AI 中文总结
研究物理人工智能系统中传输原始传感器数据的问题，提出组合语义通信框架，利用范畴论度量、格罗滕迪克拓扑等，将多设备协调表述为斯塔克尔伯格博弈，用ADMM算法计算策略，实现带宽减少和延迟降低，保持推理准确率。
AI 中文摘要
物理人工智能系统涉及具有嵌入式人工智能模型的分布式传感代理，它们必须在网络环境中进行协调以感知、推理和行动。传输原始传感器数据会带来大量通信开销、延迟和冗余。虽然语义通信通过传输与任务相关的信息缓解了这些挑战，但现有的基于深度学习的联合信源信道编码方法适应性有限、分布外泛化能力差且存在可扩展性挑战。为解决这些限制，本文提出了一种组合语义通信（CSC）框架，使异构物理人工智能源能够传输语义表示，这些表示在基站或边缘服务器处有意义地组合以进行远程推理。首先，开发了一种组合语义的范畴论度量来量化每个设备对推理任务的贡献，超越互信息。其次，格罗滕迪克拓扑和预层形式化了跨设备的语义组合，确保一致性和任务相关性。在此基础上，将多设备协调表述为一个斯塔克尔伯格博弈，其中设备承诺采用编码策略，基站最优地组合接收到的语义表示。一种基于交替方向乘子法（ADMM）的算法计算均衡信令策略。在温和条件下建立了均衡存在性，当组合信息产生递增的集体利益时，该均衡是帕累托最优的。仿真结果表明，所提出的方法在不同自动驾驶场景下保持85%推理准确率的同时，与协作多智能体、分布式梯度下降和均匀选择CSC基线相比，带宽减少高达17%，端到端延迟降低53%。
英文摘要
Physical artificial intelligence (AI) systems involve distributed sensing agents with embedded AI models that must coordinate to perceive, reason, and act in networked environments. Transmitting raw sensor data incurs significant communication overhead, latency, and redundancy. While semantic communication (SC) mitigates these challenges by transmitting task-relevant information, existing deep learning-based joint source-channel coding approaches exhibit limited adaptability, poor out-of-distribution generalization, and scalability challenges. To address these limitations, this paper proposes a framework for compositional semantic communication (CSC), enabling heterogeneous physical AI sources to transmit semantic representations (SRs) that compose meaningfully at a base station (BS) or edge server for remote inference. First, a category-theoretic measure of compositional semantics is developed to quantify each device's contribution to inference tasks beyond mutual information. Second, Grothendieck topologies and presheaves formalize semantic composition across devices, ensuring consistency and task relevance. Building on these foundations, multi-device coordination is formulated as a Stackelberg game in which devices commit to encoding strategies and the BS optimally composes received SRs. An ADMM-based algorithm computes equilibrium signaling strategies. Equilibrium existence is established under mild conditions and is Pareto optimal when compositional information yields increasing collective benefit. Simulation results demonstrate that the proposed approach achieves up to 17% bandwidth reduction and 53% lower end-to-end latency than cooperative multi-agent, distributed gradient descent, and uniform-selection CSC baselines while maintaining 85% inference accuracy across diverse autonomous driving scenarios.
