TY - JOUR
T1 - Distributed SNR estimation with power constrained signaling over Gaussian multiple-access channels
AU - Banavar, Mahesh K.
AU - Tepedelenlioglu, Cihan
AU - Spanias, Andreas
N1 - Funding Information:
Manuscript received November 10, 2011; revised February 09, 2012; accepted February 10, 2012. Date of publication February 22, 2012; date of current version May 11, 2012. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Ta-Sung Lee. The work in this correspondence is supported by the SenSIP Center, Arizona State University. Parts of this work have previously appeared in the Proceedings of the IEEE DSP/SPE Workshop, January 2011 [1].
PY - 2012/6
Y1 - 2012/6
N2 - A sensor network is used for distributed signal-to-noise ratio (SNR) estimation in a single-time snapshot. Sensors observe a signal embedded in noise, and each observation is phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the mean and variance are estimated jointly, using an asymptotically minimum-variance estimator. It is shown that this joint estimator decouples into simple individual estimators of the mean and the variance. The constant-modulus phase modulation scheme ensures a fixed transmit power, robust estimation across several sensing noise distributions, as well as an SNR estimate that requires a single set of transmissions from the sensors to the fusion center. The estimators are evaluated in terms of asymptotic variance, which are then used to evaluate the performance of the SNR estimator with Gaussian and Cauchy sensing noise distributions in the cases of total transmit power constraint as well as a per-sensor power constraint. For each sensing noise distribution, the optimal phase transmission parameters are also determined. The asymptotic relative efficiency of the estimators is evaluated. It is shown that among the noise distributions considered, the estimators are asymptotically efficient only when the noise distribution is Gaussian. Simulation results corroborate analytical results.
AB - A sensor network is used for distributed signal-to-noise ratio (SNR) estimation in a single-time snapshot. Sensors observe a signal embedded in noise, and each observation is phase modulated using a constant-modulus scheme and transmitted over a Gaussian multiple-access channel to a fusion center. At the fusion center, the mean and variance are estimated jointly, using an asymptotically minimum-variance estimator. It is shown that this joint estimator decouples into simple individual estimators of the mean and the variance. The constant-modulus phase modulation scheme ensures a fixed transmit power, robust estimation across several sensing noise distributions, as well as an SNR estimate that requires a single set of transmissions from the sensors to the fusion center. The estimators are evaluated in terms of asymptotic variance, which are then used to evaluate the performance of the SNR estimator with Gaussian and Cauchy sensing noise distributions in the cases of total transmit power constraint as well as a per-sensor power constraint. For each sensing noise distribution, the optimal phase transmission parameters are also determined. The asymptotic relative efficiency of the estimators is evaluated. It is shown that among the noise distributions considered, the estimators are asymptotically efficient only when the noise distribution is Gaussian. Simulation results corroborate analytical results.
KW - Asymptotic variance
KW - Distributed estimation
KW - SNR estimation
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84861140680&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861140680&partnerID=8YFLogxK
U2 - 10.1109/TSP.2012.2188524
DO - 10.1109/TSP.2012.2188524
M3 - Article
AN - SCOPUS:84861140680
SN - 1053-587X
VL - 60
SP - 3289
EP - 3294
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 6
M1 - 6156472
ER -