Multi-scale spectrum sensing in millimeter wave cognitive networks

Nicolo Michelusi, Matthew Nokleby, Urbashi Mitra, Robert Calderbank

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, a multi-scale approach to spectrum sensing and information exchange in millimeter wave cognitive cellular networks is proposed. In order to overcome the huge energy cost of acquiring full network state information on the occupancy of each cell over the network, secondary users acquire local state estimates, which are aggregated up the hierarchy to produce multi-scale estimates of spectrum occupancy. The proposed design accounts for local estimation errors and the irregular interference patterns arising due to sensitivity to blockages, high attenuation, and high directionality at millimeter wave. A greedy algorithm based on agglomerative clustering is proposed to design an interference-based tree (IBT), matched to the interference pattern of the network. The proposed aggregation algorithm over IBT is shown to be much more cost efficient than acquiring full network state information from the neighboring cells, requiring as little as 1/5th of the energy cost.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1640-1644
Number of pages5
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Externally publishedYes
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Publication series

NameConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume2017-October

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
CountryUnited States
CityPacific Grove
Period10/29/1711/1/17

ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Biomedical Engineering
  • Instrumentation

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