Textbook reading seminar 2023
Channel Modeling (CM)
- 電気電子工学文献詳読I（M1)・II（M2）
- Time: 16:25-17:55, Wed
- Lecturers (D/M2/M1): Calist, Suzuki, Takahashi
- Articles (Spring, 2023)
- A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel, EuCAP, 2023. (\Kimlab\users\lab\bookreading_seminar\2023)
- X. Wang et al., “A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics,” GLOBECOM 2022 – 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022 .
- H. -J. Song and N. Lee, “Terahertz Communications: Challenges in the Next Decade,” in IEEE Transactions on Terahertz Science and Technology, vol. 12, no. 2, pp. 105-117, March 2022.
- R. Charbonnier et al., “Calibration of Ray-Tracing With Diffuse Scattering Against 28-GHz Directional Urban Channel Measurements,” in IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 14264-14276, Dec. 2020
- V. Degli-Esposti, F. Fuschini, E. M. Vitucci and G. Falciasecca, “Measurement and Modelling of Scattering From Buildings,” in IEEE Transactions on Antennas and Propagation, vol. 55, no. 1, pp. 143-153, Jan. 2007 (\Kimlab\users\lab\bookreading_seminar\2023)
- Kai Mao et al., “Machine Learning – Based 3D Channel Modeling for U2V mmWave Communications,” IEEE Internet of Things Journal, Vol. 9, September 2022.
- J. M. Eckhardt et al., “Uniform Analysis of Multipath Components From Various Scenarios With Time-Domain Channel Sounding at 300GHz,” in IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 446-460, 2023
| Date | Topic | Charge |
|---|---|---|
| 5/17 | Article 1, 2 | Calist1 |
| 6/14 | Article 3 | Takahashi1 |
| 6/21 | Article 4, 5 | Suzuki1 |
| 7/19 | Article 6 | Calist2 |
| 7/26 | Article 7 | Takahashi2 |
- Articles (Fall, 2023)
- J. Gomez-Ponce, N. A. Abbasi, A. E. Willner, C. J. Zhang and A. F. Molisch, “Directionally Resolved Measurement and Modeling of THz Band Propagation Channels,” in IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 663-686, 2022.
- S. Ju and T. S. Rappaport, “Simulating Motion – Incorporating Spatial Consistency into NYUSIM Channel Model,” 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, 2018, pp. 1-6.
- S. Ju and T. S. Rappaport, “Millimeter-Wave Extended NYUSIM Channel Model for Spatial Consistency,” 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018.
- TBD
- TBD
- Z. Li, P. Wang, K. Liu and Z. Tian, “MimoLoc: Indoor Localization With Assistance of Microwave Reflection of Downlink Signal in Sub-6G MIMO Networks,” in IEEE Transactions on Microwave Theory and Techniques, doi: 10.1109/TMTT.2023.3314061.
| Date | Topic | Charge |
|---|---|---|
| 10/25 (Wed, 5th) | Article 1 | Takahashi3 |
| 11/10 (Fri, 4th) | Article 2,3 | Suzuki2 |
| 11/24 (Fri, 4th) | Article 4 | Calist3 |
| 12/20 (Wed, 5th) | Article 6 | Koularp3 |
Machine Learning (ML)
- 電気電子工学文献詳読I（M1)・II（M2）
- Textbook (Articles)
- Time: 16:25-17:55, Fri
- Lecturers (M2/M1): Ikegami, Wako, Wang, Koularp
- Schedule
| Date | Topic | Charge |
|---|---|---|
| 5/31 (Wed, 5th) | 1: Chapter 1, Chapter 2 (ADALINE) | Wang1 |
| 6/16 | 2: Chapter 3 (Logistic Regression, SVM, DT, KNN) | Ikegami1 |
| 6/23 | 3: Chapter 4 (Preprocessing) | Wako1 |
| 7/7 | 4: Chapter 5 (Reduction) | Koularp1 |
| 7/21 | 5: Chapter 6 (Tuning) | Wang2 |
| Date | Topic | Charge |
|---|---|---|
| 11/10 (Fri. 5th) | 1: Chapter 7 (Ensemble) | Ikegami2 |
| 11/22 (Wed. 5th) | 2: Chapter 10 (Regression) | Wako2 |
| 11/24 (Fri. 5th) | 3: Chapter 11 (Clustering) | Kourlarp2 |
| 12/22 (Fri. 5th) | 5: Chapter 12 (Neural Network) |
Python 環境構築
既にgitとかpythonをインストール済みの人がやると面倒なことになるかもしれないので気をつけてください．
パワーシェルを立ち上げてPSVersionを確認する．
PS> $PSVersionTable
PSVersionが5.0以上だったらScoopをインストールする．（PSVersionが5.0未満だったらパワーシェルの最新版をインストールしてから行うこと．）
PS> Set-ExecutionPolicy RemoteSigned -scope CurrentUser
PS> iex (new-object net.webclient).downloadstring('https://get.scoop.sh')
成功したら，gitのインストール
PS> scoop install git
次に，Pythonのインストール（Anacondaを使用）
PS> scoop bucket add extras
PS> scoop install anaconda3
最後に任意のディレクトリに教科書のソースコードをクローン
PS> mkdir Work # 任意のディレクトリ作成（不必要なら無視）
PS> cd Work # 任意のディレクトリに移動
PS> git clone https://github.com/rasbt/python-machine-learning-book.git # クローン
エラーとか出たら聞いてください．環境構築まだの人は↑を参考に各自やっておいてください．
・pythonがつかえる
・ソースコードをダウンロード（クローン）済み
になっている人はOKです．あと教科書（日本語版（第1版））は，「\\133.35.167.28\kimlab\users\lab\勉強会資料\ML\教科書」に置いたので各自見てください．
一応，自分で使う目的以外には使用しないでください．
