Secure and Efficient Dynamic Spectrum Access Solutions for Future Wireless Networks

University of Technology Sydney. Faculty of Engineering and Information Technology.The future wireless communication network (5G and beyond) is expected to provide many advantages, such as an extremely high peak rate, ultralow latency and less energy consumption. However, since an extremely large number of connecting devices will be deployed, the demand for the spectrum will also be growing exponentially, causing a problem of spectrum shortage. To effectively address the spectrum crunch, dynamic spectrum access (DSA), including both sensing-based and database-driven DSA, has been proposed. In this thesis, we investigate critical challenges in DSA, including the efficiency in sensing-based techniques and privacy in database-driven techniques. First, to improve the sensing performance of the sensing-based DSA in half-duplex (HD) systems, we propose two sensing approaches leveraging the property of deep learning networks. Our solutions are significantly superior in terms of the robustness to noise uncertainty, timing delay, and carrier frequency offset (CFO), compared to conventional sensing methods. Moreover, our work does not require any prior information of signals, which however is essential for the traditional sensing methods. Second, to improve the sensing performance of sensing-based DSA in full-duplex (FD) systems, we develop two novel sensing methods using the features of orthogonal frequency division multiplexing (OFDM) signals. The developed sensing approaches are robust to not only residual SI but also timing delay or CFO. We also obtain the closed-form expressions of the probability of detection and false alarm for our approaches. Third, to protect the users' privacy in the database-driven DSA, we develop two schemes to protect the operational privacy of Incumbent Users (IUs) and honest/dishonest Secondary Users (SUs). To implement our proposed work, we introduce an interference calculation scheme that allows users to calculate an interference budget without revealing operational information. It also reduces the computing overhead of our developed approaches. Additionally, we propose a “punishment and forgiveness” mechanism to encourage dishonest SUs to provide truthful information. Theoretical analysis and extensive simulations show that our proposed schemes can better protect all users’ operational privacy under various privacy attacks, yielding higher spectrum utilization with less online overhead, compared with state of the art approaches