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)|
|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.|
|Number of pages||5|
|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
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016|
|Period||3/20/16 → 3/25/16|
Bibliographical noteFunding Information:
Supported in part by NSF AST- 1247885, ECCS 1231504.
© 2016 IEEE.
Copyright 2016 Elsevier B.V., All rights reserved.