Semantic Communication for Rate-Limited Closed-Loop Distributed Communication-Sensing-Control Systems

2025-12-22

AI Summary

In bandwidth-constrained distributed sensing-communication-control closed-loop systems, conventional approaches fail to jointly optimize semantic fidelity across perception, communication, and control layers. Method: This paper proposes a semantics-aware communication framework grounded in Weaver’s three-level model, decomposing semantic error minimization into three hierarchical objectives: L1 (observation reconstruction), L2 (state estimation), and L3 (control execution). It introduces a unified, goal-oriented semantic compression and rate-adaptive framework spanning the entire sensing–communication–control loop, supporting multi-level semantic error definitions and heterogeneous optimization. The method employs a GRU-based semantic encoder, a PPO-driven dynamic bit-rate allocation algorithm, and multi-sensor LQR control modeling under bandwidth constraints. Contribution/Results: Experiments demonstrate significant performance gains across all three layers under stringent bandwidth limits, validating the effectiveness and generalizability of co-designing semantic compression with resource allocation strategies.

Technology Category

• Multiagent Systems: Agent Communication
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The growing integration of distributed integrated sensing and communication (ISAC) with closed-loop control in intelligent networks demands efficient information transmission under stringent bandwidth constraints. To address this challenge, this paper proposes a unified framework for goal-oriented semantic communication in distributed SCC systems. Building upon Weaver's three-level model, we establish a hierarchical semantic formulation with three error levels (L1: observation reconstruction, L2: state estimation, and L3: control) to jointly optimize their corresponding objectives. Based on this formulation, we propose a unified goal-oriented semantic compression and rate adaptation framework that is applicable to different semantic error levels and optimization goals across the SCC loop. A rate-limited multi-sensor LQR system is used as a case study to validate the proposed framework. We employ a GRU-based AE for semantic compression and a PPO-based rate adaptation algorithm that dynamically allocates transmission rates across sensors. Results show that the proposed framework effectively captures task-relevant semantics and adapts its resource allocation strategies across different semantic levels, thereby achieving level-specific performance gains under bandwidth constraints.

Problem

• Optimizes semantic communication for distributed sensing-control systems
• Addresses bandwidth constraints via hierarchical semantic error modeling
• Proposes adaptive rate allocation for multi-sensor LQR systems

Innovation

• Hierarchical semantic formulation with three error levels
• Unified goal-oriented semantic compression and rate adaptation framework
• GRU-based AE for compression and PPO-based rate adaptation algorithm