Opportunistic spectrum access for UAV communications towards ultra dense networks
The growing popularity of unmanned aerial vehicle (UAV) attracts significant research interests and applications including low-altitude and airborne vehicles. Since there is no declared spectrum allocated to UAV communications, opportunistic transmission has been commonly considered as an important...
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Main Authors: | , , , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2021
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/145922 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The growing popularity of unmanned aerial vehicle (UAV) attracts significant research interests and applications including low-altitude and airborne vehicles. Since there is no declared spectrum allocated to UAV communications, opportunistic transmission has been commonly considered as an important way for supporting UAV communications. When sharing the same spectrum with other users such as satellites and mobile base stations, accurate spectrum sensing and allocation are of critical importance for UAV communications to avoid serious interference. As the UAVs can constantly move to different locations with various spectrum environments, the spectrum decision may be invalid only in a short period, leading to require fast spectrum sensing. Furthermore, an UAV needs to predict possible temporal and spatial vacations of the spectrum. In this case, the spectrum prediction has a high dimensional state space which is notoriously difficult to solve. In this paper, some other issues such as how to determine the spectrum processing time and how to detect the primary signals with high priority to avoid interference, are also discussed. Finally, a fast spectrum sensing algorithm is proposed to improve the energy detection performance by optimizing the error estimation and a constant ratio of missed detection. Our proposed algorithm does not require high computational capability and can achieve relatively accurate sensing in low signal-to-noise ratio scenarios. |
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