The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

Cao Yu-ping - One of the best experts on this subject based on the ideXlab platform.

Lajos Hanzo - One of the best experts on this subject based on the ideXlab platform.

  • intelligent reflecting surface aided mimo broadcasting for simultaneous wireless information and power transfer
    IEEE Journal on Selected Areas in Communications, 2020
    Co-Authors: Cunhua Pan, Hong Ren, Kezhi Wang, Maged Elkashlan, Arumugam Nallanathan, Jiangzhou Wang, Lajos Hanzo
    Abstract:

    An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase Shift Matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase Shift Matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.

Hiroshi Nakazato - One of the best experts on this subject based on the ideXlab platform.

Weitun Yang - One of the best experts on this subject based on the ideXlab platform.

  • cyclic Shift Matrix a new tool for the translation matching problem
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Xiurui Geng, Weitun Yang
    Abstract:

    For numerous applications in image registration, sub-pixel translation estimation is a fundamental task, and increasing attention has been given to methods based on image phase information. However, we have found that none of these methods is universal. In other words, for any one of these methods, we can always find some image pairs which will not be well matched. In this paper, by introducing the cyclic Shift Matrix (CSM), we present a new model for the translation matching problem and derive a least squares solution for the model. In addition, by repeatedly applying the CSM to the matching image, an iterative CSM method is proposed to further improve the matching accuracy. Furthermore, we show that the traditional phase-based matching algorithms can only achieve an exact solution when there is a cyclic Shift relationship between the images to be matched. The proposed method is evaluated using simulated and real images and demonstrates a better performance in both accuracy and robustness compared with the state-of-the-art methods.

  • Cyclic Shift Matrix—A New Tool for the Translation Matching Problem
    IEEE Transactions on Geoscience and Remote Sensing, 2019
    Co-Authors: Xiurui Geng, Weitun Yang
    Abstract:

    For numerous applications in image registration, sub-pixel translation estimation is a fundamental task, and increasing attention has been given to methods based on image phase information. However, we have found that none of these methods is universal. In other words, for any one of these methods, we can always find some image pairs which will not be well matched. In this paper, by introducing the cyclic Shift Matrix (CSM), we present a new model for the translation matching problem and derive a least squares solution for the model. In addition, by repeatedly applying the CSM to the matching image, an iterative CSM method is proposed to further improve the matching accuracy. Furthermore, we show that the traditional phase-based matching algorithms can only achieve an exact solution when there is a cyclic Shift relationship between the images to be matched. The proposed method is evaluated using simulated and real images and demonstrates a better performance in both accuracy and robustness compared with the state-of-the-art methods.

Mao-ting Chien - One of the best experts on this subject based on the ideXlab platform.