人工智慧無線通訊
Artificial Intelligence Wireless
| 節 | 週三 |
|---|---|
A 18:30–19:20 | 人工智慧無線通訊 ED203 3 節連堂 |
B 19:30–20:20 | |
C 20:30–21:20 |
* 根據陽明交大上課時間表所列
This course aims to equip students with machine learning (AI) and wireless communication algorithm design capabilities, understanding 5G, B5G, and 6G Communication Systems, and enabling hands-on implementation of communication algorithms in 5G and O-RAN small base stations on FPGA/RFSoC platforms. Students will learn the complete process from communication system modeling and AI algorithm design to implementation and testing on an FPGA board, thereby mastering cross-domain skills spanning theory, algorithms, and hardware implementation.
1. Wireless and Digital Communications 2. Computer Networks 3. Machine Learning / Deep Learning
TA hours: Monday, Tuesday 09.00-11.00
1. Assignments: 20% 2. Mid-Term: 30% 3. Final Exam 30% 4. Project 20%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction |
| 第 2 週 | Channel Modelling, Estimation, and Compression I |
| 第 3 週 | Channel Modelling, Estimation, and Compression II |
| 第 4 週 | Learning Receiver Design : Signal Detection and Channel Decoding I |
| 第 5 週 | Learning Receiver Design : Signal Detection and Channel Decoding II |
| 第 6 週 | End to End Learning of Wireless Communication Systems I |
| 第 7 週 | End to End Learning of Wireless Communication Systems II |
| 第 8 週 | Learning Resource Allocation in Wireless Networks I |
| 第 9 週 | Mid-Term Proposal / Learning Resource Allocation in Wireless Networks II |
| 第 10 週 | Wireless for AI : Distributed and Federated Learning I |
| 第 11 週 | Wireless for AI : Distributed and Federated Learning II |
| 第 12 週 | Collaborative Learning over Wireless Networks |
| 第 13 週 | Optimized Federated Learning in Wireless Networks with Constrained Resources |
| 第 14 週 | Quantized Federated Learning, Over-the-Air Computation for Distributed Learning over Wireless Networks |
| 第 15 週 | Federated Knowledge Distillation, Differentially Private Wireless Federated Learning, and Timely Wireless Edge Inference |
| 第 16 週 | Final Paper Presentation |
1. Wireless Communications and Machine Learning. L. Liang, S. Jin, H. Ye, and G. Y. Li, Cambridge, United Kingdom: Cambridge University Press, 2026. doi: 10.1017/9781009232210. 2. Machine Learning for Future Wireless Communications, F. L. Luo, Wiley, IEEE Press, 2020 3. Key Technologies for 5G Wireless Systems, Vincent W. S. Wong, Robert Schober, Derrick Wing Kwan Ng, Li-Chun Wang, Cambridge University Press 2017. 4. SDR with Zynq Ultrascale+ RFSoC, AMD, 2022. 5. Shaping future 6G networks: Needs, impacts, and technologies. John Wiley & Sons, 2021.
- 地點
- ED203
- 時間
- 每週三晚上18:30~21:00
- 聯絡方式
- https://aiwireless-535100.github.io/ wang@nycu.edu.tw