Energy efficient short‐packet‐communication in UAV‐assisted cognitive network

This paper studies unmanned aerial vehicle (UAV)‐assisted cognitive network, where the UAV can improve the communication quality of edge users. Short packet communication (SPC) is widely used due to its low delay transmission characteristic. Unlike long packet communication in conventional wireless...

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Bibliographic Details
Published in:IET communications 2023-06, Vol.17 (10), p.1149-1161
Main Authors: Han, Huizhu, Huang, Yangchao, Hu, Hang, Pan, Yu, An, Qi, Liu, Shihao, Zhao, Senhao
Format: Article
Language:English
Subjects:
Airspeed
Algorithms
Communication
Data transmission
Energy consumption
Error analysis
False alarms
Internet of Things
Iterative algorithms
Optimization
Packet transmission
Transmission rate (communications)
Unmanned aerial vehicles
Wireless communications
Wireless networks
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Summary:This paper studies unmanned aerial vehicle (UAV)‐assisted cognitive network, where the UAV can improve the communication quality of edge users. Short packet communication (SPC) is widely used due to its low delay transmission characteristic. Unlike long packet communication in conventional wireless networks, SPC has a non‐negligible packet error rate and its data transmission rate is less than Shannon capacity. Considering the fact that the UAV is usually powered by battery, the energy efficiency (EE) maximisation problem is investigated based on short packet transmission in the UAV‐assisted cognitive network. Firstly, the closed‐form expression of EE is analysed, and then the optimisation problem is formulated by jointly optimising the spectrum sensing time, packet error rate, the flight speed, and the coverage range of UAV. Secondly, the optimisation problem is solved by dividing it into four subproblems. Then, an efficient iterative algorithm is proposed to tackle this problem. Simulation results show that the proposed optimisation scheme can evidently improve the EE performance compared with other benchmark schemes. In addition, the proposed joint optimisation algorithm not only has better convergence than exhaustive method, but also has higher stability than PSO algorithm.
ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.12435