Work

Goal-Oriented UAV Communication Design and Optimization for Target Tracking: A Machine Learning Approach

Year: 2024

Type: article

Abstract: 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.

Source: IEEE Communications Letters

DOI: 10.1109/lcomm.2024.3442370

Authors: Wenchao Wu, Yanning Wu, Yuanqing Yang, Yansha Deng

Institution: King's College London

Language: English

Topic: UAV Applications and Optimization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Engineering and Physical Sciences Research Council

Awards: G1259514249, Smart Solutions Towards Cellular-Connected Unmanned Aerial Vehicles System (AUTONOMY)