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

arXiv: https://arxiv.org/abs/2505.01780 · HTML: https://mlsanity.com/paper/2505.01780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 10 of 10)

- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Semantic Communications in Networked Systems: A Data Significance Perspective](https://mlsanity.com/paper/2103.05391/) (2021)
- [Wireless Network Design for Control Systems: A Survey](https://mlsanity.com/paper/1708.07353/) (2017)
- [Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimization](https://mlsanity.com/paper/2211.00229/) (2022)
- [Goal-oriented Semantic Communications for Robotic Waypoint Transmission: The Value and Age of Information Approach](https://mlsanity.com/paper/2312.13182/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Localization in Dynamic Indoor MIMO-OFDM Wireless Systems using Domain Adaptation](https://mlsanity.com/paper/2408.13017/) (2024)
- [Reducing the LQG Cost with Minimal Communication](https://mlsanity.com/paper/2109.12246/) (2021)
- [Integrated Communication, Localization, and Sensing in 6G D-MIMO Networks](https://mlsanity.com/paper/2403.19785/) (2024)
- [Goal-Oriented State Information Compression for Linear Dynamical System Control](https://mlsanity.com/paper/2407.10224/) (2024)

## Abstract (arXiv)

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.