TY - GEN

T1 - The Cognitive Compressive Sensing problem

AU - Bagheri, Saeed

AU - Scaglione, Anna

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84906569187&partnerID=8YFLogxK

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U2 - 10.1109/ISIT.2014.6875424

DO - 10.1109/ISIT.2014.6875424

M3 - Conference contribution

AN - SCOPUS:84906569187

SN - 9781479951864

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 3195

EP - 3199

BT - 2014 IEEE International Symposium on Information Theory, ISIT 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Symposium on Information Theory, ISIT 2014

Y2 - 29 June 2014 through 4 July 2014

ER -