TY - GEN
T1 - Linear MMSE Precoder Combiner Designs for Decentralized Estimation in Wireless Sensor Networks
AU - Rajput, Kunwar Pritiraj
AU - Verma, Yogesh
AU - Venkategowda, Naveen K.D.
AU - Jagannatham, Aditya K.
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.
AB - This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.
KW - Wireless sensor networks
KW - linear decentralized estimation
KW - majorization theory
KW - precoder-combiner design
UR - http://www.scopus.com/inward/record.url?scp=85100440503&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100440503&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9322457
DO - 10.1109/GLOBECOM42002.2020.9322457
M3 - Conference contribution
AN - SCOPUS:85100440503
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -