Task-Oriented Semantics-Aware Communication for Wireless UAV Control and Command Transmission

TL;DR

This paper introduces a task-oriented, semantics-aware communication framework for UAV control data that enhances reliability and efficiency by leveraging data importance and context, surpassing traditional bit-oriented methods.

Contribution

It proposes a systematic TOSA communication framework for C&C data, defining its value based on similarity and AoI, and employs DRL to optimize information transmission.

Findings

The TOSA framework improves communication reliability for UAV control.

Simulation results validate the effectiveness of the proposed approach.

The method outperforms traditional bit-oriented communication in UAV scenarios.

Abstract

To guarantee the safety and smooth control of Unmanned Aerial Vehicle (UAV) operation, the new control and command (C&C) data type imposes stringent quality of service (QoS) requirements on the cellular network. However, the existing bit-oriented communication framework is already approaching the Shannon capacity limit, which can hardly guarantee the ultra-reliable low latency communications (URLLC) service for C&C transmission. To solve the problem, task-oriented semantics-aware (TOSA) communication has been proposed recently by jointly exploiting the context of data and its importance to the UAV control task. However, to the best of our knowledge, an explicit and systematic TOSA communication framework for emerging C&C data type remains unknown. Therefore, in this paper, we propose a TOSA communication framework for C&C transmission and define its value of information based on both the similarity and age of information (AoI) of C&C signals. We also propose a deep reinforcement learning (DRL) algorithm to maximize the TOSA information. Last but not least, we present the simulation results to validate the effectiveness of our proposed TOSA communication framework.