Joint Parameter Estimation From Binary Observations Over Decentralized Channels

[en] In wireless sensor networks, due to the bandwidth constraint, the distributed nodes (DNs) might only provide binary representatives of the source signal, and then transmit them to the central node (CN). In this paper, we consider the joint estimation of signal amplitude and background noise variance from binary observations over decentralized channels. We first analyze the Cramér–Rao lower bounds (CRLBs) of the parameters of interest and develop a quasilinear estimator (QLE), in which the desirable estimates can be obtained from several intermediate parameters linearly. Next, we consider a more realistic situation where the decentralized channel is noisy during the data transmission. Based on the error propagation model, the asymptotic analysis shows that the performance of the proposed QLE is mainly dominated by the thresholds of the quantizers, which encourages us to adopt a correlated quantization (CQ) scheme by exploiting the spatial correlation among background noises/channel noises. To ease the implementation of QLE in practice, an adaptive quantization (AQ) scheme is also proposed so as to obtain reasonable selections of the required thresholds. Finally, numerical simulations are provided to validate our theoretical findings.

Disciplines :

Computer science

Author, co-author :

Fan, Wenzhe

Xia, Yili

Li, Chunguo

Huang, Yongming

OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)

External co-authors :

yes

Language :

English

Title :

Joint Parameter Estimation From Binary Observations Over Decentralized Channels

Publication date :

2022

Journal title :

IEEE Transactions on Signal Processing

ISSN :

1053-587X

Publisher :

IEEE, United States

Volume :

Pages :

509 - 522

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Security, Reliability and Trust

Available on ORBilu :

since 27 December 2022

Statistics

Number of views

45 (0 by Unilu)

Number of downloads

0 (0 by Unilu)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenAlex citations

WoS citations^™

Bibliography

A. S. Behbahani, A. M. Eltawil, and H. Jafarkhani, "Linear decentralized estimation of correlated data for power-constrained wireless sensor networks," IEEE Trans. Signal Process., vol. 60, no. 11, pp. 6003-6016, Nov. 2012.
Z. Luo, "Universal decentralized estimation in a bandwidth constrained sensor network," IEEE Trans. Inf. Theory, vol. 51, no. 6, pp. 2210-2219, Jun. 2005.
J. Xiao and Z. Luo, "Universal decentralized detection in a bandwidthconstrained sensor network," IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2617-2624, Aug. 2005.
H. Senol and C. Tepedelenlio, "Performance of distributed estimation over unknown parallel fading channels," IEEE Trans. Signal Process., vol. 56, no. 12, pp. 6057-6068, Dec. 2008.
A. Shirazinia, S. Dey, D. Ciuonzo, and P. S. Rossi, "Massive MIMO for decentralized estimation of a correlated source," IEEE Trans. Signal Process., vol. 64, no. 10, pp. 2499-2512, May 2016.
F. Gao, T. Cui, and A. Nallanathan, "On channel estimation and optimal training design for amplify and forward relay networks," IEEE Trans. Wireless Commun., vol. 7, no. 5, pp. 1907-1916, May 2008.
H. Senol, X. Li, and C. Tepedelenlio, "Rapidly time-varying channel estimation for full-duplex amplify-and-forward one-way relay networks," IEEE Trans. Signal Process., vol. 66, no. 11, pp. 3056-3069, Jun. 2018.
T. C. Aysal and K. E. Barner, "Constrained decentralized estimation over noisy channels for sensor networks," IEEE Trans. Signal Process., vol. 56, no. 4, pp. 1398-1410, Apr. 2008.
M. S. Stein, S. Bar, J. A. Nossek, and J. Tabrikian, "Performance analysis for channel estimation with 1-bit ADC and unknown quantization threshold," IEEE Trans. Signal Process., vol. 66, no. 10, pp. 2557-2571, May 2018.
M. Stein,A.Kürzl, A. Mezghani, and J. A. Nossek, "Asymptotic parameter tracking performance with measurement data of 1-bit resolution," IEEE Trans. Signal Process., vol. 63, no. 22, pp. 6086-6095, Nov. 2015.
O. Dabeer andA.Karnik, "Signal parameter estimation using 1-bit dithered quantization," IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5389-5405, Dec. 2006.
J. Fang and H. Li, "Adaptive distributed estimation of signal power from one-bit quantized data," IEEE Trans. Aerosp. Electron. Syst., vol. 46, no. 4, pp. 1893-1905, Oct. 2010.
D. K. Ho and B. D. Rao, "Antithetic dithered 1-bit massive MIMO architecture: Efficient channel estimation via parameter expansion and PML," IEEE Trans. Signal Process., vol. 67, no. 9, pp. 2291-2303, May 2019.
A. Ribeiro and G. B. Giannakis, "Bandwidth-constrained distributed estimation for wireless sensor networks-part I: Gaussian case," IEEE Trans. Signal Process., vol. 54, no. 3, pp. 1131-1143, Mar. 2006.
A. Ribeiro and G. B. Giannakis, "Bandwidth-constrained distributed estimation for wireless sensor networks-part II: Unknown probability density function," IEEE Trans. Signal Process., vol. 54, no. 7, pp. 2784-2796, Jul. 2006.
A. Mezghani, F. Antreich, and J. A. Nossek, "Multiple parameter estimation with quantized channel output," in Proc. Int. ITG Workshop Smart Antennas, 2010, pp. 143-150.
G. Zeitler, G. Kramer, and A. C. Singer, "Bayesian parameter estimation using single-bit dithered quantization," IEEE Trans. Signal Process., vol. 60, no. 6, pp. 2713-2726, Jun. 2012.
Y. Li, C. Tao, G. Granados, A. Mezghani, A. L. Swindlehurst, and L. Liu, "Channel estimation and performance analysis of one-bit massive MIMO systems," IEEE Trans. Signal Process., vol. 65, no. 15, pp. 4075-4089, Aug. 2017.
J. Choi, J. Mo, and R. W. Heath, "Near maximum-likelihood detector and channel estimator for uplink multiuser massive MIMO systems with one-bit ADCs," IEEE Trans. Commun., vol. 64, no. 5, pp. 2005-2018, May 2016.
H. Fu and Y. Chi, "Quantized spectral compressed sensing: Cramer-Rao bounds and recovery algorithms," IEEE Trans. Signal Process., vol. 66, no. 12, pp. 3268-3279, Jun. 2018.
J. Zhu, X. Wang, X. Lin, and Y. Gu, "Maximum likelihood estimation from sign measurements with sensing matrix perturbation," IEEE Trans. Signal Process., vol. 62, no. 15, pp. 3741-3753, Aug. 2014.
J. Zhu, X. Lin, R. S. Blum, and Y. Gu, "Parameter estimation from quantized observations in multiplicative noise environments," IEEE Trans. Signal Process., vol. 63, no. 15, pp. 4037-4050, Aug. 2015.
A. Host-Madsen and P. Handel, "Effects of sampling and quantization on single-tone frequency estimation," IEEE Trans. Signal Process., vol. 48, no. 3, pp. 650-662, Mar. 2000.
J. Ren, T. Zhang, J. Li, and P. Stoica, "Sinusoidal parameter estimation from signed measurements via majorization-minimization based RELAX," IEEE Trans. Signal Process., vol. 67, no. 8, pp. 2173-2186, Apr. 2019.
S. M. Kay, Fundamentals of Statistical Signal Processing. Upper Saddle River, NJ, USA: Prentice Hall PTR, 1993.
J. Zhang, R. S. Blum, L.M. Kaplan, and X. Lu, "A fundamental limitation on maximum parameter dimension for accurate estimation with quantized data," IEEE Trans. Inf. Theory, vol. 64, no. 9, pp. 6180-6195, Sep. 2018.
F. Liu, H. Zhu, C. Li, J. Li, P.Wang, and P.V.Orlik, "Angular-domain channel estimation for one-bit massive MIMO systems: Performance bounds and algorithms," IEEE Trans. Veh. Technol., vol. 69, no. 3, pp. 2928-2942, Mar. 2020.
H. C. Papadopoulos, G. W. Wornell, and A. V. Oppenheim, "Sequential signal encoding from noisy measurements using quantizers with dynamic bias control," IEEE Trans. Inf. Theory, vol. 47, no. 3, pp. 978-1002, Mar. 2001.
N. Mukhopadhyay, Probability and Statistical Inference. New York, NY, USA: Marcel Dekker, 2000.
W. H. Greene, Econometric Analysis. Upper Saddle River, NJ, USA: Prentice-Hall, 2003.
G.W. Oehlert, "A note on the delta method," Amer. Statist., vol. 46, no. 1, pp. 27-29, 1992.
T.Koski, "Statistics of the binary quantizer error in single-loop sigma-delta modulation with white Gaussian input," IEEE Trans. Inf. Theory, vol. 41, no. 4, pp. 931-943, Jul. 1995.
H. Li and J. Fang, "Distributed adaptive quantization and estimation for wireless sensor networks," IEEE Signal Process. Lett., vol. 14, no. 4, pp. 669-672, Jul. 2006.
J. Fang and H. Li, "Distributed adaptive quantization for wireless sensor networks: From delta modulation to maximum likelihood," IEEE Trans. Signal Process., vol. 56, no. 10, pp. 5246-5257, Oct. 2008.
G. B. Dantzig, Linear Programming and Extensions. Princeton, NJ, USA: Princeton Univ. Press, 1998.
J. G. Saw, M. C.Yang, and T.C.Mo, "Chebyshev inequality with estimated mean and variance," Amer. Stat., vol. 38, no. 2, pp. 130-132, 1984.
M. Chiani, D. Dardari, and M. K. Simon, "New exponential bounds and approximations for the computation of error probability in fading channels," IEEE Trans. Wireless Commun., vol. 2, no. 4, pp. 840-845, Jul. 2003.