Qian-Zhi Huang

Learn More
This letter studies the energy-constrained MMSE decentralized estimation problem with the best-linear-unbiased-estimator fusion rule, under the assumptions that 1. Each sensor can only send a quantized version of its raw measurement to the fusion center (FC), and 2. Exact knowledge of the sensor noise variance is unknown at the FC but only an associated(More)
We study the problem of minimal-energy decentralized estimation via sensor networks with the best-linear-unbiased-estimator fusion rule. While most of the existing solutions require the knowledge of instantaneous noise variances for energy allocation, the proposed approach instead relies on an associated statistical model. The minimization of total energy(More)
This paper studies minimal-energy decentralized estimation in sensor networks under best-linear-unbiased-estimator fusion rule. While most of the existing related works require the knowledge of instantaneous noise variances for energy allocation, the proposed approach instead relies on an associated statistical model. The minimization of total energy is(More)
This paper studies the energy-constrained MMSE decentralized estimation problem with the best-linear-unbiased- estimator fusion rule, under the assumptions that i. each sensor can only send a quantized version of its raw measurement to the fusion center (FC), and ii. exact knowledge of the sensor noise variance is unknown at the FC but only an associated(More)
In this paper, we deal with the challenges regarding the provision of Inter Carrier interference (ICI) and Peak-to-Average Power Ratio (PAPR) for the performance evaluation of Multiple-Input and Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM). We propose a Non-linear Companding Technique (NCT) based on the inverse hyperbolic cosine(More)
  • 1