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

2024-12-05

AI Summary

To address the dual requirements of high-speed communication and safe collision avoidance for unmanned aerial vehicles (UAVs) in low-altitude economy scenarios, this paper proposes a ground-base-station–UAV integrated sensing and communication (ISAC) system. We jointly optimize base station beamforming and UAV trajectory to maximize the total communication rate under constraints on sensing signal-to-noise ratio (SNR), collision avoidance, mission execution, and transmit power. Methodologically, we introduce DeepLSC—a novel deep reinforcement learning framework—featuring: (i) a constraint-aware noise exploration strategy ensuring feasible action selection; and (ii) a hierarchical experience replay mechanism with symmetric experience augmentation to enhance training efficiency and robustness. Simulation results demonstrate that DeepLSC achieves significant gains in total communication rate while strictly satisfying all constraints, accelerates convergence by 37%, and exhibits strong generalization across varying airspace densities and mission scales.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Integrated Sensing and Communication (ISAC)
• Unmanned Aerial Vehicle (UAV)
• High-speed Communication and Obstacle Avoidance

Innovation

• DeepLSC
• Integrated Sensing and Communication (ISAC)
• Deep Reinforcement Learning