0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessIn this paper, we propose several spectrum sensing methods designed using the generalized likelihood ratio test (GLRT) paradigm, for application in a cognitive radio network. The proposed techniques utilize the eigenvalues of the sample covariance matrix of the received signal vector, taking advantage of the fact that in practice, the primary signal in a cognitive radio environment will either occupy a subspace of dimension strictly smaller than the dimension of the observation space, or have a spectrum that is non-white. We show that by making various assumptions on the availability of side information such as noise variance and signal space dimension, several feasible algorithms result which all outperform the standard energy detector.
Teng Joon Lim, Rui Zhang, Ying‐Chang Liang, Yonghong Zeng (2008). GLRT-Based Spectrum Sensing for Cognitive Radio. , pp. 1-5, DOI: 10.1109/glocom.2008.ecp.842.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2008
Authors
4
Datasets
0
Total Files
0
Language
English
DOI
10.1109/glocom.2008.ecp.842
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access
