Goal-Oriented UAV Communication Design and Optimization for Target Tracking: A MachineLearning Approach
<|reference_start|>Goal-Oriented UAV Communication Design and Optimization for Target Tracking: A MachineLearning Approach: To accomplish various tasks, safe and smooth control of unmanned aerial vehicles (UAVs) needs to be guaranteed, which cannot be met by existing ultra-reliable low latency communications (URLLC). This has attracted the attention of the communication field, where most existing work mainly focused on optimizing communication performance (i.e., delay) and ignored the performance of the task (i.e., tracking accuracy). To explore the effectiveness of communication in completing a task, in this letter, we propose a goal-oriented communication framework adopting a deep reinforcement learning (DRL) algorithm with a proactive repetition scheme (DeepP) to optimize C&C data selection and the maximum number of repetitions in a real-time target tracking task, where a base station (BS) controls a UAV to track a mobile target. The effectiveness of our proposed approach is validated by comparing it with the traditional proportional integral derivative (PID) algorithm.<|reference_end|>
@article{wu2024goal-oriented,
    title={Goal-Oriented UAV Communication Design and Optimization for Target
  Tracking: A MachineLearning Approach},
    author={Wenchao Wu, Yanning Wu, Yuanqing Yang, and Yansha Deng},
    journal={arXiv preprint arXiv:2408.04358},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04358},
    primaryClass={eess.SY cs.SY}
}