機器學習無線通訊
Machine Learning for Wireless Communications
| 節 | 週三 |
|---|---|
A 18:30–19:20 | 機器學習無線通訊 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
這課程想針對機器學習(Machine Learning, ML) 在無線通訊和網路,做一個全面的介紹,包括 系統架構,跨層優化、實體層、通訊規約設計、網路切片、資源分配等,分為四大部分: (一) ML-based Spectrum Intelligence and Adaptive Resource Management ; (二) ML-based Transmission Intelligence and Adaptive Baseband Processing; (三) ML-based Network Intelligence and Adaptive System Optimization; (四) Advanced Topics in 6G Standards 目標是讓學生能夠學習到如何應用機器學習到未來的無線通訊系統和網路。 Part I: Spectrum Intelligence and Adaptive Resource Management 1. Machine Learning for Spectrum Access and Sharing 2. Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 3. Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 4. Deep Learning–Based Coverage and Capacity Optimization 5. Machine Learning for Optimal Resource Allocation 6. Machine Learning in Energy Efficiency Optimization 7. Deep Learning Based Traffic and Mobility Prediction 8. Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing Part II Transmission Intelligence and Adaptive Baseband Processing 9. Machine Learning–Based Adaptive Modulation and Coding Design 10. Neural Networks for Signal Intelligence: Theory and Practice 11. Neural Network–Based Wireless Channel Prediction Part III Network Intelligence and Adaptive System Optimization 12. Machine Learning for Digital Front-End: Comprehensive Overview 13. Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 14. Machine Learning for Context-Aware Cross-Layer Optimization 15. Physical-Layer Location Verification by Machine Learning 16. Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching Part IV Advanced Topics in 6G Standards 17. Satellite Communications in Non-Terrestrial Network 18. Joint Sensing and Communications
1. Digital Communications 2. Computer Networks
1. Assignments: 20% 2. Mid-Term: 30% 3. Final Exam 30% 4. Project 20%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Overview of AI in Wireless : Evolution of Wireless Communications and Integration with AI |
| 第 2 週 | AI in Radio Resource Management (I) – Resource Allocation in Cognitive Radio Networks |
| 第 3 週 | AI in Radio Resource Management (II) – Adaptive Spectrum Sharing |
| 第 4 週 | AI in Radio Resource Management (III) – Joint-Sensing in ISAC |
| 第 5 週 | AI in Transmission Intelligence (I) – Channel Prediction & Adaptive Modulation |
| 第 6 週 | AI in Transmission Intelligence (II) – Adaptive Link-Level Transmission & Ray Tracing |
| 第 7 週 | AI in Transmission Intelligence (III) – AI in Network Intelligence (I) – System Integration and System Optimization |
| 第 8 週 | AI in Network Intelligence (I) – Neural Network for Full-Duplex Radios : Self-Interference Cancellation |
| 第 9 週 | AI in Network Intelligence (II) – Machine Learning for Cross-Layer Optimization |
| 第 10 週 | AI in Network Intelligence (III) – Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching |
| 第 11 週 | Mid-Term Exams: Final project Proposal (Students) |
| 第 12 週 | Advanced Topics (I) – Connect-Compute Energy Efficient Techniques for 6G Wireless |
| 第 13 週 | Advanced Topics (II) – O-RAN : Overview, Practices, and Applications |
| 第 14 週 | Advanced Topics (III) – 6G SatCom : Overview, Practices, and Applications |
| 第 15 週 | Advanced Topics (IV) – Next Generation Communications : Generative AI for Efficient Network and Sustainable Wireless Systems |
| 第 16 週 | Final-Term Exams: Final Presentation (Students) |
1. Machine Learning for Future Wireless Communications, F. L. Luo, Wiley, IEEE Press, 2020 2. Key Technologies for 5G Wireless Systems, Vincent W. S. Wong, Robert Schober, Derrick Wing Kwan Ng, Li-Chun Wang, Cambridge University Press 2017. 3. SDR with Zynq Ultrascale+ RFSoC, AMD, 2022. 4. Shaping future 6G networks: Needs, impacts, and technologies. John Wiley & Sons, 2021.
- 聯絡方式
- wang@nycu.edu.tw