Abstract
Estimating the power spectrum of a wide-sense stationary stochastic process is a core component of several signal processing tasks. Distributed spectrum sensing problems naturally emerge in cases where measurements of different realizations of a stochastic process are collected at multiple spatial locations. This paper describes a distributed power spectrum sensing scheme for stochastic processes which are well represented by an autoregressive (AR) process. The sensing model comprises a network of scattered low-end sensors which transmit randomly filtered, one bit quantized power measurements to a fusion center. The problem of AR power spectrum estimation from such binary power measurements is cast as a non-convex optimization problem, and an alternating minimization algorithm is proposed to obtain a stationary point. Simulations showcase the effectiveness of this scheme when the AR parametrization is valid.
Original language | English (US) |
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Title of host publication | 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4293-4297 |
Number of pages | 5 |
ISBN (Electronic) | 9781479999880 |
DOIs | |
State | Published - May 18 2016 |
Event | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China Duration: Mar 20 2016 → Mar 25 2016 |
Publication series
Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
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Volume | 2016-May |
ISSN (Print) | 1520-6149 |
Other
Other | 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 |
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Country/Territory | China |
City | Shanghai |
Period | 3/20/16 → 3/25/16 |
Bibliographical note
Funding Information:Supported in part by NSF AST- 1247885, ECCS 1231504.
Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.