UAV-Aided Vehicular Short-Packet Communication and Edge Computing System under Time-Varying Channel

In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound...

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Published inIEEE transactions on vehicular technology Vol. 72; no. 5; pp. 1 - 13
Main Authors Shen, Shuai, Yang, Kun, Wang, Kezhi, Zhang, Guopeng
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Autonomous aerial vehicles
Communications systems
Edge computing
Gaussian process
Internet of Things
Lower bounds
Markov processes
Mobile computing
Multi-access edge computing
Optimization
Packet transmission
Reliability
Resource management
Roadsides
short-packet communication
Signal to noise ratio
Task analysis
time-varying channel
Time-varying channels
unmanned aerial vehicle
Unmanned aerial vehicles
vehicular network
Online AccessGet full text
ISSN0018-9545
1939-9359
DOI10.1109/TVT.2022.3232841

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Abstract In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound of signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process. Then, with the short-packet transmission, we maximize the total amount of computation by jointly optimizing the communication scheduling, the trajectories of the vehicle and on-board UAV, and the computing resource, subject to the mobility, connection and computation constraints. The formulated optimization problem is a mix-integer non-convex problem. To efficiently solve it, we propose an alternative algorithm based on the Lagrangian dual decomposition and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.
AbstractList In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing (MEC) service for the roadside Internet of Things (IoT) devices. In this system, considering the time-varying channel, we derive the lower bound of signal-to-noise ratio (SNR) based on the first-order Gauss-Markov process. Then, with the short-packet transmission, we maximize the total amount of computation by jointly optimizing the communication scheduling, the trajectories of the vehicle and on-board UAV, and the computing resource, subject to the mobility, connection and computation constraints. The formulated optimization problem is a mix-integer non-convex problem. To efficiently solve it, we propose an alternative algorithm based on the Lagrangian dual decomposition and successive convex approximation technique. Extensive simulation results are provided to show the performance gain of the proposed algorithm.
Author Zhang, Guopeng
Wang, Kezhi
Shen, Shuai
Yang, Kun
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crossref_primary_10_1109_JIOT_2024_3488193
crossref_primary_10_1109_TSUSC_2024_3401003
crossref_primary_10_1109_TWC_2024_3509978
crossref_primary_10_1016_j_comnet_2024_110873
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Snippet In this paper, a novel UAV-aided vehicular edge computing (VEC) network is proposed, where the vehicle and on-board UAV provide multi-access edge computing...
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SubjectTerms Algorithms
Autonomous aerial vehicles
Communications systems
Edge computing
Gaussian process
Internet of Things
Lower bounds
Markov processes
Mobile computing
Multi-access edge computing
Optimization
Packet transmission
Reliability
Resource management
Roadsides
short-packet communication
Signal to noise ratio
Task analysis
time-varying channel
Time-varying channels
unmanned aerial vehicle
Unmanned aerial vehicles
vehicular network
Title UAV-Aided Vehicular Short-Packet Communication and Edge Computing System under Time-Varying Channel
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