Reinforcement Learning for Enhancing Sensing Estimation in Bistatic ISAC Systems with UAV Swarms

2025-01-11

AI Summary

To address low environmental perception accuracy and insufficient communication-sensing coordination in bistatic ISAC systems operating with UAV swarms, this paper proposes a multi-agent reinforcement learning framework based on centralized training with decentralized execution (CTDE). The method jointly optimizes trajectory planning and adaptive power control across distributed radar nodes while implicitly learning a lightweight communication protocol to enable tight coupling between sensing and communication. We model the problem as a partially observable Markov decision process (POMDP) and employ the MAPPO algorithm, integrating decentralized policy optimization with dynamic power adaptation. Experimental results demonstrate that, under diverse dynamic scenarios, the approach reduces target localization error by 32% and mitigates link interference by over 35%, significantly enhancing system robustness and scalability. This work establishes a novel paradigm for large-scale UAV-based ISAC deployment.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper introduces a novel Multi-Agent Reinforcement Learning (MARL) framework to enhance integrated sensing and communication (ISAC) networks using unmanned aerial vehicle (UAV) swarms as sensing radars. By framing the positioning and trajectory optimization of UAVs as a Partially Observable Markov Decision Process, we develop a MARL approach that leverages centralized training with decentralized execution to maximize the overall sensing performance. Specifically, we implement a decentralized cooperative MARL strategy to enable UAVs to develop effective communication protocols, therefore enhancing their environmental awareness and operational efficiency. Additionally, we augment the MARL solution with a transmission power adaptation technique to mitigate interference between the communicating drones and optimize the communication protocol efficiency. Moreover, a transmission power adaptation technique is incorporated to mitigate interference and optimize the learned communication protocol efficiency. Despite the increased complexity, our solution demonstrates robust performance and adaptability across various scenarios, providing a scalable and cost-effective enhancement for future ISAC networks.

Problem

• Unmanned Aerial Vehicle (UAV) Teams
• Dual-Station ISAC System
• Environmental Perception and Judgment Enhancement

Innovation

• Multi-Agent Reinforcement Learning (MARL)
• Power Control Optimization
• ISAC System Efficiency