提出一种自编码压缩方法，提升受限带宽下多传感器系统的感知与控制性能。
Observation Compression in Rate-Limited Closed-Loop Distributed ISAC Systems: From Signal Reconstruction to Control
- 用自编码器压缩高维观测数据，适应有限网络传输能力。
- 低噪声传感器优先分配资源，直至压缩无损后转向高噪声传感器。
- 适用于需在带宽受限下实现精准状态估计与控制的分布式系统。
在闭环分布式多传感器集成感知与通信（ISAC）系统中，性能常受限于高维观测数据在有限带宽网络中的传输。本文首先构建了速率受限闭环分布式ISAC系统的通用框架，并提出一种基于自编码器的观测压缩方法以突破传输容量瓶颈。在此框架基础上，通过闭环线性二次调节器（LQR）系统开展案例研究，分析观测、压缩与状态维度之间的相互作用对重建精度、状态估计误差及控制性能的影响。在多传感器场景中，结果表明：最优资源分配策略优先保障低噪声传感器，直至压缩达到无损状态，之后将资源重新分配给高噪声传感器。
原文摘要 · Abstract (English)
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.
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