发表机构
Auburn University（奥本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对显著性驱动的跨层语义通信设计问题，提出Meta-VIB收发器与MA-RMAB信道分配方法，在行人安全数据集实验中，于0 dB、5 dB平均信噪比下分别实现最高1000倍、40倍的语义频谱效率增益。
AI 中文摘要
本文研究显著性驱动的跨层语义通信设计问题。基于统计决策理论，我们引入了一种样本级数据显著性的信息论度量，用于量化每个单独观测值的特定任务价值。利用该度量，我们构建了一个跨层优化问题，同时优化：（i）物理层语义编码与推理，（ii）MAC层资源分配，目标是最大化语义频谱效率，其定义为单位带宽、单位时间内传递的语义价值。在物理层，我们开发了一种新型语义收发器——元学习变分信息瓶颈（Meta-Learning Variational Information Bottleneck，Meta-VIB），它采用元学习得到的超网络将高维观测值压缩成语义显著的隐变量，无需在线重新训练即可即时适应动态信道条件和变化的符号预算。在MAC层，我们将信道分配建模为多动作 restless 多臂老虎机（Multi-Action Restless Multi-Armed Bandit，MA-RMAB），并采用Q最大化算法，该算法根据传感器信息的语义价值动态分配信道资源。在真实世界的行人安全数据集上的实验表明，我们的联合设计相较于基准方法在语义频谱效率上实现了显著提升，在平均信噪比为0 dB时达到了高达1000倍的增益，在平均信噪比为5 dB时达到了40倍的增益。
英文摘要
In this paper, we study a significance-driven cross- layer semantic communication design problem. Based on sta- tistical decision theory, we introduce an information-theoretic measure of per-sample data significance that quantifies the task-specific value of each individual observation. Using this metric, we formulate a cross-layer optimization problem that simultaneously optimizes (i) physical-layer semantic encoding and inference and (ii) MAC-layer resource allocation, with the objective of maximizing semantic spectrum efficiency, defined as the semantic value delivered per unit bandwidth per unit time. At the physical layer, we develop Meta-Learning Variational Information Bottleneck (Meta-VIB), a new semantic transceiver that employs a meta-learned hypernetwork to compress high- dimensional observations into semantically significant latents, enabling instantaneous adaptation to dynamic channel conditions and varying symbol budgets without online retraining. At the MAC layer, we model channel allocation as a Multi-Action Restless Multi-Armed Bandit (MA-RMAB) and adopt the Q- Maximization algorithm, which dynamically allocates channel resources to sensors based on their semantic value of information. Experimental results on a real-world pedestrian safety dataset demonstrate that our joint design achieves substantial gains in semantic spectrum efficiency over baselines, reaching up to 1000 times gain at an average SNR of 0 dB and 40 times gain at an average SNR of 5 dB.
