In closed-loop distributed multi-sensor integrated sensing and communication (ISAC) systems, performance often hinges on transmitting high-dimensional sensor observations over rate-limited networks. In this paper, we first present a general framework for rate-limited closed-loop distributed ISAC systems, and then propose an autoencoder-based observation compression method to overcome the constraints imposed by limited transmission capacity. Building on this framework, we conduct a case study using a closed-loop linear quadratic regulator (LQR) system to analyze how the interplay among observation, compression, and state dimensions affects reconstruction accuracy, state estimation error, and control performance. In multi-sensor scenarios, our results further show that optimal resource allocation initially prioritizes low-noise sensors until the compression becomes lossless, after which resources are reallocated to high-noise sensors.

翻译：在闭环分布式多传感器集成感知与通信（ISAC）系统中，性能往往取决于通过速率受限网络传输高维传感器观测数据。本文首先提出一个速率受限闭环分布式ISAC系统的通用框架，随后提出一种基于自动编码器的观测压缩方法，以克服有限传输容量带来的约束。基于该框架，我们通过闭环线性二次调节器（LQR）系统进行案例研究，分析观测维度、压缩维度与状态维度之间的相互作用如何影响重构精度、状态估计误差与控制性能。在多传感器场景中，我们的研究进一步表明：最优资源分配策略会优先分配给低噪声传感器，直至压缩过程实现无损传输，随后将资源重新分配给高噪声传感器。