Your Search Results

Use this resource - and many more! - in your textbook!

AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.

Experience the freedom of customizing your course pack with AcademicPub!
Not an educator but still interested in using this content? No problem! Visit our provider's page to contact the publisher and get permission directly.

The Restless Multi-Armed Bandit Formulation of the Cognitive Compressive Sensing Problem

By: Bagheri, S.; Scaglione, A.;

2015 / IEEE


This item from - IEEE Transaction - Signal Processing and Analysis - In this paper we introduce the Cognitive Compressive Sensing (CCS) problem, modeling a Cognitive Receiver (CR) that optimizes the K projections of a N>K dimensional vector dynamically, by optimizing the objective of correctly detecting the maximum number of idle entries, while updating each time its Bayesian beliefs on the future vector realizations. We formulate and study the CCS as a Restless Multi-Armed Bandit problem, generalizing the popular Cognitive Spectrum Sensing model, in which the CR can sense K out of the N sub-channels and propose a novel adaptive Finite Rate of Innovation (FRI) sampling method based on the CCS approach. While in general the optimum policy remains elusive, we provide sufficient conditions in which in the limit for large K and N the greedy policy is optimum. Numerical results corroborate our theoretical findings.