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Wang, Haoyu

University of British Columbia

In maritime communications, particularly within the Royal Canadian Navy and Canadian Armed Forces, the prevalent use of static spectrum access presents significant inefficiencies, characterized by suboptimal spectrum utilization and the absence of automated frequency adaptability. To address these issues, this thesis designs an intelligent automated cognitive radio system. This system employs a hybrid Dynamic Spectrum Access (DSA) scheme, underpinned by deep reinforcement learning, to dynamically adjust to the maritime environment, thereby optimizing spectrum efficiency and enabling automatic frequency switching and power selection. Meanwhile, we specifically design a Media Access Control (MAC) protocol to enable the implementation of this scheme in GNU Radio. Additionally, we propose a novel wide-band cooperative spectrum sensing algorithm to obtain more accurate channel state information. Furthermore, the artificial noise used in this system enhances the communication security of primary users. Finally, we have implemented this system in GNU Radio and conducted experiments. Both simulation and experimental results have confirmed the effectiveness of the proposed solution, which allows secondary users to automatically switch frequency bands to access the radio environment without interfering with the normal transmissions of primary users, thereby improving spectrum utilization.

Text

2024-04-30

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/88131

Electrical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/