Aohan LI (李傲寒)

0110100_Ⅰ類（情報系）, 0110000_情報理工学域, The University of Electro-Communications

Beyond5G/6G

Internet of Things (Resource Allocation, Edge Computing, MAC Protocols) | Internet of Things (リソース割り当て, エッジコンピューティング, MAC プロトコル)

Multiplexing Techniques (NOMA, Pulse, etc.) | 多重化通信方式 (NOMA, パルスなど)

Quantum Computer (Quantum Annealing) | 量子コンピュータ（量子アニーリング）

Artificial Intelligence (Machine Learning, Deep Learning) | 人工知能 (機械学習, 深層学習)

Cognitive Radio Networks (Spectrum Prediction, Channel Selection, Control Channel Establishment, Routing Selection) | コグニティブ無線ネットワーク (スペクトラム予測, チャネル選択, 制御チャネルの確立, ルーティング選択)

Laser Chaos, Quantum Computing, Game Theory | レーザカオス, 量子コンピューティング, ゲーム理論

ジャーナル論文 - rm_published_papers: International Conference Proceedings 査読済み

Power Distribution Optimization in MU-MIMO Using Spherical Wave Function Expansion

by Aohan LI

公開済 10/2025

2025 International Symposium on Nonlinear Theory and Its Applications

ジャーナル論文 - rm_published_papers: International Conference Proceedings 査読済み

(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems

by Aohan LI

公開済 10/2025

IEEE CIoT (8th Conference on Cloud and Internet of Things)

ジャーナル論文 - rm_published_papers: Scientific Journal 査読済み

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

by Aohan LI

公開済 12/08/2025

Sensors, 25, 16, 4996 - 4996

The rapid growth of Long Range (LoRa) devices has led to network congestion, reducing spectrum and energy efficiency. To address this problem, we propose an energy-efficient reinforcement learning method for distributed LoRa networks, enabling each device to independently select appropriate transmission parameters, i.e., channel, transmission power (TP), and bandwidth (BW) based on acknowledgment (ACK) feedback and energy consumption. Our method employs the Upper Confidence Bound (UCB)1-tuned algorithm and incorporates energy metrics into the reward function, achieving lower power consumption and high transmission success rates. Designed to be lightweight for resource-constrained IoT devices, it was implemented on real LoRa hardware and tested in dense network scenarios. Experimental results show that the proposed method outperforms fixed allocation, adaptive data rate low-complexity (ADR-Lite), and ϵ-greedy methods in both transmission success rate and energy efficiency.

ジャーナル論文 - rm_published_papers: Scientific Journal 査読済み

Prototype-wise momentum-based federated contrast learning

by Aohan LI

公開済 07/2025

Journal of Information and Intelligence