Exploiting sparse dynamics for bandwidth reduction in cooperative sensing systems

Harish Ganapathy, Constantine Caramanis, Lei Ying

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Recently, there has been a significant interest in developing cooperative sensing systems for certain types of wireless applications. In such systems, a group of sensing nodes periodically collects measurements about the signals being observed in the given geographical region and transmits these measurements to a central node, which in turn processes this information to recover the signals. For example, in cognitive radio networks, the signals of interest are those generated by the primary transmitters and the sensing nodes are the secondary users. In such networks, it is critically important to be able to reliably determine the presence or absence of primary transmitters in order to avoid causing interference. The standard approach to transmitting these measurements from the sensor nodes to the fusion center has been to use orthogonal channels. Such an approach quickly places a burden on the control-channel-capacity of the network that would scale linearly in the number of cooperating sensing nodes. In this paper, we show that as long as one condition is satisfied: the dynamics of the observed signals are sparse, i.e., the observed signals do not change their values very rapidly in relation to the time-scale at which the measurements are collected, we can significantly reduce the control bandwidth of the system while achieving near full (linear) bandwidth performance.

Original languageEnglish (US)
Article number6509448
Pages (from-to)3671-3682
Number of pages12
JournalIEEE Transactions on Signal Processing
Volume61
Issue number14
DOIs
StatePublished - Jul 17 2013

Keywords

  • Compressed sensing
  • compressive sampling
  • cooperative sensing
  • null-space property
  • restricted isometry

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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