Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Zhang, Junlin; Liu, Mingqian; Zhao, Nan; Chen, Yunfei; Yang, Qinghai; Ding, Zhiguo

Abstract

Unmanned Aerial Vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. With recent developments in UAVs, spectrum and energy efficient green UAV communication has become crucial. To deal with this issue, Spectrum Sharing Policy (SSP) is introduced to support green UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication. Firstly, we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Then, we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication.

Citation

Zhang, J., Liu, M., Zhao, N., Chen, Y., Yang, Q., & Ding, Z. (2023). Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication. Digital Communications and Networks, 9(4), 846-855. https://doi.org/10.1016/j.dcan.2022.09.017

• Journal Article Type: Article
• Acceptance Date: Sep 21, 2022
• Online Publication Date: Oct 5, 2022
• Publication Date: 2023-08
• Deposit Date: Jan 11, 2024
• Publicly Available Date: Jan 11, 2024
• Journal: Digital Communications and Networks
• Electronic ISSN: 2352-8648
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 4
• Pages: 846-855
• DOI: https://doi.org/10.1016/j.dcan.2022.09.017
• Public URL: https://durham-repository.worktribe.com/output/2119302