Observation Compression in Rate-Limited Closed-Loop Distributed ISAC Systems: From Signal Reconstruction to Control

TL;DR

This paper introduces a framework and autoencoder-based compression method for rate-limited distributed ISAC systems, analyzing how observation compression impacts control and estimation performance in multi-sensor setups.

Contribution

It proposes a novel autoencoder-based observation compression approach within a general framework for rate-limited distributed ISAC systems, and provides analysis on resource allocation and system performance.

Findings

Optimal resource allocation prioritizes low-noise sensors first.

Compression becomes lossless before reallocating resources to high-noise sensors.

The framework links observation, compression, and control performance in ISAC systems.

Abstract

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.