Rate-Limited Closed-Loop Distributed ISAC Systems: An Autoencoder Approach
This work addresses performance bottlenecks in multi-sensor ISAC systems for applications like autonomous vehicles or IoT, but it is incremental as it builds on existing autoencoder and LQR frameworks.
The paper tackles the problem of transmitting high-dimensional sensor observations in rate-limited closed-loop distributed ISAC systems by proposing an autoencoder-based compression method, and results show that optimal resource allocation prioritizes low-noise sensors until compression becomes lossless.
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.