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

TL;DR

This paper introduces a goal-oriented UAV communication framework using deep reinforcement learning to enhance real-time target tracking accuracy, addressing the limitations of traditional communication-focused optimization methods.

Contribution

It proposes a novel DRL-based approach with a proactive repetition scheme for optimizing communication in UAV target tracking tasks, emphasizing task performance.

Findings

Outperforms PID in tracking accuracy

Effective in optimizing data selection and repetitions

Validates approach through comparative experiments

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.