TY - GEN
T1 - Joint control and compressed sensing for dynamic spectrum access in agile wireless networks
AU - Michelusi, Nicolo
AU - Mitra, Urbashi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/30
Y1 - 2014/1/30
N2 - In this paper, a cross-layer framework for joint distributed sensing, estimation and control in agile wireless networks is presented. A network of secondary users (SUs) opportunistically accesses portions of the spectrum left unused by a licensed network of primary users (PUs). A central controller (CC) schedules the spectrum bands detected as idle for access by the SUs, based on compressed measurements acquired by the SUs. The sparsity in the spectrum occupancy dynamics is exploited: leveraging the spectrum occupancy estimate in the previous slot, the CC needs to estimate only a sparse residual uncertainty vector via sparse recovery techniques, so that only few measurements suffice. The sensing probability of the SUs and the spectrum scheduling are adapted over time by the CC, based on the current spectrum occupancy estimate, and jointly optimized so as to maximize the SU throughput, under constraints on the PU throughput degradation and the sensing cost incurred by the SUs. A compact state space representation and decoupling of the state estimator from the CC are proposed: the estimator provides a maximum-a-posteriori spectrum estimate, as well as false-alarm and mis-detection error probabilities for the bins detected as busy and idle, respectively, based on which the CC performs scheduling and sensing decisions. Simulation results demonstrate improvements up to 11% in the SU throughput over a static sensing scheme.
AB - In this paper, a cross-layer framework for joint distributed sensing, estimation and control in agile wireless networks is presented. A network of secondary users (SUs) opportunistically accesses portions of the spectrum left unused by a licensed network of primary users (PUs). A central controller (CC) schedules the spectrum bands detected as idle for access by the SUs, based on compressed measurements acquired by the SUs. The sparsity in the spectrum occupancy dynamics is exploited: leveraging the spectrum occupancy estimate in the previous slot, the CC needs to estimate only a sparse residual uncertainty vector via sparse recovery techniques, so that only few measurements suffice. The sensing probability of the SUs and the spectrum scheduling are adapted over time by the CC, based on the current spectrum occupancy estimate, and jointly optimized so as to maximize the SU throughput, under constraints on the PU throughput degradation and the sensing cost incurred by the SUs. A compact state space representation and decoupling of the state estimator from the CC are proposed: the estimator provides a maximum-a-posteriori spectrum estimate, as well as false-alarm and mis-detection error probabilities for the bins detected as busy and idle, respectively, based on which the CC performs scheduling and sensing decisions. Simulation results demonstrate improvements up to 11% in the SU throughput over a static sensing scheme.
UR - http://www.scopus.com/inward/record.url?scp=84936123948&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936123948&partnerID=8YFLogxK
U2 - 10.1109/ALLERTON.2014.7028585
DO - 10.1109/ALLERTON.2014.7028585
M3 - Conference contribution
AN - SCOPUS:84936123948
T3 - 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
SP - 1156
EP - 1162
BT - 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
Y2 - 30 September 2014 through 3 October 2014
ER -