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- W4297582017 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." @default.
- W4297582017 created "2022-09-30" @default.
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- W4297582017 creator A5037040357 @default.
- W4297582017 date "2014-01-16" @default.
- W4297582017 modified "2023-10-14" @default.
- W4297582017 title "The Cognitive Compressive Sensing Problem" @default.
- W4297582017 doi "https://doi.org/10.48550/arxiv.1401.4237" @default.
- W4297582017 hasPublicationYear "2014" @default.
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