Transfer learning-based spectrum sensing over unknown scenarios - Zhang, Qishu (Nanyang Technological University, 2024)
Efficient spectrum sensing is crucial for the optimal utilization of the increasingly crowded electromagnetic spectrum in cognitive radio networks (CRNs). Traditional spectrum sensing methods, including energy detection, matched filtering, and cyclostationary feature detection, often struggle in dynamic and unknown environments due to their dependence on predefined signal characteristics and extensive prior knowledge. To address these limitations, this dissertation explores the application of transfer learning-based deep learning techniques to enhance spectrum sensing performance in diverse and unpredictable scenarios.