Research Topics (Ongoing)

Topic 1:  AI for Decentralized Resource Allocation in 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.

[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.

[C1] 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.

[C2] 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.

[C3] 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.

[C4] 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 for Optimization Problems in 6G.

In this research, we work on quantum annealing for ultra-fast optimization in wireless communications (NOMA systems, UAV systems, WLAN systems, RIS systems, etc.)

References:

[J1] 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. 

[J2] 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.

Topic 3: Cybersecurity.

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

References:

[C1] 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.

[C2] Y. Negoya (M2@Li_Lab.), F. Cui, Z. Zhang, M. Pan, T. Ohtsuki, A. Li, "Differential Privacy Based Federated Learning with Homomorphic Encryption in Omics Data," 6 pages, 2026. (Under Review)