This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency-based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the nonconvex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for collocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.
|Number of pages||14|
|Journal||IEEE Journal on Selected Topics in Signal Processing|
|Publication status||Published - 2017 Apr|
Bibliographical noteFunding Information:
Manuscript received June 16, 2016; revised November 25, 2016; accepted February 25, 2017. Date of publication March 3, 2017; date of current version April 11, 2017. This work was supported in part by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (2014R1A1A2054577) and in part by the Ministry of Science, ICT & Future Planning (2015R1A2A1A15054248). The guest editor coordinating the reviewof this paper and approving it for publicationwas Daniel P. Palomar.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering