# Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2412.04074 · HTML: https://mlsanity.com/paper/2412.04074/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 14 of 14)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Benchmarking Deep Reinforcement Learning for Continuous Control](https://mlsanity.com/paper/1604.06778/) (2016)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2110.02857/) (2021)
- [Throughput Maximization for UAV-enabled Integrated Periodic Sensing and Communication](https://mlsanity.com/paper/2203.06358/) (2022)
- [ISAC from the Sky: UAV Trajectory Design for Joint Communication and Target Localization](https://mlsanity.com/paper/2207.02904/) (2022)
- [Trajectory Planning of Cellular-Connected UAV for Communication-assisted Radar Sensing](https://mlsanity.com/paper/2207.13238/) (2022)
- [Networked ISAC for Low-Altitude Economy: Coordinated Transmit Beamforming and UAV Trajectory Design](https://mlsanity.com/paper/2406.16946/) (2024)
- [UAV Trajectory and Beamforming Optimization for Integrated Periodic Sensing and Communication](https://mlsanity.com/paper/2203.10223/) (2022)
- [Beamforming Design and Trajectory Optimization for UAV-Empowered Adaptable Integrated Sensing and Communication](https://mlsanity.com/paper/2210.01434/) (2022)
- [A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV](https://mlsanity.com/paper/2405.17015/) (2024)
- [When UAVs Meet ISAC: Real-Time Trajectory Design for Secure Communications](https://mlsanity.com/paper/2306.14140/) (2023)
- [Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach](https://mlsanity.com/paper/2306.16342/) (2023)

## Built on by (most cited first, 6 of 6)

- [LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning](https://mlsanity.com/paper/2604.16910/) (2026)
- [Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding](https://mlsanity.com/paper/2412.21069/) (2024)
- [Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning](https://mlsanity.com/paper/2506.18560/) (2025)
- [CSI-tuples-based 3D Channel Fingerprints Construction Assisted by MultiModal Learning](https://mlsanity.com/paper/2603.25288/) (2026)
- [UAV-Assisted Cooperative Edge Inference for Low-Altitude Economy via MoE-based Hierarchical Deep Reinforcement Learning](https://mlsanity.com/paper/2605.19290/) (2026)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

This paper studies an integrated sensing and communications (ISAC) system for low-altitude economy (LAE), where a ground base station (GBS) provides communication and navigation services for authorized unmanned aerial vehicles (UAVs), while sensing the low-altitude airspace to monitor the unauthorized mobile target. The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs' trajectories, subject to the constraints on the average signal-to-noise ratio requirement for sensing, the flight mission and collision avoidance of UAVs, as well as the maximum transmit power at the GBS. Typically, this is a sequential decision-making problem with the given flight mission. Thus, we transform it to a specific Markov decision process (MDP) model called episode task. Based on this modeling, we propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique. In DeepLSC, a reward function and a new action selection policy termed constrained noise-exploration policy are judiciously designed to fulfill various constraints. To enable efficient learning in episode tasks, we develop a hierarchical experience replay mechanism, where the gist is to employ all experiences generated within each episode to jointly train the neural network. Besides, to enhance the convergence speed of DeepLSC, a symmetric experience augmentation mechanism, which simultaneously permutes the indexes of all variables to enrich available experience sets, is proposed. Simulation results demonstrate that compared with benchmarks, DeepLSC yields a higher sum-rate while meeting the preset constraints, achieves faster convergence, and is more robust against different settings.