Startseite Performance analysis of UAV-enabled dual-hop mixed RF-FSO communication systems with user scheduling
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Performance analysis of UAV-enabled dual-hop mixed RF-FSO communication systems with user scheduling

  • Deepika Latka , Mona Aggarwal EMAIL logo und Swaran Ahuja
Veröffentlicht/Copyright: 10. Dezember 2024
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Abstract

Using UAVs as relays in wireless communication offers several advantages like flexibility, coverage, line-of-sight communication, cost effectiveness, adaptability to changing network demands, etc. UAVs can perform surveillance, delivery, mapping, and disaster response. They offer real-time data collection, high maneuverability, and access to remote areas. In this manuscript, we consider a UAV-enabled dual hop mixed RF/FSO wireless communication system. The system includes a UAV acting as a decode-and-forward relay, facilitating communication from the base station (B) to multiple mobile users. The opportunistic scheduling scheme is used to select the user with the best instantaneous channel conditions, typically based on the highest signal-to-noise ratio (SNR) or lowest outage probability, to maximize throughput and ensure more reliable communication. The first hop from B to the UAV relay R, employs a free space optics (FSO) link modeled with gamma–gamma fading. The second hop, from R to multiple users, is a radio frequency (RF) link modeled with Rayleigh fading. The direct communication between base station and mobile users is not possible due to the blockage by various natural and man-made obstacles. We analyze the outage probability at high SNR values to gain further insights into system performance. Additionally, closed-form analytical expressions for the outage probability and average bit error rate (ABER) of the proposed system are derived.


Corresponding author: Mona Aggarwal, Department of MDE, North Cap University, Sector 23 A, Gurgaon, Haryana 122017, India, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2024-10-06
Accepted: 2024-11-08
Published Online: 2024-12-10

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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