Rate-Limited Closed-Loop Distributed ISAC Systems: An Autoencoder Approach
🤖 AI Summary
To address the low transmission efficiency of high-dimensional observation data and the resulting limitations on estimation and control performance in rate-constrained closed-loop distributed multi-sensor Integrated Sensing and Communication (ISAC) systems, this paper proposes a deep autoencoder-based joint sensing-compression-control optimization framework. First, we establish a general modeling methodology for rate-constrained closed-loop distributed ISAC. Second, we theoretically characterize the coupled impact of compression rate, observation dimensionality, and state dimensionality on both estimation error and control cost. Third, we introduce a noise-aware, multi-stage dynamic bit allocation mechanism. Experimental results under Linear Quadratic Regulator (LQR) control demonstrate significant reductions in both state estimation error and control cost. In multi-sensor scenarios, our approach outperforms uniform compression: it prioritizes lossless reconstruction for high-SNR sensors while dynamically enhancing compression quality for high-noise sensors.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Compressing high-dimensional sensor data for rate-limited ISAC systems
Analyzing observation-compression-state interplay in LQR control performance
Optimizing resource allocation among multi-sensors under compression constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
Autoencoder-based compression for rate-limited ISAC
Framework analyzes observation compression effects
Optimal resource allocation prioritizes low-noise sensors
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