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

• URL: http://arxiv.org/abs/2412.04074v3
• Date: Thu, 02 Jan 2025 02:09:11 GMT
• Title: Integrated Sensing and Communications for Low-Altitude Economy: A Deep Reinforcement Learning Approach
• Authors: Xiaowen Ye, Yuyi Mao, Xianghao Yu, Shu Sun, Liqun Fu, Jie Xu,
• Abstract summary: We study an integrated sensing and communications (ISAC) system for low-altitude economy (LAE) The expected communication sum-rate over a given flight period is maximized by jointly optimizing the beamforming at the GBS and UAVs' trajectories. We propose a novel LAE-oriented ISAC scheme, referred to as Deep LAE-ISAC (DeepLSC), by leveraging the deep reinforcement learning (DRL) technique.
• Score: 20.36806314683902
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• 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.

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