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

Nicolo Michelusi, Matthew Nokleby, Urbashi Mitra, Robert Calderbank

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Article number8571293
Pages (from-to)2673-2688
Number of pages16
JournalIEEE Transactions on Communications
Issue number4
StatePublished - Apr 2019
Externally publishedYes


  • Spectrum sensing
  • cognitive radio
  • dense networks
  • interference management

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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