（ANN）的解码器共同优化，以适应仅基于数据的源，观察和渠道统计信息。我们还介绍了压缩的IB-TBMA（CIB-TBMA）协议，该协议通过通过IB启发的聚类阶段减少CodeWord的数量来改善IB-TBMA。数值结果证明了代码本和神经解码器的联合设计的重要性，并验证了密码书压缩的好处。
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.
