arXiv subjects

Masayuki Imanishi

Publications and source records attributed to Masayuki Imanishi.

4 recordsLinked to original sources

arXiv2022

Determination of the electron trap level in Fe-doped GaN by phonon-assisted conduction phenomenon

We acoustically measured the energy level for thermally activated conduction (TAC) in high-resistivity Fe-doped GaN using the non-contacting antenna-transmission acoustic-resonance method. The acoustic attenuation takes a maximum at a specific temperature, where the TAC is accelerated with the help of phonon energy. The Debye type relaxation is thus observed for acoustic attenuation, and its activation energy (0.54$\pm$0.04 eV) was determined with attenuation measurements at various frequencies and temperatures. This value agrees with the E3 level in GaN, indicating that thermally associated conduction originates from the E3 trap level. We also measured the five independent elastic constants at high temperatures.

physics.app-ph↗

arXiv2019

Trainable Projected Gradient Detector for Massive Overloaded MIMO Channels: Data-driven Tuning Approach

This paper presents a deep learning-aided iterative detection algorithm for massive overloaded multiple-input multiple-output (MIMO) systems where the number of transmit antennas $n$ is larger than that of receive antennas $m$. Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named the trainable projected gradient-detector (TPG-detector). The trainable internal parameters, such as the step-size parameter, can be optimized with standard deep learning techniques, i.e., the back propagation and stochastic gradient descent algorithms. This approach is referred to as data-driven tuning, and ensures fast convergence during parameter estimation in the proposed scheme. The TPG-detector mainly consists of matrix-vector product operations whose computational cost is proportional to $m n$ for each iteration. In addition, the number of trainable parameters in the TPG-detector is independent of the number of antennas. These features of the TPG-detector result in a fast and stable training process and reasonable scalability for large systems. Numerical simulations show that the proposed detector achieves a comparable detection performance to those of existing algorithms for massive overloaded MIMO channels, e.g., the state-of-the-art IW-SOAV detector, with a lower computation cost.

cs.IT↗

arXiv2018

Deep Learning-Aided Projected Gradient Detector for Massive Overloaded MIMO Channels

The paper presents a deep learning-aided iterative detection algorithm for massive overloaded MIMO systems. Since the proposed algorithm is based on the projected gradient descent method with trainable parameters, it is named as trainable projected descent-detector (TPG-detector). The trainable internal parameters can be optimized with standard deep learning techniques such as back propagation and stochastic gradient descent algorithms. This approach referred to as data-driven tuning brings notable advantages of the proposed scheme such as fast convergence. The numerical experiments show that TPG-detector achieves comparable detection performance to those of the known algorithms for massive overloaded MIMO channels with lower computation cost.

cs.IT↗

arXiv2017

Proceedings of Workshop AEW10: Concepts in Information Theory and Communications

The 10th Asia-Europe workshop in "Concepts in Information Theory and Communications" AEW10 was held in Boppard, Germany on June 21-23, 2017. It is based on a longstanding cooperation between Asian and European scientists. The first workshop was held in Eindhoven, the Netherlands in 1989. The idea of the workshop is threefold: 1) to improve the communication between the scientist in the different parts of the world; 2) to exchange knowledge and ideas; and 3) to pay a tribute to a well respected and special scientist.

cs.IT↗