TY - GEN
T1 - Data precoding and energy transmission for parameter estimation in MIMO wireless powered sensor networks
AU - Venkategowda, Naveen K.D.
AU - Lee, Hoon
AU - Lee, Inkyu
N1 - Funding Information:
This work was supported by National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) of Korea Government under Grant 2014R1A2A1A10049769 and 2017R1A2B3012316.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/8
Y1 - 2018/2/8
N2 - In this paper, we study parameter estimation in multiple-input multiple-output (MIMO) wireless powered sensor networks (WPSN). The sensor nodes are powered exclusively by harvesting the radio frequency signals transmitted from the energy access points. We propose a joint design of the sensor data precoders and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate. This design also incorporates optimal allocation of the harvested power for data acquisition and data transmission. We employ a zero-forcing precoding based estimation framework and the alternating minimization technique to compute the precoders, power allocation, and energy covariance matrices. Simulation results demonstrate that the proposed method achieves a superior estimation performance in comparison to the conventional energy transfer techniques for estimation in WPSNs.
AB - In this paper, we study parameter estimation in multiple-input multiple-output (MIMO) wireless powered sensor networks (WPSN). The sensor nodes are powered exclusively by harvesting the radio frequency signals transmitted from the energy access points. We propose a joint design of the sensor data precoders and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate. This design also incorporates optimal allocation of the harvested power for data acquisition and data transmission. We employ a zero-forcing precoding based estimation framework and the alternating minimization technique to compute the precoders, power allocation, and energy covariance matrices. Simulation results demonstrate that the proposed method achieves a superior estimation performance in comparison to the conventional energy transfer techniques for estimation in WPSNs.
UR - http://www.scopus.com/inward/record.url?scp=85045237368&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2017.8288111
DO - 10.1109/VTCFall.2017.8288111
M3 - Conference contribution
AN - SCOPUS:85045237368
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 5
BT - 2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 86th IEEE Vehicular Technology Conference, VTC Fall 2017
Y2 - 24 September 2017 through 27 September 2017
ER -
