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

Yujie Xu; Hui Zhou; Yansha Deng

doi:10.1109/LCOMM.2023.3290109

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

Yujie Xu, Hui Zhou, Yansha Deng

King's College London

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

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 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.

Original language	English
Pages (from-to)	2232-2236
Number of pages	5
Journal	IEEE Communications Letters
Volume	27
Issue number	8
Early online date	27 Jun 2023
DOIs	https://doi.org/10.1109/LCOMM.2023.3290109
Publication status	Published - 1 Aug 2023
Externally published	Yes

Funding

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).

Funders	Funder number
Engineering and Physical Sciences Research Council	EP/W004348/1
Department for Science, Innovation and Technology

Keywords

AoI
Control and command
DRL
UAV
similarity
task-oriented and semantics-aware communication

ASJC Scopus subject areas

Modelling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/LCOMM.2023.3290109

Cite this

@article{b43f3f4e702641a1913c7bda00659105,

title = "Task-Oriented Semantics-Aware Communication for Wireless UAV Control and Command Transmission",

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 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.",

keywords = "AoI, Control and command, DRL, UAV, similarity, task-oriented and semantics-aware communication",

author = "Yujie Xu and Hui Zhou and Yansha Deng",

year = "2023",

month = aug,

day = "1",

doi = "10.1109/LCOMM.2023.3290109",

language = "English",

volume = "27",

pages = "2232--2236",

journal = "IEEE Communications Letters",

issn = "1089-7798",

publisher = "Institute of Electrical and Electronics Engineers",

number = "8",

}

TY - JOUR

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

AU - Xu, Yujie

AU - Zhou, Hui

AU - Deng, Yansha

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 - UAV

KW - similarity

KW - task-oriented and semantics-aware communication

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

SN - 1089-7798

VL - 27

SP - 2232

EP - 2236

JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 8

ER -

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

Abstract

Funding

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this