Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R

Published Date

  • April 1, 2019

Published In

Volume / Issue

  • 67 / 4

Start / End Page

  • 2673 - 2688

Electronic International Standard Serial Number (EISSN)

  • 1558-0857

International Standard Serial Number (ISSN)

  • 0090-6778

Digital Object Identifier (DOI)

  • 10.1109/TCOMM.2018.2886020

Citation Source

  • Scopus