The Cognitive Compressive Sensing problem

Saeed Bagheri, Anna Scaglione

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

4 Citations (Scopus)

Abstract

In the Cognitive Compressive Sensing (CCS) problem, a Cognitive Receiver (CR) seeks to optimize the reward obtained by sensing an underlying N dimensional random vector, by collecting at most K arbitrary projections of it. The N components of the latent vector represent sub-channels states, that change dynamically from 'busy' to 'idle' and vice versa, as a Markov chain that is biased towards producing sparse vectors. To identify the optimal strategy we formulate the Multi-Armed Bandit Compressive Sensing (MAB-CS) problem, generalizing the popular Cognitive Spectrum Sensing model, in which the CR can sense K out of the N sub-channels, as well as the typical static setting of Compressive Sensing, in which the CR observes K linear combinations of the N dimensional sparse vector. The CR opportunistic choice of the sensing matrix should balance the desire of revealing the state of as many dimensions of the latent vector as possible, while not exceeding the limits beyond which the vector support is no longer uniquely identifiable.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Information Theory - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3195-3199
Number of pages5
ISBN (Print)9781479951864
DOIs
StatePublished - 2014
Externally publishedYes
Event2014 IEEE International Symposium on Information Theory, ISIT 2014 - Honolulu, HI, United States
Duration: Jun 29 2014Jul 4 2014

Other

Other2014 IEEE International Symposium on Information Theory, ISIT 2014
CountryUnited States
CityHonolulu, HI
Period6/29/147/4/14

Fingerprint

Compressive Sensing
Receiver
Sensing
Multi-armed Bandit
Spectrum Sensing
Support Vector
Optimal Strategy
Random Vector
Reward
Biased
Linear Combination
Markov chain
Optimise
Projection
Markov processes
Arbitrary

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Theoretical Computer Science
  • Information Systems

Cite this

Bagheri, S., & Scaglione, A. (2014). The Cognitive Compressive Sensing problem. In IEEE International Symposium on Information Theory - Proceedings (pp. 3195-3199). [6875424] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2014.6875424

The Cognitive Compressive Sensing problem. / Bagheri, Saeed; Scaglione, Anna.

IEEE International Symposium on Information Theory - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3195-3199 6875424.

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

Bagheri, S & Scaglione, A 2014, The Cognitive Compressive Sensing problem. in IEEE International Symposium on Information Theory - Proceedings., 6875424, Institute of Electrical and Electronics Engineers Inc., pp. 3195-3199, 2014 IEEE International Symposium on Information Theory, ISIT 2014, Honolulu, HI, United States, 6/29/14. https://doi.org/10.1109/ISIT.2014.6875424
Bagheri S, Scaglione A. The Cognitive Compressive Sensing problem. In IEEE International Symposium on Information Theory - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2014. p. 3195-3199. 6875424 https://doi.org/10.1109/ISIT.2014.6875424
Bagheri, Saeed ; Scaglione, Anna. / The Cognitive Compressive Sensing problem. IEEE International Symposium on Information Theory - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 3195-3199
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