Multi-view Semi-Supervised Learning for Cooperative Spectrum Sensing
Document Type
Conference Proceeding
Date of Original Version
1-1-2021
Abstract
Cooperative spectrum sensing (CSS) for cognitive radio networks (CRN) emerged to enhance the utilization of the frequency bands. CSS has multiple collaborated secondary users (SUs) to detect the presence of primary users (PUs) by fusing the sensing information and then determining the PU signals. Most of the previous works train detectors with sufficient labeled sensing data. However, collecting labeled and accurate data is time-consuming and expensive. In this paper, we propose a multiview semi-supervised learning framework for CSS (MSCSS). Specifically, MSCSS first combines the complementary sensing information from different SUs to explore a latent representation from both labeled and unlabeled sensing data. Based on the representation, it learns a robust fusion graph, performs the label propagation to unlabeled data, and trains a detector. MSCSS considers the information sensed by each SU as one view of the state of the concerned spectrum channel and fuses the multi-view information to make a decision. Simulation results demonstrate the effectiveness of our method.
Publication Title, e.g., Journal
2021 IEEE Symposium Series on Computational Intelligence Ssci 2021 Proceedings
Citation/Publisher Attribution
Li, Lusi, Laura Slayton, Hepeng Li, and Haibo He. "Multi-view Semi-Supervised Learning for Cooperative Spectrum Sensing." 2021 IEEE Symposium Series on Computational Intelligence Ssci 2021 Proceedings (2021). doi: 10.1109/SSCI50451.2021.9660075.