研究方向: 强化学习、信息论、智能通信、智能系统
主要研究工具 Highlighted Methodologies:
- 强化学习 Reinforcement Learning
- 概率生成模型 Probabilistic Generative Model
- 物理引擎与射线追踪 Physical Engine and Ray-Tracing in Computational Graphics
- 电路、反馈以及系统 Circuits, Feedbacks and Systems
目前关注领域 Fields of Current Interest:
- 语义通信 Semantic Communications
- 单比特通信系统 1-bit-ADC/DAC Communication Systems
- 智能信道 Smart Wireless Channel and Intelligent Wireless Environment
- 下一代无线异构网络 Heterogeneous Networks via B5G/6G Wireless Mediums
- Neuromorphic EDA
代表性研究工作 Featured Researches:
移动性与移动信道数据 Mobility and Mobile Channel Data
- Channel Characterization and Realization of Mobile Optical Wireless Communications by Zi-Yang Wu, Muhammad Ismail, Justin Kong, Erchin Serpedin and Jiao Wang, in IEEE Transactions on Communications, 2020.
基带与基站的联合优化 Joint Optimization on Baseband and Base Station
- Optimized DFT-spread OFDM based visible light communications with multiple lighting sources by Zi-Yang Wu, Yu-Liang Gao, Ze-Kun Wang, Chuan You, Chuang Yang, Chong Luo, and Jiao Wang, in Optics Express, 2017.
数据驱动的异质网络优化 Data-Driven HetNet Optimization
- Data-Driven Link Assignment With QoS Guarantee in Mobile RF-Optical HetNet of Things by Zi-Yang Wu, Muhammad Ismail, Erchin Serpedin and Jiao Wang, in IEEE Internet of Things Journal, 2020. Best Paper Award 最佳论文奖 on 10th International Conference on Intelligent Systems (IS’20)
- Efficient Integration of 5G and Beyond Heterogeneous Networks B5G异质网络的高效聚合 by Zi-Yang Wu, Muhammad Ismail, Justin Kong, Erchin Serpedin, and Jiao Wang, pressed by Springer Nature, 2020.
More researches (Google Scholar)