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

arXiv: https://arxiv.org/abs/2501.06454 · HTML: https://mlsanity.com/paper/2501.06454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Enabling Joint Communication and Radar Sensing in Mobile Networks -- A Survey](https://mlsanity.com/paper/2006.07559/) (2020)
- [Emergent Communication in Multi-Agent Reinforcement Learning for Future Wireless Networks](https://mlsanity.com/paper/2309.06021/) (2023)
- [Accelerating Edge Intelligence via Integrated Sensing and Communication](https://mlsanity.com/paper/2107.09574/) (2021)
- [SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks](https://mlsanity.com/paper/2402.17773/) (2024)
- [An Experimental mmWave Channel Model for UAV-to-UAV Communications](https://mlsanity.com/paper/2007.11869/) (2020)
- [UAV-enabled Integrated Sensing and Communication: Tracking Design and Optimization](https://mlsanity.com/paper/2401.03726/) (2024)
- [JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical](https://mlsanity.com/paper/2304.07766/) (2023)

## Abstract (arXiv)

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.