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Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks.
- Source :
- IEEE Transactions on Communications; Apr2019, Vol. 67 Issue 4, p2673-2688, 16p
- Publication Year :
- 2019
-
Abstract
- Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors, and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0 dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00906778
- Volume :
- 67
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Communications
- Publication Type :
- Academic Journal
- Accession number :
- 135966563
- Full Text :
- https://doi.org/10.1109/TCOMM.2018.2886020