JCAS-MARL: Joint Communication and Sensing UAV Networks via Resource-Constrained Multi-Agent Reinforcement Learning

TL;DR

This paper presents JCAS-MARL, a multi-agent reinforcement learning framework enabling UAV networks to efficiently coordinate sensing, communication, and energy use for waste hotspot detection, adapting dynamically to environmental conditions.

Contribution

It introduces a resource-aware MARL approach for joint communication and sensing in UAV networks, incorporating realistic operational constraints and dynamic information sharing.

Findings

Adaptive pilot-density control outperforms static configurations.

MARL policies effectively exploit sensing-communication-energy trade-offs.

Simulation shows improved detection reliability and network performance.

Abstract

Multi-UAV networks are increasingly deployed for large-scale inspection and monitoring missions, where operational performance depends on the coordination of sensing reliability, communication quality, and energy constraints. In particular, the rapid increase in overflowing waste bins and illegal dumping sites has created a need for efficient detection of waste hotspots. In this work, we introduce JCAS-MARL, a resource-aware multi-agent reinforcement learning (MARL) framework for joint communication and sensing (JCAS)-enabled UAV networks. Within this framework, multiple UAVs operate in a shared environment where each agent jointly controls its trajectory and the resource allocation of an OFDM waveform used simultaneously for sensing and communication. Battery consumption, charging behavior, and associated CO$_2$ emissions are incorporated into the system state to model realistic operational constraints. Information sharing occurs over a dynamic communication graph determined by UAV positions and wireless channel conditions. Waste hotspot detection requires consensus among multiple UAVs to improve reliability. Using this environment, we investigate how MARL policies exploit the sensing-communication-energy trade-off in JCAS-enabled UAV networks. Simulation results demonstrate that adaptive pilot-density control learned by the agents can outperform static configurations, particularly in scenarios where sensing accuracy and communication connectivity vary across the environment.