Graph-based Multi-view Learning for Cooperative Spectrum Sensing
Document Type
Conference Proceeding
Date of Original Version
7-18-2021
Abstract
This paper concerns the cooperative spectrum sensing (CSS) for cognitive radio (CR) networks, where the secondary users (SUs) collaborate to detect the presence of the primary users (PUs). With CSS, the information from different SUs is first fused, then, the detection of the PU signal is implemented based on the fused information. Most of the previous works focus on the design of a mapping function that calculates the probability of the existence of the PU signal based on the fused information. In this paper, we study the fusion process which combines the information from all SUs based on the local property of each SU. A graph-based multi-view learning framework for CSS (GMCSS) is designed to better fuse the information from different SUs. In the proposed framework, the information from each SU is considered as a view of the state of the target wireless channel and is fused with the information from other SUs through a graph-based learning process. Simulation results demonstrate the effectiveness of our method.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Volume
2021-July
Citation/Publisher Attribution
Li, Lusi, He Jiang, and Haibo He. "Graph-based Multi-view Learning for Cooperative Spectrum Sensing." Proceedings of the International Joint Conference on Neural Networks 2021-July, (2021). doi: 10.1109/IJCNN52387.2021.9534051.