面向物联网系统远程估计的信息瓶颈启发式基于类型的多址接入
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
AI总结：
针对现有基于类型的多址接入协议需已知分布信息的问题，提出基于信息瓶颈的IB-TBMA及压缩版CIB-TBMA，联合优化码本与神经网络解码器，仅靠数据适配统计特性，验证了联合设计与码本压缩的有效性。
摘要：
基于类型的多址接入（TBMA）是一种面向远程推理的语义感知多址协议。在TBMA中，码字在多个发射传感器间复用，每个码字被分配给不同的观测值。现有TBMA协议基于固定共享码本，以及常规的最大似然或贝叶斯解码器，这类解码器需要掌握观测和信道的分布信息。在这篇论文中，我们提出了一种基于信息瓶颈（IB）的TBMA新型设计原则。在提出的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，从而仅依靠数据就能适配源、观测及信道统计特性。我们还提出了压缩IB-TBMA（CIB-TBMA）协议，该协议通过一个受IB启发的聚类阶段减少码字数量，对IB-TBMA进行了改进。数值结果证明了码本与神经解码器联合设计的重要性，并验证了码本压缩的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
