The Experts below are selected from a list of 54324 Experts worldwide ranked by ideXlab platform

Tomoaki Ohtsuki - One of the best experts on this subject based on the ideXlab platform.

  • damping factor learning of bp detection with node selection in massive mimo using neural network
    Vehicular Technology Conference, 2020
    Co-Authors: Junta Tachibana, Tomoaki Ohtsuki
    Abstract:

    In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection Performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection Performance deteriorates when there is a difference in the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can reduce the detection Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method can improve the detection Performance compared to the conventional DNN-dBP with the same computational complexity. By computer simulation, it is shown that the proposed method significantly reduces the bit error rate (BER) Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method has the better BER Performance than the conventional DNN-dBP with the same computational complexity.

  • learning and analysis of damping factor in massive mimo detection using bp algorithm with node selection
    IEEE Access, 2020
    Co-Authors: Junta Tachibana, Tomoaki Ohtsuki
    Abstract:

    In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection Performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection Performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection Performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER Performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.

Wenyu Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Magnetoelectric interaction and transport behaviours in magnetic nanocomposite thermoelectric materials
    Nature Nanotechnology, 2017
    Co-Authors: Wenyu Zhao, Wanting Zhu, Xianli Su, Xinfeng Tang, Qingjie Zhang, Ping Wei, Jihui Yang, Yong Liu, Jing Shi
    Abstract:

    How to suppress the Performance Deterioration of thermoelectric materials in the intrinsic excitation region remains a key challenge. The magnetic transition of permanent magnet nanoparticles from ferromagnetism to paramagnetism provides an effective approach to finding the solution to this challenge. Here, we have designed and prepared magnetic nanocomposite thermoelectric materials consisting of BaFe12O19 nanoparticles and Ba0.3In0.3Co4Sb12 matrix. It was found that the electrical transport behaviours of the nanocomposites are controlled by the magnetic transition of BaFe12O19 nanoparticles from ferromagnetism to paramagnetism. BaFe12O19 nanoparticles trap electrons below the Curie temperature (TC) and release the trapped electrons above the TC, playing an ‘electron repository’ role in maintaining high figure of merit ZT. BaFe12O19 nanoparticles produce two types of magnetoelectric effect—electron spiral motion and magnon-drag thermopower—as well as enhancing phonon scattering. Our work demonstrates that the Performance Deterioration of thermoelectric materials in the intrinsic excitation region can be suppressed through the magnetic transition of permanent magnet nanoparticles.

  • magnetoelectric interaction and transport behaviours in magnetic nanocomposite thermoelectric materials
    Nature Nanotechnology, 2017
    Co-Authors: Wenyu Zhao, Xianli Su, Xinfeng Tang, Qingjie Zhang, Jihui Yang, Yimin Chao
    Abstract:

    How to suppress the Performance Deterioration of thermoelectric materials in the intrinsic excitation region remains a key challenge. The magnetic transition of permanent magnet nanoparticles from ferromagnetism to paramagnetism provides an effective approach to finding the solution to this challenge. Here, we have designed and prepared magnetic nanocomposite thermoelectric materials consisting of BaFe12O19 nanoparticles and Ba0.3In0.3Co4Sb12 matrix. It was found that the electrical transport behaviours of the nanocomposites are controlled by the magnetic transition of BaFe12O19 nanoparticles from ferromagnetism to paramagnetism. BaFe12O19 nanoparticles trap electrons below the Curie temperature (TC) and release the trapped electrons above the TC, playing an ‘electron repository’ role in maintaining high figure of merit ZT. BaFe12O19 nanoparticles produce two types of magnetoelectric effect—electron spiral motion and magnon-drag thermopower—as well as enhancing phonon scattering. Our work demonstrates that the Performance Deterioration of thermoelectric materials in the intrinsic excitation region can be suppressed through the magnetic transition of permanent magnet nanoparticles. The ferromagnetic transition in magnetic nanoparticles embedded in magnetic nanocomposite thermoelectric materials is attributed to the trapping and release of electrons, which increases the Performance of the thermoelectric materials.

Yimin Chao - One of the best experts on this subject based on the ideXlab platform.

  • magnetoelectric interaction and transport behaviours in magnetic nanocomposite thermoelectric materials
    Nature Nanotechnology, 2017
    Co-Authors: Wenyu Zhao, Xianli Su, Xinfeng Tang, Qingjie Zhang, Jihui Yang, Yimin Chao
    Abstract:

    How to suppress the Performance Deterioration of thermoelectric materials in the intrinsic excitation region remains a key challenge. The magnetic transition of permanent magnet nanoparticles from ferromagnetism to paramagnetism provides an effective approach to finding the solution to this challenge. Here, we have designed and prepared magnetic nanocomposite thermoelectric materials consisting of BaFe12O19 nanoparticles and Ba0.3In0.3Co4Sb12 matrix. It was found that the electrical transport behaviours of the nanocomposites are controlled by the magnetic transition of BaFe12O19 nanoparticles from ferromagnetism to paramagnetism. BaFe12O19 nanoparticles trap electrons below the Curie temperature (TC) and release the trapped electrons above the TC, playing an ‘electron repository’ role in maintaining high figure of merit ZT. BaFe12O19 nanoparticles produce two types of magnetoelectric effect—electron spiral motion and magnon-drag thermopower—as well as enhancing phonon scattering. Our work demonstrates that the Performance Deterioration of thermoelectric materials in the intrinsic excitation region can be suppressed through the magnetic transition of permanent magnet nanoparticles. The ferromagnetic transition in magnetic nanoparticles embedded in magnetic nanocomposite thermoelectric materials is attributed to the trapping and release of electrons, which increases the Performance of the thermoelectric materials.

Junta Tachibana - One of the best experts on this subject based on the ideXlab platform.

  • damping factor learning of bp detection with node selection in massive mimo using neural network
    Vehicular Technology Conference, 2020
    Co-Authors: Junta Tachibana, Tomoaki Ohtsuki
    Abstract:

    In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection Performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection Performance deteriorates when there is a difference in the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can reduce the detection Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method can improve the detection Performance compared to the conventional DNN-dBP with the same computational complexity. By computer simulation, it is shown that the proposed method significantly reduces the bit error rate (BER) Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method has the better BER Performance than the conventional DNN-dBP with the same computational complexity.

  • learning and analysis of damping factor in massive mimo detection using bp algorithm with node selection
    IEEE Access, 2020
    Co-Authors: Junta Tachibana, Tomoaki Ohtsuki
    Abstract:

    In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection Performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection Performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection Performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) Performance Deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER Performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.

Jing Shi - One of the best experts on this subject based on the ideXlab platform.

  • Magnetoelectric interaction and transport behaviours in magnetic nanocomposite thermoelectric materials
    Nature Nanotechnology, 2017
    Co-Authors: Wenyu Zhao, Wanting Zhu, Xianli Su, Xinfeng Tang, Qingjie Zhang, Ping Wei, Jihui Yang, Yong Liu, Jing Shi
    Abstract:

    How to suppress the Performance Deterioration of thermoelectric materials in the intrinsic excitation region remains a key challenge. The magnetic transition of permanent magnet nanoparticles from ferromagnetism to paramagnetism provides an effective approach to finding the solution to this challenge. Here, we have designed and prepared magnetic nanocomposite thermoelectric materials consisting of BaFe12O19 nanoparticles and Ba0.3In0.3Co4Sb12 matrix. It was found that the electrical transport behaviours of the nanocomposites are controlled by the magnetic transition of BaFe12O19 nanoparticles from ferromagnetism to paramagnetism. BaFe12O19 nanoparticles trap electrons below the Curie temperature (TC) and release the trapped electrons above the TC, playing an ‘electron repository’ role in maintaining high figure of merit ZT. BaFe12O19 nanoparticles produce two types of magnetoelectric effect—electron spiral motion and magnon-drag thermopower—as well as enhancing phonon scattering. Our work demonstrates that the Performance Deterioration of thermoelectric materials in the intrinsic excitation region can be suppressed through the magnetic transition of permanent magnet nanoparticles.