TY - GEN
T1 - The myopic solution of the Multi-Armed Bandit Compressive Spectrum Sensing problem
AU - Bagheri, Saeed
AU - Scaglione, Anna
PY - 2014/1/1
Y1 - 2014/1/1
N2 - In this paper we formulate a Multi-Armed Bandit Compressive Spectrum Sensing (MAB-CSS) problem, in which a Cognitive Receiver (CR) decides dynamically how to best sense N sub-channels states, that switch from being occupied to being available as independent and statistically identical Markov chains. We assume that the CR is endowed with K CSS samplers each sensing an arbitrary mixture of the N signals in the sub-channels, and upon deciding what channels are available, it collects an equal reward from each channel unoccupied that is sensed. The MAB-CSS problem accounts for the ability of the CR of sweeping a large spectrum and being able to reconstruct the exact support of the N channels occupancy pattern, as long as the latter is sufficiently sparse. This is a generalization of the typical model in which the CR can sense K out of the N sub-channels. In choosing the compressive sensing strategy, the CR needs to consider how to gather the most informative statistics on the spectrum while not exceeding the limits beyond which the occupancy is no longer identifiable. In this work, we study a simplified and noiseless discrete sensing model and establish the structure of the optimum MAB-CSS myopic policy.
AB - In this paper we formulate a Multi-Armed Bandit Compressive Spectrum Sensing (MAB-CSS) problem, in which a Cognitive Receiver (CR) decides dynamically how to best sense N sub-channels states, that switch from being occupied to being available as independent and statistically identical Markov chains. We assume that the CR is endowed with K CSS samplers each sensing an arbitrary mixture of the N signals in the sub-channels, and upon deciding what channels are available, it collects an equal reward from each channel unoccupied that is sensed. The MAB-CSS problem accounts for the ability of the CR of sweeping a large spectrum and being able to reconstruct the exact support of the N channels occupancy pattern, as long as the latter is sufficiently sparse. This is a generalization of the typical model in which the CR can sense K out of the N sub-channels. In choosing the compressive sensing strategy, the CR needs to consider how to gather the most informative statistics on the spectrum while not exceeding the limits beyond which the occupancy is no longer identifiable. In this work, we study a simplified and noiseless discrete sensing model and establish the structure of the optimum MAB-CSS myopic policy.
KW - Opportunistic access
KW - cognitive radio
KW - compressive sensing
KW - multi-channel sensing
KW - myopic policy
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U2 - 10.1109/ICASSP.2014.6855009
DO - 10.1109/ICASSP.2014.6855009
M3 - Conference contribution
AN - SCOPUS:84905216241
SN - 9781479928927
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7258
EP - 7262
BT - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Y2 - 4 May 2014 through 9 May 2014
ER -