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

TY - JOUR

T1 - Task-Oriented Semantics-Aware Communication for Wireless UAV Control and Command Transmission

AU - Xu, Yujie

AU - Zhou, Hui

AU - Deng, Yansha

N1 - Funding Information: This work was supported in part by the Engineering and Physical Research Council (EPSRC), U.K., under Grant EP/W004348/1. This work is also a contribution by Project REASON, a UK Government funded project under the Future Open Networks Research Challenge (FONRC) sponsored by the Department of Science Innovation and Technology (DSIT). Publisher Copyright: © 2023 IEEE.

PY - 2023/8/1

Y1 - 2023/8/1

N2 - 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 letter, 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.

AB - 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 letter, 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.

KW - AoI

KW - Control and command

KW - DRL

KW - similarity

KW - task-oriented and semantics-aware communication

KW - UAV

UR - http://www.scopus.com/inward/record.url?scp=85163546352&partnerID=8YFLogxK

U2 - 10.1109/LCOMM.2023.3290109

DO - 10.1109/LCOMM.2023.3290109

M3 - Article

AN - SCOPUS:85163546352

SN - 1089-7798

VL - 27

SP - 2232

EP - 2236

JO - IEEE COMMUNICATIONS LETTERS

JF - IEEE COMMUNICATIONS LETTERS

IS - 8

ER -