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

TL;DR

This paper presents a multi-agent reinforcement learning framework that optimizes UAV swarm positioning, trajectory, and communication protocols to improve sensing and communication in integrated ISAC networks.

Contribution

It introduces a novel MARL approach with transmission power adaptation for UAV-based ISAC systems, enhancing sensing accuracy and communication efficiency.

Findings

Robust performance across diverse scenarios

Enhanced environmental awareness of UAVs

Effective interference mitigation techniques

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.