Research Topics (Ongoing)

Topic 1:  AI for Resource Allocation in Decentralized IoT Systems. 

In this research, we develop lightweight, decentralized learning-based approaches for IoT systems. 

References:

[J1] R. Ariyoshi (M2@Li_Lab), A. Li (Corresponding Author), M. Hasegawa, T. Ohtsuki, "Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks," 24 pages, Sensors (IF: 3.5), Aug. 2025. (Part of the work was presented at IEEE WCNC 2025 [Link].)

[J2] A. Li, I. Urabe, M. Fujisawa, S. Hasegawa, H. Yasuda, S.-J. Kim, and M. Hasegawa, "A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN," arXiv:2208.01824, 16 pages. (Part of the work was presented at IEEE DySPAN 2021 [Link].)

[C1] R. Ariyoshi (M2@Li_Lab.), A. Li, M. Hasegawa, T. Ohtsuki, M. Pan, Z. Han, "Schwarz Information Criterion Aided Multi-Armed Bandit for Decentralized Resource Allocation in Dynamic LoRa Networks," IEEE INFOCOM (International Conference on Computer Communications) Workshop, 6 pages, Tokyo, Japan, May 2026. 

[C2] A. Li and M. Tsuzuki (M1@Li_Lab.), "(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems," IEEE CIoT (8th Conference on Cloud and Internet of Things, 2 pages, London, United Kingdom, Oct. 2025. (Best Demo Paper Award)

[C3] S. Sugiyama (M1@Li_Lab.), K. Makizoe (M2@TUS), M. Arai, M. Hasegawa, T. Ohtsuki, A. Li, "Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learning," IEEE VTC2024-Spring (The 2024 IEEE 99th Vehicular Technology Conference ), Singapore, 5 pages, June 2024.

[C4] A. Li, Z. Duan, M. Naruse, and M. Hasegawa, "Uplink Grant-Free NOMA Using Laser Chaos Decision Maker," IEICE NOLTA2022 (IEICE The 2022 International Symposium on Nonlinear Theory and Its Applications), 4 pages, Virtual, Dec. 2022.

[C5] A. Li, "Deep Reinforcement Learning Based Resource Allocation for LoRaWAN," IEEE VTC2022-Fall (IEEE 96th Vehicular Technology Conference), 4 pages, London/Beijing, Sept. 2022. 

Topic 2: Quantum Annealing Inspired Methods for Optimization Problems

In this research, we work on quantum annealing inspired methods for ultra-fast optimization problems in Beyond5G/6G, agriculture, and other relevant areas.

References:

[J1] T. Otsuka (M2@TUS), A. Li (Corresponding Author), H. Takese, K. Inaba, K. Aihara, and M. Hasegawa, "High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine for NOMA Systems," IEEE Transactions on Vehicular Technology (IF: 6.8),  vol. 73, no. 1, pp. 707-723, Jan. 2024.

[J2] T. Fujita (M2@TUS), A. Li (Corresponding Author), Q. V. Do, T. Otsuka, S.-G. Jeong, W.-J. Hwang, H. Takesue, K. Inaba, K. Aihara, and M. Hasegawa, "Applying Coherent Ising Machines for Enhancing Communication Efficiency in Large-Scale UAV-Aided Networks," IEEE Access (IF: 3.4),  vol. 12, pp. 136011-136024, Aug. 2024. 

[J3] K. Kurasawa (M2@TUS), K. Hashimoto (M1@TUS), A. Li, K. Sato, K. Inaba, H. Takesue, K. Aihara and M. Hasegawa, "A High-Speed Channel Assignment Algorithm for Dense IEEE 802.11 Systems via Coherent Ising Machine," IEEE Wireless Communications Letters (IF: 4.348), vol. 10, no. 8, pp. 1682-1686, Aug. 2021.  

Topic 3: Cybersecurity. 

In this research, we address security issues related to Wireless Communications, Quantum Computing, E-Health, and other relevant areas. 

References:

[C1] Y. Negoya (M2@Li_Lab.), F. Cui, Z. Zhang, M. Pan, T. Ohtsuki, A. Li, "A Privacy-Preserving Federated Learning Method with Homomorphic Encryption in Omics Data," 6 pages, 2026. (Under Review)

[C2] H. Zhong, K. Ju, M. Sistla, X. Zhang, A. Li, X. Qin, X. Fu, and M. Pan, "Tuning Quantum Computing Privacy through Quantum Error Correction," 2024 IEEE GLOBECOM (Global Communications Conference), Cape Town, South Africa, 6 pages, Dec. 2024.

Topic 4: Biomedical Signal Processing. 

In this research, we analyze physiological data, including EEGs (brain activity) and ECGs (heart signals).

References:

[C1] R. Anurag, K. A. Muthukumar, and A. Li, "Non-Invasive Fetal Electrocardiogram Extraction Using Deep Learning and Earth Mover’s Distance," IEEE ICC (International Conference on Communications), 5 pages, Glasgow, Scotland, UK, May 2026.