TY - JOUR
T1 - Parametric frugal sensing of power spectra for moving average models
AU - Konar, Aritra
AU - Sidiropoulos, Nicholas D.
AU - Mehanna, Omar
N1 - Publisher Copyright:
© 1991-2012 IEEE.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - Wideband spectrum sensing is a fundamental component of cognitive radio and other applications. A novel frugal sensing scheme was recently proposed as a means of crowdsourcing the task of spectrum sensing. Using a network of scattered low-end sensors transmitting randomly filtered power measurement bits to a fusion center, a non-parametric approach to spectral estimation was adopted to estimate the ambient power spectrum. Here, a parametric spectral estimation approach is considered within the context of frugal sensing. Assuming a Moving-Average (MA) representation for the signal of interest, the problem of estimating admissible MA parameters, and thus the MA power spectrum, from single bit quantized data is formulated. This turns out being a non-convex quadratically constrained quadratic program (QCQP), which is NP-Hard in general. Approximate solutions can be obtained via semi-definite relaxation (SDR) followed by randomization; but this rarely produces a feasible solution for this particular kind of QCQP. A new Sequential Parametric Convex Approximation (SPCA) method is proposed for this purpose, which can be initialized from an infeasible starting point, and yet still produce a feasible point for the QCQP, when one exists, with high probability. Simulations not only reveal the superior performance of the parametric techniques over the globally optimum solutions obtained from the non-parametric formulation, but also the better performance of the SPCA algorithm over the SDR technique.
AB - Wideband spectrum sensing is a fundamental component of cognitive radio and other applications. A novel frugal sensing scheme was recently proposed as a means of crowdsourcing the task of spectrum sensing. Using a network of scattered low-end sensors transmitting randomly filtered power measurement bits to a fusion center, a non-parametric approach to spectral estimation was adopted to estimate the ambient power spectrum. Here, a parametric spectral estimation approach is considered within the context of frugal sensing. Assuming a Moving-Average (MA) representation for the signal of interest, the problem of estimating admissible MA parameters, and thus the MA power spectrum, from single bit quantized data is formulated. This turns out being a non-convex quadratically constrained quadratic program (QCQP), which is NP-Hard in general. Approximate solutions can be obtained via semi-definite relaxation (SDR) followed by randomization; but this rarely produces a feasible solution for this particular kind of QCQP. A new Sequential Parametric Convex Approximation (SPCA) method is proposed for this purpose, which can be initialized from an infeasible starting point, and yet still produce a feasible point for the QCQP, when one exists, with high probability. Simulations not only reveal the superior performance of the parametric techniques over the globally optimum solutions obtained from the non-parametric formulation, but also the better performance of the SPCA algorithm over the SDR technique.
KW - Cognitive radio
KW - distributed spectrum sensing
KW - moving-average processes
KW - parametric spectral analysis
KW - quadratically constrained quadratic programming (QCQP)
KW - quantization
KW - semidefinite programming (SDP) relaxation
UR - http://www.scopus.com/inward/record.url?scp=84922563746&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84922563746&partnerID=8YFLogxK
U2 - 10.1109/TSP.2014.2386291
DO - 10.1109/TSP.2014.2386291
M3 - Article
AN - SCOPUS:84922563746
VL - 63
SP - 1073
EP - 1085
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 5
M1 - 6998085
ER -