Mikio Hasegawa

Professor

University of Tokyo

Japan

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Professor Mikio Hasegawa is a faculty member at Tokyo University of Science, Japan. His research focuses on advanced topics in wireless networks, reinforcement learning, and optimization techniques. Recent publications include work on energy-efficient resource allocation in distributed networks and the application of graph neural networks in urban wireless performance modeling.

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Collaborators (10)

Hiroo Sekiya

Chiba University

JAPAN

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Naoki Wakamiya

Osaka University

JAPAN

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Kien Nguyen

Associate Professor

Chiba University

JAPAN

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Takatomo Mihana

Assistant professor

University of Tokyo

JAPAN

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Makoto Naruse

Professor

University of Tokyo

JAPAN

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SeonGeun Jeong

Pusan National University

SOUTH KOREA

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Maki Arai

University of Tokyo

JAPAN

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Kazuto Yano

Head of Department

Electronics and Telecommunications Research Institute

JAPAN

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André Röhm

JAPAN

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Viet Quoc Pham

Assistant Professor

Trinity College Dublin

IRELAND

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Mikio Hasegawa

Research Interests

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About

Articles (25)

Two-Dimensional Beam Selection by Multiarmed Bandit Algorithm Based on a Quantum Walk

Graph neural network-based multi-metric performance modeling in urban multi-RAT wireless networks

Quantum-Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks

Biological mimesis based on the WiBIC platform: Modeling of Pavlovian conditioning

Fast beam tracking in Massive MIMO by Laser Chaos Decision Maker

Quantum Annealing-Based Sum Rate Maximization for Multi-UAV-Aided Wireless Networks

An efficient beaconing of bluetooth low energy by decision making algorithm

Game Theory Approach for Autonomous Mobility-Assisted Piggyback Network with mm-Wave Links

VT-MCNet: High-Accuracy Automatic Modulation Classification Model Based on Vision Transformer

Performance Evaluation of Asynchronous Pulse Code Multiple Access in Massive IoT Networks

Primary channel selection in dynamic channel bonding using laser chaos decision maker

Ultrafast Resource Allocation by Parallel Bandit Architecture Using Chaotic Lasers for Downlink NOMA Systems

Enhancing Data Rate of Asynchronous Pulse Code Multiple Access for Massive IoT

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices

Design and Implementation of Decentralized TDMA for Low Power IoT Devices Based on Desynchronization of Nonlinear Oscillators

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement

An implementation and evaluation of MPTCP-based IoT router

Applying reinforcement learning algorithms to ground station selection in satellite-terrestrial optical communication

Paper Applying reinforcement learning algorithms to ground station selection in satellite-terrestrial optical communication

Design and Implementation of SF Selection Based on Distance and SNR Using Autonomous Distributed Reinforcement Learning in LoRa Networks

High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems

UWB NLOS/LOS Classification Using Hybrid Quantum Convolutional Neural Networks

Collaborators (10)

Hiroo Sekiya

Naoki Wakamiya

Kien Nguyen

Takatomo Mihana

Makoto Naruse

SeonGeun Jeong

Maki Arai

Kazuto Yano

André Röhm

Viet Quoc Pham

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