Ph.D., Keio University, Mar. 2020

Internet of Things (Resource Allocation, Edge Computing, MAC Protocols)

Multiplexing Techniques (NOMA, Pulse, etc.)

Quantum Computer (Quantum Annealing)

Artificial Intelligence (Machine Learning, Deep Learning)

Cognitive Radio Networks (Spectrum Prediction, Channel Selection, Control Channel Establishment, Routing Selection)

Laser Chaos, Quantum Computing, Game Theory

Manufacturing technology (mechanical, electrical/electronic, chemical engineering), Communication and network engineering

Informatics, Information networks

Nov. 2022 - Present
Tokyo University of Science, Graduate School of Engineering, Visiting Researcher

Apr. 2022 - Present
The University of Electro-Communications, Graduate School of Informatics and Engineering, Assistant Professor

Apr. 2020 - Mar. 2022
Tokyo University of Science, Faculty of Engineering Electrical Engineering, Assistant Professor

Apr. 2017 - Mar. 2020
Keio University, Graduate School of Science and Technology, Department of Information and Computer Science

Mar. 2024 - Mar. 2026
Career Education Committee Member, The University of Electro-Communications

May 2024 - Aug. 2024
Technical Program Committee, IEEE/CIC ICCC (International Conference on Communications in China)

Jan. 2023 - Jun. 2023
Technical Program Committee, The 2023 IEEE 97th Vehicular Technology Conference

Jan. 2023 - Jun. 2023
Technical Program Committee, IEEE International Conference on Communications

Dec. 2022 - Dec. 2022
Publicity Chair, The 12th International Conference on Smart Computing, Networking and Services (SmartCNS-2022)

Jan. 2022 - Oct. 2022
Technical Program Committee, 19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

Sep. 2022 - Sep. 2022
Technical Program Committee, The 2022 IEEE 96th Vehicular Technology Conference

Aug. 2022 - Aug. 2022
Track Chair, IEEE PRAI (2022 the 5th International Conference on Pattern Recognition and Artificial Intelligence)

Aug. 2022 - Aug. 2022
Technical Program Committee, The 19th Annual International Conference on Privacy, Security & Trust (PST 2022)

Aug. 2022 - Aug. 2022
Technical Program Committee, IEEE/CIC ICCC (International Conference on Communications in China)

Jun. 2022 - Jun. 2022
Technical Program Committee, EuCNC (European Conference on Networks and Communications)&6G Summit

Jun. 2022 - Jun. 2022
Technical Program Committee, The 2022 IEEE 95th Vehicular Technology Conference

Aug. 2021 - Oct. 2021
Technical Program Committee, WCSP'21 (2021 International Conference on Wireless Communications and Signal Processing)

Sep. 2020 - Oct. 2021
Technical Program Committee mMember, IEEE ATC 2021 ( The 18th IEEE International Conference on Advanced and Trusted Computing)

Aug. 2021 - Sep. 2021
Technical Program Committee, IEEE VTC2021-Fall, Machine Learning and AI for Communications/Recent Results and Workshops

Mar. 2021 - Jul. 2021
Technical Program Committee, IEEE/CIC ICCC (International Conference on Communications in China)

Mar. 2021 - Jun. 2021
Technical Program Committee, IEEE IWCMC (17th International Wireless Communications & Mobile Computing Conference), E-Health Symposium

Jun. 2020 - Dec. 2020
Technical Program Committee Member, IEEE Global Communications Conference (GLOBECOM), SAC-Internet of Things & Smart Connected Communities,

Mar. 2020 - Jun. 2020
Technical Program Committee Member, IEEE/CIC ICCC (International Conference on Communications in China)

Mar. 2020 - Jun. 2020
Technical Program Committee Member, IEEE 16th International Wireless Communications & Mobile Computing Conference (IWCMC), E-Health Symposium,

Jan. 2020 - May 2020
Technical Program Committee Member, IEEE Vehicular Technology Conference (VTC) Spring, Track: Radio Access Technology and Heterogeneous Networks ,

Mar. 2019 - Jun. 2019
Technical Program Committee Member, IEEE 15th International Wireless Communications & Mobile Computing Conference (IWCMC), E-Health Symposium,

Apr. 2021
IEEE International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)
High-Speed Optimization of User Pairing in NOMA System Using Laser Chaos Based MAB Algorithm
Excellent Paper Award

Mar. 2021
The Telecommunications Advancement Foundation
Multiple radios for fast rendezvous in heterogeneous cognitive radio networks
Excellent Paper Award, Telecom System Technology Student Award

Aug. 2014
IEEE 9th International Conference on Communications and Networking in China,
Coalition graph game for multi-hop routing path selection in Cooperative Cognitive Radio Networks
Best Paper Award, Aohan Li, Xin Guan, Ziheng Yang, and Tomoaki Ohtsuki

An Autonomous and DistribuFully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learningted Transmission Parameters Selection Method Using Deep Reinforcement Learning in Mobile LoRa Networks
S. Sugiyama; K. Makizoe; M. Arai; M. Hasegawa; T. Otsuki; A. Li
IEEE VTC2024-Spring (The 2024 IEEE 99th Vehicular Technology Conference ), 1-5, Jun. 2024

An Efficient Beaconing of Bluetooth Low Energy by Decision Making ALgorithm
M. Fujisawa; H. Yasuda; R. Isogai; Y. Yoshida; M. Arai; A. Li; S. Kim; M. Hasegawa
Discover Artificial Intelligence, 1-20, Apr. 2024

Data Augmentation and Individual Identification Method for Emitters Using Contour Stella Image Mapping
G. Han; W. Wang; Z. Xu; A. Li
IEEE Transactions on Cognitive Communications and Networking, 1-11, Mar. 2024

Ultrafast Resource Allocation by Parallel Bandit Architecture Using Chaotic Lasers for Downlink NOMA Systems
M. Sugiyama; T. Mihana; A. Li; M. Naruse; M. Hasegawa
IEEE Access, Jan. 2024

Performance Evaluation of Resource Allocation Optimization in UAV Network with Ising Machine
T. Fujita; A. Li; Q. V. Do; T. Otsuka; S.-G. Jeong; W.-J. Hwang; H. Takesue; K. Inaba; K. Aihara; M. Hasegawa
The 10th Japan-Korea Joint Workshop on Complex Communication Science, Jan. 2024

Ultrafast channel allocation by a Parallel Laser Chaos Decision-Maker for Downlink NOMA Systems
M. Sugiyama; A. Li; M. Arai; T. Mihana; M. Hasegawa
The 10th Japan-Korea Joint Workshop on Complex Communication Science, Jan. 2024

Source Location Privacy Protection Algorithm Based on Polyhedral Phantom Routing in Underwater Acoustic Sensor Networks
G. Han; R. Xia; H. Wang; A. Li
IEEE Internet of Things Journal, 1-14, Sep. 2023

Latency Minimization in Wireless-Powered Federated Learning Networks with NOMA
M. Alishahi; P. Fortier; M. Zeng; F. Fang; A. Li
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC), 1-5, Sep. 2023

Design and Implementation of MAB Based Power Consumption Optimization Method on Bluetooth Low Energy
M. Fujisawa; H. Yasuda; R. Isogai; Y. Yoshida; A. Li; S. Kim; M. Hasegawa
IEICE The 2023 International Symposium on Nonlinear Theory and Its Applications (IEICE NOLTA2023), 1-4, Sep. 2023

Resource Allocation for Large Scale UAV Networks Using Coherent Ising Machine
T. Fujita; A. Li; Q. Do; S. Jeong; W. Hwang; H. Takesue; K. Inaba; K. Aihara; M. Hasegawa
IEICE The 2023 International Symposium on Nonlinear Theory and Its Applications (IEICE NOLTA2023), 1-4, Sep. 2023

Ultrafast channel allocation in downlink NOMA using a parallel array of laser chaos decision-makers
M. Sugiyama; A. Li; M. Naruse; M. Hasegawa
IEICE The 2023 International Symposium on Nonlinear Theory and Its Applications (IEICE NOLTA2023), 1-4, Sep. 2023

An Intelligent Multi-Local Model Bearing Fault Diagnosis Method Using Small Sample Fusion
X. Zhou; A. Li; G. Han
Sensors, Aug. 2023

A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks
Y. He; G. Han; A. Li; T. Taleb; C. Wang; H. Yu
IEEE Transactions on Mobile Computing, Aug. 2023

High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems
T. Otsuka; A. Li; H. Takese; K. Inaba; K. Aihara; M. Hasegawa
IEEE Transactions on Vehicular Technology, 1-18, Jul. 2023

Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices
I. Urabe; A. Li; M. Fujisawa; S.-J. Kim; M. Hasegawa
Sensors, 1-22, Jul. 2023

A Backbone Network Construction-Based Multi-AUV Collaboration Source Location Privacy Protection Algorithm in UASNs
H. Wang; G. Han; A. Gong; A. Li; Y. Hou
IEEE Internet of Things Journal, 1-12, May 2023

QoS-driven distributed cooperative data offloading and heterogeneous resource scheduling for IIoT
F. Zhang; G. Han; A. Li; C. Lin; L. Liu
IEEE Internet of Things Magazine, 1-9, Apr. 2023

Design and Implementation of Decentralized TDMA for Low Power IoT Devices
T. Osada; H. Yasuda; A. Li; S.-J. Kim; M. Hasegawa
The 5th International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC 2023), 1-5, Feb. 2023

High-Speed Optimization of NOMA System Using Coherent Ising Machine in Dynamic Environment
T. Otsuka; A. Li; H. Takesue; K. Inaba; K. Aihara; M. Hasegawa
2023 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'23), 1-4, Feb. 2023

Controversy-Adjudication-Based Trust Management Mechanism in the Internet of Underwater Things
Jinfang Jiang; Shanshan Hua; Guangjie Han; Aohan Li; Chuan Lin
IEEE Internet of Things Journal, 10, 3, 2603-2614, Feb. 2023

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement
Naoki Fujita; André Röhm; Takatomo Mihana; Ryoichi Horisaki; Aohan Li; Mikio Hasegawa; Makoto Naruse
Entropy, MDPI AG, 25, 1, 146-146, 11 Jan. 2023, Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology, economics, and other fields. In our previous study, we proposed an efficient method to infer the underlying compatibilities among the entities, under the constraint that only the total compatibility is observable. Furthermore, by transforming the pairing problem into a traveling salesman problem with a multi-layer architecture, a pairing optimization algorithm was successfully demonstrated to derive a high-total-compatibility pairing. However, there is substantial room for further performance enhancement by further exploiting the underlying mathematical properties. In this study, we prove the existence of algebraic structures in the pairing problem. We transform the initially estimated compatibility information into an equivalent form where the variance of the individual compatibilities is minimized. We then demonstrate that the total compatibility obtained when using the heuristic pairing algorithm on the transformed problem is significantly higher compared to the previous method. With this improved perspective on the pairing problem using fundamental mathematical properties, we can contribute to practical applications such as wireless communications beyond 5G, where efficient pairing is of critical importance. As the pairing problem is a special case of the maximum weighted matching problem, our findings may also have implications for other algorithms on fully connected graphs.

Experimental Evaluation of SF-Channel Selection Based on Autonomous Distributed Reinforcement Learning for LoRaWAN Devices
I. Urabe; M. Fujisawa; A. Li; S.-J Kim; M. Hasegawa
The 9th Japan-Korea Joint Workshop on Complex Communication Sciences, 1-1, Jan. 2023

Scalable Channel Allocation in Downlink NOMA Using Parallel Array of Laser Chaos Decision-Maker
M. Sugiyama; A. Li; M. Naruse; M. Hasegawa
The 37th International Conference on Information Networking (ICOIN 2023), 1-6, Jan. 2023

UAV data delivery and routing optimization in Piggyback Network
So Hasegawa; Kazuki Kuwata; Aohan Li; Yoshito Watanabe; Yozo Shoji; Mikio Hasegawa
Nonlinear Theory and Its Applications, IEICE, 14, 1, 66-77, Jan. 2023

A Deep-Learning-Based Fault Diagnosis Method of Industrial Bearings Using Multi-Source Information
Xiaolu Wang; Aohan Li; Guangjie Han
Applied Sciences, 13, 2, 933-933, Jan. 2023

AUV-Assisted Stratified Source Location Privacy Protection Scheme based on Network Coding in UASNs
Hao Wang; Guangjie Han; Yulin Liu; Aohan Li; Jinfang Jiang
IEEE Internet of Things Journal, 1-13, Jan. 2023

An efficient observation algorithm that achieves the minimum number of measurements for pairing optimization
N. Fujita; A. Rohm; T. Mihana; R. Horisaki; A. Li; M. Hasegawa; M. Naruse
IEICE The 2022 International Symposium on Nonlinear Theory and Its Applications (IEICE NOLTA2022), 1-4, Dec. 2022

Fast Resource Allocation for NOMA System Using Coherent Ising Machine
T. Otsuka; A. Li; H. Takesue; K. Inaba; K. Aihara; M. Hasegawa
IEICE The 2022 International Symposium on Nonlinear Theory and Its Applications (IEICE NOLTA2022), 1-4, Dec. 2022

Uplink Grant-Free NOMA Using Laser Chaos Decision Maker
A. Li; Z. Duan; M. Naruse; M. Hasegawa
IEICE NOLTA2022 (IEICE The 2022 International Symposium on Nonlinear Theory and Its Applications), 1-4, Dec. 2022

Design and Implementation of SF Selection Based on Distance and SNR Using Autonomous Distributed Reinforcement Learning in LoRa Networks
I. Urabe; A. Li; S.-J. Kim; Mikio Hasegawa
4th EAI International Conference on Artificial Intelligence for Communications and Networks, 1-8, Nov. 2022

Deep Reinforcement Learning Based Resource Allocation for LoRaWAN
A. Li
IEEE VTC2022-Fall (IEEE 96th Vehicular Technology Conference), 1-4, Sep. 2022

Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks
So Hasegawa; Ryoma Kitagawa; Aohan Li; Song Ju Kim; Yoshito Watanabe; Yozo Shoji; Mikio Hasegawa
Applied Sciences (Switzerland), 12, 15, 7424-7424, Aug. 2022, In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.

High-Speed Resource Allocation Optimization of NOMA System via Coherent Ising Machine
T. Otsuka; A. Li; H. Takesue; K. Inaba; K. Aihara; M. Hasegawa
The 18th International Conference on Multimedia Information Technology and Applications (MITA 2022), 1-1, Jul. 2022

Performance Evaluation of Reinforcement Learning Based Distributed Channel Selection Algorithm in Massive IoT Networks
Daisuke Yamamoto; Honami Furukawa; Aohan Li; Yusuke Ito; Koya Sato; Koji Oshima; So Hasegawa; Yoshito Watanabe; Yozo Shoji; Song Ju Kim; Mikio Hasegawa
IEEE Access, 10, 67870-67882, Jun. 2022, In recent years, the demand for new applications using various Internet of Things (IoT) devices has led to an increase in the number of devices connected to wireless networks. However, owing to the limitation of available frequency resources for IoT devices, the degradation of the communication quality caused by channel congestion is a practical problem in developing IoT technology. Many IoT devices have hardware and software limitations that prevent centralized channel allocation, and congestion is even more severe in massive IoT networks without a central controller. Therefore, developing a distributed and sophisticated channel selection algorithm is necessary. In previous studies, the channel selection of each IoT device was modeled as a multi-armed bandit (MAB) problem, and a wireless channel selection method based on the MAB algorithm, which is a simple reinforcement learning, was proposed. In particular, it has been shown that the MAB algorithm of tug-of-war (TOW) dynamics can efficiently select channels with much lower computational complexity and power compared with other reinforcement learning-based channel-selection methods. This paper proposes a distributed channel selection method based on TOW dynamics in fully decentralized networks. We evaluate the effectiveness of the proposed method and other distributed channel-selection methods on the communication success rate in massive IoT networks by experiments and simulations. The results show that the proposed method improves the communication success rate more than other distributed channel selection methods even in a dense and dynamic network environment.

Efficient Pairing in Unknown Environments: Minimal Observations and TSP-Based Optimization
Naoki Fujita; Nicolas Chauvet; Andre Rohm; Ryoichi Horisaki; Aohan Li; Mikio Hasegawa; Makoto Naruse
IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 10, 57630-57640, May 2022

BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos Decision-Maker
M. Sugiyama; A. Li; Z. Duan; M. Naruse; M. Hasegawa
Electronics, 11, 9, 1452, 30 Apr. 2022

A Motor Fault Diagnosis Method Based on Industrial Wireless Sensor Networks
X. Wang; A. Li; G. Han; Y. Cui
Journal of Computers, 33, 2, 127-136, Apr. 2022

A Localization Method Based on Partial Correlation Analysis for Dynamic Wireless Network,
Y. Horiguchi; Y. Ito; A. Li; M. Hasegawa
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (EA), E105.A, 3, 594-597, Mar. 2022

BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos-Based MAB Algorithm
M. Sugiyama; A. Li; Z. Duan; M. Naruse; M. Hasegawa
IEEE International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC), 1-6, Feb. 2022

User Pairing Using Laser Chaos Decision Maker for NOMA Systems
Z. Duan; A. Li; N. Okada; Y. Ito; N. Chauvet; M. Naruse; M. Hasegawa
IEICE Nonlinear Theory and Its Applications (NOLTA), Institute of Electronics, Information and Communications Engineers (IEICE), E13-N, 1, 72-83, Jan. 2022

Dynamic Channel Bonding in WLANs by Hierarchical Laser Chaos Decision Maker
H. Kanemase; A. Li; Y. Ito; N. Chauvet; M. Naruse; M. Hasegawa
IEICE Nonlinear Theory and Its Applications (NOLTA), Institute of Electronics, Information and Communications Engineers (IEICE), E13-N, 1, 84-100, Jan. 2022

A Lightweight Decentralized Reinforcement Learning Based Channel Selection Approach for High-Density LoRaWAN
A. Li; M. Fujisawa; I Urabe; R. Kitagawa; S. Kim; M. Hasegawa
IEEE DySPAN (IEEE International Symposium on Dynamic Spectrum Access Networks), 9-14, Dec. 2021

Proposal of efficient algorithms for large scale pairing
N. Fujita; N. Chauve; A. Rohm; R. Horisaki; A. Li; M. Hasegawa; M. Naruse
IEICE International Conference on Emerging Technologies for Communications (IEICE ICETC), 1-4, Dec. 2021

High-Density Resource-Restricted Pulse-Based IoT Networks
F. Peper; K. Leibnitz; C. Tanaka; K. Honda; M. Hasegawa; K. Theofilis; A. Li; N. Wakamiya
IEEE Transactions on Green Communications and Networking, Institute of Electrical and Electronics Engineers (IEEE), 5, 4, 1856-1868, Dec. 2021

Design and Implementation of Pulse-Based Protocol with Chirp Spread Spectrum for Massive IoT
K. Honda; F. Peper; A. Nakamura; A. Li; Y. Ito; K. Leibnitz; K. Theofilis; N. Wakamiya; M. Hasegawa
IEEE The 20th International Symposium on Communications and Information Technologies (ISCIT), 1-4, Oct. 2021

Locating false data injection attacks on smart grids using D-FACTS devices
B. Li; Q. Du; J. Song; A. Li; X. Ma
Springer The 19th International Conference on Service-Oriented Computing (ICSOC), 1-15, Oct. 2021

Performance evaluation of pulse-based multiplexing protocol implemented on massive IoT devices
Chiemi Tanaka; Kentaro Honda; Aohan Li; Ferdinand Peper; Kenji Leibnitz; Konstantinos Theofilis; Naoki Wakamiya; Mikio Hasegawa
Nonlinear Theory and Its Applications, IEICE, Institute of Electronics, Information and Communications Engineers (IEICE), 12, 4, 726-737, Oct. 2021

A High-Speed Channel Assignment Algorithm for Dense IEEE 802.11 Systems via Coherent Ising Machine
Komei Kurasawa; Kota Hashimoto; Aohan Li; Koya Sato; Kensuke Inaba; Hiroki Takesue; Kazuyuki Aihara; Mikio Hasegawa
IEEE Wireless Communications Letters, Institute of Electrical and Electronics Engineers (IEEE), 10, 8, 1682-1686, Aug. 2021

Experimental Evaluation of Reinforcement Learning Methods Based Channel Selection in Distributed Heterogeneous IoT Systems
R. Kitagawa; A. Li; Y. Ito; M. Hasegawa; S. Hasegawa; S. Kim
The 17th International Conference on Multimedia Information Technology and Applications (MITA 2021), 1-1, Jul. 2021

Performance Evaluation of High-Speed Channel Assignment in Dense Wireless Wireless LANs by Coherent Ising Machine
K. Hashimoto; K. Kurasawa; Y. Ito; A. Li; M. Hasegawa; K. Inaba; H. Takesue; K. Aihara
The 17th International Conference on Multimedia Information Technology and Applications (MITA 2021), 1-1, Jul. 2021

A reinforcement learning based collision avoidance mechanism to superposed LoRa signals in distributed massive IoT systems
Takuma Onishi; Aohan Li; Song-Ju Kim; Mikio Hasegawa
IEICE Communications Express, Institute of Electronics, Information and Communications Engineers (IEICE), 10, 5, 289-294, 01 May 2021

Coherent Ising Machine Based Optimal Channel Allocation and User Pairing in NOMA Networks
T. Otsuka; K. Kurasawa; Z. Duan; A. Li; K. Sato; H. Takesue; K. Aihara; K. Inaba; M. Hasegawa
IEEE International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC), 1-4, Apr. 2021

High-speed Optimization of User Pairing in NOMA System Using Laser Chaos Based MAB Algorithm
Z. Duan; N. Okada; A. Li; M. Naruse; N. Chauvet; M. Hasegawa
IEEE International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC), 1-5, Apr. 2021

Dynamic Channel Bonding Using Laser Chaos Decision Maker in WLANs
H. Kanemasa; A. Li; M. Naruse; N. Chauvet; M. Hasegawa
IEEE International Conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC), 1-5, Apr. 2021

Analysis on Effectiveness of Surrogate Data-Based Laser Chaos Decision Maker
Norihiro Okada; Mikio Hasegawa; Nicolas Chauvet; Aohan Li; Makoto Naruse
Complexity, Hindawi Limited, 2021, 1-9, 26 Feb. 2021, The laser chaos decision maker has been demonstrated to enable ultra-high-speed solutions of multiarmed bandit problems or decision-making in the GHz order. However, the underlying mechanisms are not well understood. In this paper, we analyze the chaotic dynamics inherent in experimentally observed laser chaos time series via surrogate data and further accelerate the decision-making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision-making detail. Then, we analyze the decision-making ability using three different surrogate chaos time series to examine the underlying mechanism. We clarify that the negative autocorrelation of laser chaos improves decision-making and that the amplitude distribution of the original laser chaos time series is not optimal. Hence, we introduce a new parameter for adjusting the amplitude distribution of the laser chaos to enhance the decision-making performance. This study provides a new insight into exploiting the supremacy of chaotic dynamics in artificially constructed intelligent systems.

Piggy-back Network to enable beyond 5G Society supported by Autonomous Mobilities: Evaluation of End-to-End Throughput on Optimized Piggy-back Networks
Kazuki Kuwata; Yozo Shoji; Mikio Hasegawa; Yusuke Ito; Yoshito Watanabe; Aohan Li; So Hasegawa
International Symposium on Wireless Personal Multimedia Communications, WPMC, 2021-December, 1-5, 2021, As one of the technologies to enable Beyond 5G society, a Piggy-back Network has been proposed as a communication system based on Store-Carry-Forwarding with high-speed millimeter-wave links. In this paper, we evaluate the end-to-end throughput of optimized Piggy-back Networks. We formulate optimization of the data transfer route in the Piggy-back Network as a pickup and delivery problem and apply a heuristic optimization algorithm to the formulated problem. The results show that the optimized Piggy-back Network enables high throughput even for long-distance communication.

A Channel Selection Algorithm Using Reinforcement Learning for Mobile Devices in Massive IoT System
H. Furukawa; A. Li; Y. Shoji; Y. Watanabe; S. Kim; K. Sato; Y. Andreopoulos; M. Hasegawa
IEEE Consumer Communications & Networking Conference (IEEE CCNC), 1-2, Jan. 2021, It is necessary to develop an efficient channel selection method with low power consumption to achieve high communication quality for distributed massive IoT system. To this end, Ma et al. [1] proposed an autonomous distributed channel selection method based on the Tug-of-War (ToW) dynamics. The ToW-based method can achieve equivalent performance to UCB1-tuned [2], [3] with low computational complexity and power consumption, which is recognized as a best practice technique for solving multi-armed bandit (MAB) problems. However, Ref. [1] only considered fixed IoT devices with simplex communication.

Implementation and Experimental Evaluation of A Reinforcement Learning Based Channel Selection on A Mobile IoT System
H. Furukawa; A. Li; Y. Shoji; Y. Watanabe; S. Kim; K. Sato; Y. Andreopoulos; M. Hasegawa
IEICE International Conference on Emerging Technologies for Communications (IEICE ICETC), 1-1, Dec. 2020

On High-Density Resource-Restricted Puls-Based IoT Networks
F. Peper; K. Leibnitz; K. Theofilis; M. Hasegawa; N. Wakamiya; C. Tanaka; J. Teramae; S. Sekizawa; A. Li
IEEE Global Communications Conference (IEEE GLOBECOM), Institute of Electrical and Electronics Engineers (IEEE), 1-6, Dec. 2020

Implementation of Pulse-based Multiplexing Protocol for Massive IoT
C. Tanaka; A. Li; F. Peper; K. Leibnitz; K. Theofilis; N. Wakamiya; M. Hasegawa
The 2020 International Symposium on Nonlinear Theory and Its Applications (NOLTA2020), 1-4, Nov. 2020

ReAL: A New ResNet-ALSTM Based Intrusion Detection System for the Internet of Energy
J. Song; B. Li; Y. Wu; Y. Shi; A. Li
IEEE 45th Conference on Local Computer Networks (IEEE LCN), 1-6, Nov. 2020

A Fast Blind Scheme With Full Rendezvous Diversity for Heterogeneous Cognitive Radio Networks
Aohan Li; Guangjie Han; Tomoaki Ohtsuki
IEEE Transactions on Cognitive Communications and Networking, Institute of Electrical and Electronics Engineers (IEEE), 5, 3, 805-818, Sep. 2019

Full-Duplex-Based Control Channel Establishment for Cognitive Internet of Things
Aohan Li; Guangjie Han
IEEE Communications Magazine, Institute of Electrical and Electronics Engineers (IEEE), 57, 3, 70-75, Mar. 2019

Multiple Radios for Fast Rendezvous in Heterogeneous Cognitive Radio Networks
Aohan Li; Guangjie Han; Tomoaki Ohtsuki
IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 7, 37342-37359, Mar. 2019

Learning-Based Optimal Channel Selection in the Presence of Jammer for Cognitive Radio Networks
Aohan Li; Fereidoun H. Panahi; Tomoaki Ohtsuki; Guangjie Han
IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), IEEE, 1-6, Dec. 2018, Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

Enhanced Channel Hopping Algorithm for Heterogeneous Cognitive Radio Networks
Aohan Li; Guangjie Han; Tomoaki Ohtsuki
IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), IEEE, 1-6, Dec. 2018, In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

SSL: Smart Street Lamp Based on Fog Computing for Smarter Cities
Gangyong Jia; Guangjie Han; Aohan Li; Jiaxin Du
IEEE Transactions on Industrial Informatics, Institute of Electrical and Electronics Engineers (IEEE), 14, 11, 4995-5004, Nov. 2018

A fairness-based MAC protocol for 5G Cognitive Radio Ad Hoc Networks
Aohan Li; Guangjie Han
Journal of Network and Computer Applications, Elsevier BV, 111, 28-34, Jun. 2018

Coordinate Channel-Aware Page Mapping Policy and Memory Scheduling for Reducing Memory Interference Among Multimedia Applications
Gangyong Jia; Guangjie Han; Aohan Li; Jaime Lloret
IEEE Systems Journal, Institute of Electrical and Electronics Engineers (IEEE), 11, 4, 2839-2851, Dec. 2017

Energy-Efficient Channel Hopping Protocol for Cognitive Radio Networks
Aohan Li; Guangjie Han; Tomoaki Ohtsuki
IEEE GLOBECOM 2017 - 2017 IEEE GLOBAL COMMUNICATIONS CONFERENCE, IEEE, 1, 6, Dec. 2017, Channel Hopping (CH) is a representative technique to solve the rendezvous problem for Cognitive Radio Networks (CRNs). Multiple radios technique were utilized in several latest researches on CH owing to the fact that it can significantly reduce the Time-To-Rendezvous (TTR) while the cost of the device is low. However, the radios of one unlicensed Secondary User (SU) may access same channel at the same time for most of the existing multi-radio CH protocols, which is a waste of energy. Moreover, the number of radios for the SUs is implicitly assumed same or must be more than one, which is unrealistic for heterogeneous CRNs. In this paper, an energy-efficient CH protocol, Hybrid Radio Rendezvous (HRR) protocol is proposed to address the above issues. Furthermore, theoretical analysis is presented to derive the upper bound on the Maximum TTR (MTTR) for the HRR protocol. In addition, the theoretical analysis is corroborated by extensive simulations while the simulation results show that the HRR protocol outperforms the state-of-the-art CH protocols in terms of the TTR and the energy efficiency.

Channel Hopping Protocols for Dynamic Spectrum Management in 5G Technology
Aohan Li; Guangjie Han; Joel J. P. C. Rodrigues; Sammy Chan
IEEE Wireless Communications, Institute of Electrical and Electronics Engineers (IEEE), 24, 5, 102-109, Oct. 2017

Distributed DOA Estimation for Arbitrary Topology Structure of Mobile Wireless Sensor Network Using Cognitive Radio
Liangtian Wan; Guangjie Han; Daqiang Zhang; Aohan Li; Naixing Feng
WIRELESS PERSONAL COMMUNICATIONS, SPRINGER, 93, 2, 431-445, Mar. 2017, In order to improve the frequency spectrum availability and evade insecurity frequency range, the cognitive radio is introduced in wireless sensor network (WSN), which constructs the cognitive wireless network (CWN). The dynamic spectrum access (DSA) is used in CWN as the spectrum access scheme. In this paper, sensor nodes of mobile wireless sensor network (MWSN) are deployed based on the prior information of the deployment environment. The idea of CWN is introduced in MWSN. A distributed direction-of-arrival (DOA) estimation algorithm is proposed. The clustering of nodes constructs a sub-NWSN which acts as the sensor array used for DOA estimation. The Fourier domain (FD) root multiple signal classification (root-MUSIC) algorithm is applied for DOA estimation of sub-MWSN with arbitrary topology structure. The weight values of sub-MWSNs can be formulated as a function of the number of nodes, snapshot number and battery capacity of nodes. The total cost spectrum function is achieved finally. The improved performance of distributed FD root-MUSIC algorithm is verified by comparing with the manifold separation technique.

Cooperative Secondary Users Selection in Cognitive Radio Ad Hoc Networks
Aohan Li; Guangjie Han; Lei Shu; Mohsen Guizani
2016 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC), IEEE, 915-920, Sep. 2016, Secondary Users (SUs) have capability to sense available licensed spectrum in Cognitive Radio Networks (CRNs). Hence, SUs can opportunistically access to the licensed spectrum without disturbing Primary Users (PUs). In this paper, a novel network architecture is proposed to reduce the production cost and the energy consumption for CRNs. The proposed network architecture is based on the spectral requirement of Secondary Users (SUs). In the proposed network architecture, only parts of SUs are equipped with Cognitive Radio (CR) module. In addition, a minimum number of SUs are selected to sense available licensed spectrum, which aims at reducing the energy consumption further. The minimum number of SUs selection problem is formulated as a non-linear programming problem under the constrains of energy efficiency and the real-time available spectrum information. However, the non-linear programming problem is a NP-hard problem. Hence, a distributed heuristic algorithm is proposed to calculate the near-optimal solution. The simulation results demonstrate that the proposed heuristic algorithm in the proposed network architecture outperforms the random algorithm in the proposed network architecture and traditional Cognitive Radio Ad Hoc Networks (CRAHNs) in energy efficiency.

A Sensitive Secondary Users Selection Algorithm for Cognitive Radio Ad Hoc Networks
Aohan Li; Guangjie Han; Liangtian Wan; Lei Shu
Sensors, MDPI AG, 16, 4, 445-445, 26 Mar. 2016

An Improved AES Encryption Algorithm Based on Chaos Theory in Wireless Communication Networks
Ziheng Yang; Aohan Li; Lingling Yu; Shijun Kang; Mengjiang Han; Qun Ding
IEEE Third International Conference on Robot, Vision and Signal Processing (RVSP), 1-4, Nov. 2015

Code Synchronization Algorithm Based on Segment Correlation in Spread Spectrum Communication
Aohan Li; Ziheng Yang; Renji Qi; Feng Zhou; Guangjie Han
Algorithms, MDPI AG, 8, 4, 870-894, 09 Oct. 2015

Behavior Aware Data Placement for Improving Cache Line Level Locality in Cloud Computing
Jianjun Wang; Gangyong Jia; Aohan Li; Guangjie Han; Lei Shu
JOURNAL OF INTERNET TECHNOLOGY, LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV, 16, 4, 705-716, Jul. 2015, Due to the VM contention on shared computing resources, especially shared caches, in datacenters, cloud computing paradigm inevitably brings noticeable performance overhead of VMs to customers. Therefore, taking advantage of both spatial and temporal locality to efficiently excavate cache plays an important role in bridging the performance gap between processor cores and main memory. This paper is motivated by two key observations: (1) the access behavior is highly non-uniform and dynamic at the cache line level; (2) neither current spatial nor temporal cache management schemes can efficiently utilize cache capacity for excessively focusing on inter cache line, ignoring the optimization within cache line. Therefore, we propose a novel adaptive scheme, called BADP, which combines task's behavior to place data for improving locality at the cache line level. In the proposed scheme, a cache line level monitor captures the behavior of individual variables accessing and judiciously places variables together with similar behavior so that preventing the underutilized variables in the cache line occupying the valuable cache. The controller decides on the best placement for all variables. Further, our BADP can cooperate with current state-of-the-art cache management schemes.

Coalition Graph Game for Robust Routing in Cooperative Cognitive Radio Networks
Xin Guan; Aohan Li; Zhipeng Cai; Tomoaki Ohtsuki
MOBILE NETWORKS & APPLICATIONS, SPRINGER, 20, 2, 147-156, Apr. 2015, This paper mainly studies on the problem of robust multi-hop routing selection in Cooperative Cognitive Radio Networks (CCRNs). Our objective is to improve the throughput of Primary Users (PUs) while increase the opportunity that Secondary Users (SUs) can access the licensed spectrum. We combine the multi-hop routing selection problem with the graph-based cooperative game and bipartite graph model. A novel effective multi-hop routing selection algorithm called GBRA is proposed for CCRNs. The effect of relays to the routing path on throughput is considered. A novel method is introduced to divide coalition. A fair allocation rule to allocate the total profits of one coalition to its members is also introduced. Finally, based on the proposed coalition division method and the proposed profits allocation scheme in one coalition, the stability of the multi-hop routing path selected by GBRA is proved. Theoretical analysis and performance evaluation show that both PUs and SUs can improve their communication performance when they cooperate with each other.

Dynamic Time-slice Scaling for Addressing OS Problems Incurred by Main Memory DVFS in Intelligent System
Gangyong Jia; Guangjie Han; Jinfang Jiang; Aohan Li
MOBILE NETWORKS & APPLICATIONS, SPRINGER, 20, 2, 157-168, Apr. 2015, Main memory dynamic voltage and frequency scaling (DVFS) has been proposed recently for improving energy efficiency further. However, recent work overlook the operating systems (OS) problems incurred by it, such as unpredictable performance decreasing, unfair performance sharing and priority inversion, which may render performance analysis, optimization and isolation extremely difficult. In this paper, we analyze the OS problems incurred by memory DVFS in detail firstly, and propose dynamic time-slice scaling (DTS) to address these problems, where allocating each thread a time-slice according to threads' memory accessing behavior and memory frequency. Our paper has three main contributions: 1) we analyze the OS problems incurred by the newly approach of memory active low-power modes, the first work paying attention to the effect of up-to-date DVFS memory architecture; 2) performance decrease is more predictable and share is more fairness through adjusting time-slice; 3) priority inversion is solved with starvation forbidden. Simulation results show that the proposed methods can substantially reduce unpredictable performance degradation, improve fairness of performance sharing and solve the priority inversion.

Coalition Graph Game for Multi-hop Routing Path Selection in Cooperative Cognitive Radio Networks
Aohan Li; Xin Guan; Ziheng Yang; Tomoaki Ohtsuki
2014 9TH INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND NETWORKING IN CHINA (CHINACOM), IEEE, 530-534, Aug. 2014, This paper mainly study on the problem of multihop routing path selection in Cooperative Cognitive Radio Network (CCRN). Our objective is to improve the effective throughput of primary users (PUs) while increase the opportunity that secondary users (SUs) can access the licensed spectrum owned by PUs. We combine the multi-hop routing selection problem with the graph-based cooperative game. We propose a multi-hop cooperative routing path selection algorithm called GBRA for CCRN. We consider how to divide coalition. We also propose a fair allocation rule to allocate the total profit of one coalition to its members. Finally, we prove the stability of multi-hop cooperative routing paths which selected by GBRA. Simulation results show the performance of GBRA.

A Double Deep Q Network Based Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications
S. Sugiyama; K. Makizoe; M. Arai; M. Hasegawa; T. Otsuki; A. Li
IEICE Technical Committee on Radio Communication Systems
Jun. 2024
Jun. 2024 Jun. 2024

Transmission Parameters Selection Method Using Reinforcement Learning for Improving Energy Efficiency in Massive IoT Systems
Ryota Ariyoshi; Seiya Sugiyama; Mikio Hasegawa; Tomoaki Otsuki; Aohan Li
IEICE General Conference
Mar. 2024
04 Mar. 2024- 08 Mar. 2024

An Autonomous and Distributed Transmission Parameters Selection Method Using Deep Reinforcement Learning in Mobile LoRa Networks
Seiya Sugiyama; Keigo Makizoe; Maki Arai; Mikio Hasegawa; Tomoaki Otsuki; Aohan Li
IEICE General Conference
Mar. 2024
04 Mar. 2024- 08 Mar. 2024

Investigation of Beam Allocation Methods in Massive MIMO Using High-Speed Optimization by Ising Machines
Shunta Naganuma; Tsukumo Fujita; Maki Arai; Aohan Li; Mikio Hasegawa
IEICE General Conference
Mar. 2024
04 Mar. 2024- 08 Mar. 2024

Primary Channel Selection in Dynamic Channel Bonding Using Ultra-Fast Decision Making of Laser Chaos Decision Maker
Haruto Ando; Aohan Li; Maki Arai; Mikio Hasegawa
IEICE General Conference
Mar. 2024
04 Mar. 2024- 08 Mar. 2024

Transmission Parameters Selection Method Using Reinforcement Learning for Improving Energy Efficiency in Massive IoT Systems
R. Ariyoshi; S. Sugiyama; M. Hasegawa; T. Ohtsuki; A. Li
GlobalNet Workshop 2024 in Hiroshima
Mar. 2024

An Autonomous and Distributed Transmission Parameters Selection Method Using Deep Reinforcement Learning in Mobile LoRa Networks
S. Sugiyama; K. Makizoe; M. Arai; M. Hasegawa; T. Otsuki; A. Li
GlobalNet Workshop 2024 in Hiroshima
Mar. 2024

Investigation of Ultra-Fast Beam Selection Optimization Method Based on Ising Model
S. Naganuma; M. Arai; M. Hasegawa; A. Li; T. Fujiata
IEICE Multiple Innovative Kenkyu-kai Association for Wireless Communications
10 Oct. 2023

Resource allocation optimization in multi-user NOMA systems using higher-order Hamiltonians
S. Ishibashi; T. Otsuka; M. Arai; A. Li; H. Takesue; K. Aihara; M. Hasegawa
IEICE Technical Committee on Complex Communication Sciences (CCS)
04 Aug. 2023

A Study on Resource Allocation Optimization in Multi-User NOMA Networks Based on Higher-Order Hamiltonians
S. Ishibashi; T. Otsuka; A. Li; H. Takesue; K. Aihara; M. Hasegawa
IEICE NOLTA Society Conference
Jun. 2023

A study on optimization of scheduling in intelligent reflecting surface assisted communication using coherent ising machine
Y. Li; T. Otsuka; A. Li; H. Takesue; K. Aihara; M. Hasegawa
IEICE NOLTA Society Conference
Jun. 2023

Scalable Channel Allocation in Downlink NOMA Using Parallel Array of Laser Chaos Decision-Maker
M. Sugiyama; A. Li; M. Naruse; M. Hasegawa
IEICE Technical Committee on Complex Communication Sciences (CCS)
Mar. 2023
Mar. 2023 Mar. 2023

A study on high-speed channel assignment for dynamic NOMA system by laser decision maker
S. Matsuoka; M. Siguyama; A. Li; M. Naruse; M. Hasegawa
IEICE Multiple Innovative Kenkyu-kai Association for Wireless Communications
Oct. 2022
Oct. 2022 Oct. 2022

BER Minimization in Downlink NOMA by Laser Chaos Decision-Maker Based User Pairing
M. Sugiyama; A. Li; Z. Duan; M. Naruse; M. Hasegawa
IEICE NOLTA Society Conference
Jun. 2022
Jun. 2022 Jun. 2022

Implementation and Experimental Evaluation of a Distributed Reinforcement Learning Based Channel and SF Selection Method for LoRa Devices
I. Urabe; M. Fujisawa; A. Li; Y. Ito; S-J. Kim; M. Hasegawa
電子情報通信学会NOLTAソサイエティ大会
Jun. 2022
Jun. 2022 Jun. 2022

Application of pairing optimization algorithm to non-orthogonal multiple access
N. Fujita; N. Chauve; A. Roehm; T. Mihana; R. Horisaki; A. Li; M. Hasegawa; M. Naruse
電子情報通信学会複雑コミュニケーションサイエンス研究会 (CCS)
Jun. 2022
Jun. 2022 Jun. 2022

Transformation of Pairing Optimization into Traveling Salesman Problem
N. Fujita; N. Chauve; A. Röhm; H. Ryoichim; A. Li; M. Hasegawa; M. Naruse
IEICE General Conference
Mar. 2022
Mar. 2022

Optimization of User Pairing in NOMA Systems Using Laser Chaos Decision Maker
M. Sugiyama; A. Li; Z. Duan; M. Naruse; M. Hasegawa
IEICE Multiple Innovative Kenkyu-kai Association for Wireless Communications
29 Oct. 2021
27 Oct. 2021- 29 Oct. 2021

Implementation and experimental evaluation of MAB-based channel selection algorithm for LoRa devices
M. Fujisawa; A. Li; I. Urabe; R. Kitagawa; Y. Ito; H. Yasuda; S. Kim; M. Hasegawa
IEICE Multiple Innovative Kenkyu-kai Association for Wireless Communications
29 Oct. 2021
27 Oct. 2021- 29 Oct. 2021

Cross layer optimization using machine learning in long distance space communications
Atsuhiro Yumoto; Koji Oshima; Yusuke Ito; Aohan Li; Mikio Hasegawa
IEICE Multiple Innovative Kenkyu-kai Association for Wireless Communications
28 Oct. 2021
27 Oct. 2021- 29 Oct. 2021

Proposal of Search Reduction Algorithm for Non-Orthogonal Multiple Access
N. Fujita; N. Chauvet; A. Röhm; H. Ryoichim; A. Li; M. Hasegawa; M. Naruse
IEICE Society Conference
14 Sep. 2021
14 Sep. 2021- 17 Sep. 2021

A Study on Vehicle Allocation and Routing Optimization Methods in Piggy-back Network
K. Kuwata; Y. Ito; A. Li; Y. Shoji; Y. Watanabe; S. Hasegawa; M. Hasegawa
IEICE NOLTA Society Conference
12 Jun. 2021
12 Jun. 2021- 12 Jun. 2021

Performance Evaluation of Distributed Channel Selection Algorithm Based on Reinforcement Learning for Massive Mobile IoT Systems
D. Yamamoto; H. Furukawa; Y. Ito; A. Li; S. Kim; M. Hasegawa
IEICE Technical Committee on Smart Radio
20 May 2021
20 May 2021- 21 May 2021

Applying Tug-of-War Dynamics to Dynamic Competitive Multi-Armed Bandit Problems
I. Urabe; A. Li; Y. Ito; S. Kim; S. Hasegawa; M. Hasegawa
IEICE General Conference
09 Mar. 2021
09 Mar. 2021- 12 Mar. 2021

Experimental Evaluation of Reinforcement Leaning Methods Based Channel Selection in Distributed Heterogeneous IoT Systems
R. Kitagawa; S. Hasegawa; A. Li; S. Kim; M. Hasegawa
IEICE General Conference
09 Mar. 2021
09 Mar. 2021- 12 Mar. 2021

Implementation and Performance Evaluation of APCMA Applied to Massive IoT System
K. Honda; C. Tanaka; A. Li; F. Peper; K. Theofilis; N. Wakamiya; M. Hasegawa
IEICE General Conference
09 Mar. 2021
09 Mar. 2021- 12 Mar. 2021

High-Density Wireless Networks Based on Asynchronous Pulse Code Multiple Access (APCMA)
F. Peper; K. Leibnitz; K. Theofilis; M. Hasegawa; C. Tanaka; K. Honda; A. Li; N. Wakamiya
IEICE General Conference
09 Mar. 2021
09 Mar. 2021- 12 Mar. 2021

Analysis on Effectiveness of Laser Chaos Decision Maker
N. Okada; M. Naruse; N. Chauvet; A. Li; M. Hasegawa
IEICE General Conference
09 Mar. 2021
09 Mar. 2021- 12 Mar. 2021

Ultra-Fast Beam Selection Using Laser Chaos Decision Maker in Massive MIMO System
A. Uozumi; N. Okada; M. Naruse; N. Chauvet; A. Li; M. Hasegawa
IEICE General Conference
09 Mar. 2021
09 Mar. 2021- 12 Mar. 2021

A Study on Coherent Ising Machine with External Magnetic Fields -(1) An Analysis on Stability of Embedded Solutions -
K. Kurasawa; A. Li; H. Takesue; K. Aihara; M. Hasegawa
電子情報通信学会ソサイエティ大会
17 Sep. 2020
15 Sep. 2020- 18 Sep. 2020

A Study on Coherent Ising Machine with External Magnetic Fields -(2) Application to Traveling Salesman Problems-
T. Otsuka; K. Kurasawa; A. Li; H. Takesue; K. Aihara; M. Hasegawa
IEICE Society Conference
17 Sep. 2020
15 Sep. 2020- 18 Sep. 2020

A Study on Coherent Ising Machine with External Magnetic Fields -(3) Application to Quadratic Assignment Problems-
K. Hashimoto; K. Kurasawa; A. Li; H. Takesue; K. Aihara; M. Hasegawa
IEICE Society Conference
17 Sep. 2020
15 Sep. 2020- 18 Sep. 2020

An Analysis on Performance of Laser Chaos Decision Maker by the Method of Surrogate Data
Nirihiro Okadam; Makoto Naruse; Nicolas Chauvet; Aohan Li; Mikio Hasegawa
IEICE Technical Committee on Complex Communication Sciences
05 Jun. 2020
05 Jun. 2020- 05 Jun. 2020

Deep Q-Learning Based Resource Allocation for Energy Harvesting Internet of Things
Aohan Li; Tomoaki Ohtsuki
IEICE General Conference
17 Mar. 2020
17 Mar. 2020- 20 Mar. 2020

Resource Allocation Using Deep Reinforcement Learning in Energy Harvesting IoT System
Aohan Li; Tomoaki Ohtsuki
IEICE Technical Committee on Radio Communication Systems
04 Mar. 2020
04 Mar. 2020- 06 Mar. 2020

Enhanced Channel Hopping Algorithm for Heterogeneous Cognitive Ad Hoc Networks
Aohan Li; Tomoaki Ohtsuki
IEICE Technical Committee on Radio Communication Systems
21 Nov. 2018
20 Nov. 2018- 22 Nov. 2018

Two-Stage Fuzzy Q-Learning Based Channel Selection Algorithm in Cognitive Radio Networks
Aohan Li; F. H. Panahi; Tomoaki Ohtsuki
IEICE Technical Committee on Radio Communication Systems
19 Oct. 2018
18 Oct. 2018- 19 Oct. 2018

Fuzzy Q-Learning based Channel Selection Method for Cognitive Radio Networks
Aohan Li; F. H. Panahi; Tomoaki Ohtsuki
IEICE Multiple Innovative Kenkyu-kai Association for Wireless Communications
27 Sep. 2018
26 Sep. 2018- 28 Sep. 2018

Channel Selection Scheme for Cognitive Radio Networks with Secondary User Hardware Limitation Using a Two-Stage Learning Approach
Aohan Li; Tomoaki Ohtsuki
IEICE Society Conference
12 Sep. 2018
11 Sep. 2018- 14 Sep. 2018

Jump-Stay Based Frequency Hopping Strategy for Control Channel Establishment in Heterogeneous Cognitive Radio Networks
Aohan Li; Tomoaki Ohtsuki
IEICE Society Conference
12 Sep. 2017
12 Sep. 2017- 15 Sep. 2017

Improved Channel Hopping Algorithm-for Heterogeneous Cognitive Radio Networks
Aohan Li; Tomoaki Ohtsuki
IEICE Technical Committee on Radio Communication Systems
20 Jul. 2017
19 Jul. 2017- 21 Jul. 2017

Channel Hopping Algorithm Based on Multiple Radios for Cognitive Radio Networks
Aohan Li, Tomoaki Ohtsuki
IEICE Technical Committee on Radio Communication Systems
22 Jun. 2017
21 Jun. 2017- 23 Jun. 2017

Innovative Comprehensive Communications Design
Oct. 2023 - Present
The University of Electro-Communications

Fundamental Programming
Apr. 2022 - Present
The University of Electro-Communications

Mathematical Information Science Laboratory I・Computer Science Laboratory I
Apr. 2022 - Present
The University of Electro-Communications

Programming and Algorithm
Apr. 2020 - Mar. 2022
Tokyo University of Science

Graduation Research
Apr. 2020 - Mar. 2022
Tokyo University of Science

Basic Electrical & Electronics Information
Apr. 2020 - Mar. 2022
Tokyo University of Science

Electrical Engineering Experiment
Apr. 2020 - Mar. 2022
Tokyo University of Science

Apr. 2017 - Present
IEEE

Apr. 2017 - Present
IEICE

AI based optimization of the spectrum and energy efficiency for Intelligent 6G
李 傲寒
日本学術振興会, 科学研究費助成事業 若手研究, 電気通信大学, 若手研究, 22K14263
Apr. 2022 - Mar. 2025

Research on adaptive wireless communication technology
セイコーホールディングス株式会社, Tokyo University of Science, 共同研究, Coinvestigator
Jul. 2021 - Mar. 2022

Joint Research on sanitization systems by autonomous mobile robots and sanitization sensors
ロボティクス株式会社(株式会社ECTR), Tokyo University of Science, 共同研究, Coinvestigator
Jun. 2021 - Mar. 2022

Research on the applications of coherent Ising machine
Nippon Telegraph and Telephone Corporation, Tokyo University of Science, Collaborative Research, Coinvestigator
May 2021 - Mar. 2022

Joint Research on IoT wireless networks for solving regional issues
国立研究開発法人情報通信研究機構, 共同研究, Coinvestigator
Mar. 2020 - Mar. 2021

Deep Learning Based Dynamic Spectrum Access for Next Generation Wireless Communication
The Telecommunications Advancement Foundation, Keio University, Research Grant, Principal investigator
Apr. 2019 - Apr. 2020

Sensor Networks (specialty section of Frontiers in Sensors).
Peer review etc, Peer review, 2021 - Present

EURASIP Journal on Advances in Signal Processing (Sparse/Low-rank Tensor Signal Processing for Communication and Radar Systems)
Peer review etc, Peer review, 2021 - Present

IEEE ATC 2021 ( The 18th IEEE International Conference on Advanced and Trusted Computing)
Peer review etc, Peer review, 2021 - Oct. 2021

2021 International Conference on Wireless Communications and Signal Processing
Peer review, 2021 - Oct. 2021

IEEE VTC2021-Fall, Machine Learning and AI for Communications/Recent Results and Workshops
Peer review etc, Peer review, 2021 - Sep. 2021

IEEE/CIC ICCC (International Conference on Communications in China)
Peer review etc, Peer review, 2021 - Jul. 2021

IEEE IWCMC (17th International Wireless Communications & Mobile Computing Conference), E-Health Symposium
Peer review etc, Peer review, 2021 - Jun. 2021

IEEE GLOBECOM (Global Communications Conference), SAC-Internet of Things & Smart Connected Communities
Peer review etc, Peer review, 2020 - Dec. 2020

IEEE/CIC ICCC (International Conference on Communications in China)
Peer review etc, Peer review, 2020 - Aug. 2020

IEEE 16th International Wireless Communications & Mobile Computing Conference (IWCMC), E-Health Symposium
Peer review etc, Peer review, 2020 - Jun. 2020

IEEE VTC (Vehicular Technology Conference) Spring, Track: Radio Access Technology and Heterogeneous Networks
Peer review etc, Peer review, 2020 - May 2020

IEEE IWCMC (15th International Wireless Communications & Mobile Computing Conference), E-Health Symposium
Peer review etc, Peer review, 2019 - Jun. 2019

IEICE Transactions on Communications
Peer review etc, Peer review

IEEE Systems Journal
Peer review etc, Peer review

IEEE ACCESS
Peer review etc, Peer review

IEEE Communications Letters
Peer review etc, Peer review

ACM Transactions on Internet Technology
Peer review etc, Peer review

IEEE Transactions on Cognitive Communications and Networking
Peer review etc, Peer review

IEEE Internet of Things Journal
Peer review etc, Peer review

IEEE Transactions on Wireless Communications
Peer review etc, Peer review

IEEE Transactions on Vehicular Technology
Peer review etc, Peer review

IEEE Transactions on Industrial Informatics,
Peer review etc, Peer review

IEEE Network
Peer review etc, Peer review

IEEE Wireless Communications Magazine
Peer review etc, Peer review

IEEE Communications Magazine
Peer review etc, Peer review