# **Daily Logs - Ray** ###### tags: `Daily logs` * 5/13 * 初步了解且嘗試將zc sequence IQ signal的值用2’s complement去定義 * survey paper * 5/14 * 看論文 Current Status and Directions of IEEE 802.11be, the Future Wi-Fi 7 * 與網路實驗助教們一同想停課替代方案 * 處理實驗室110年度1-3月各計畫（公務）用品自行列冊列管清冊 * 5/17 * 處理網路實驗遠距教學之內容 * survey paper * 5/18 * 處理網路實驗遠距教學之內容 * survey paper * 5/19 * 與John線上討論、測試兩台repeater同步、觀測repeater之zc sequence事項 * survey paper * 5/20 * repeater計畫處理發現的bug，解issue * 5/21 * 上網課 網路技術分析 * 5/24 * repeater計畫處理zc sequence module 之 code * 5/26 * survey paper * repeater計畫思考如何處理rx端訊號之雜訊與phase offset * 5/27 * 複習有關 Match filter 知識 * 5/28 * 寫matlab code (分析repeater計畫中repeater tx 與 rx端收到之data) * 5/30 * survey paper * 5/31 * survey paper * survey carrier frequency offset and sample frequency offset * 6/1 * survey paper * survey wifi室內定位技術 * 6/2 * 繼續寫matlab code (分析repeater計畫中repeater tx 與 rx端收到之data) * survey paper * 6/3 * 寫matlab code (repeater計畫中repeater tx 與 rx端收到之data之取值與2進位十進位轉換) * 6/4 * debug matlab code * ML HW3 * 6/5 * ML HW3 * 讀paper Coexistence Management for URLLC in Campus Networks via Deep Reinforcement Learning * 6/7 * ML HW3 * 讀paper Coexistence Management for URLLC in Campus Networks via Deep Reinforcement Learning(公式與algorithm) * 6/8 * ML HW3 修改coding 撰寫報告 * 網路技術分析複習期中報告 * 6/9 * survey Markov Decision Process * 網路技術分析複習期中報告、回答問題 * 6/11 * 寫matlab code 與將John給的資料放進matlab做分析(同一台repeater tx 送與 rx端收到之data) * survey RNN * 6/13 * 完成網路技術分析second presentation ppt * survey CNN * 6/14 * 準備網路技術分析second presentation 英文講稿 * 做網路技術分析期末project * 6/15 * 完成網路技術分析期末project * survey DRL and Q-learning(paper中核心內容) * 6/16 * 6/16 Survey E-greedy演算法 on-policy v.s off-policy Q-learning * survey reference[13] A Deeper Look at Experience Replay * 製作 報告paper ppt * 6/17 * 製作 報告 paper ppt * 思考 my opinion * 6/18 * 寫matlab code 將John給的資料放進matlab做分析(同一台repeater tx 送與 rx端收、一台repeater tx送與 另一台repeater rx端收) * 6/20 * 無線通訊期末考準備 * 6/21 * 無線通訊期末考準備 * 6/22 * paper IP RAN Hybrid Provisioning in 5G Backhaul Network * 莫凡python * 6/23 * 實驗課作業打分數 * paper IP RAN Hybrid Provisioning in 5G Backhaul Network * 6/24 * 實驗課分數處理 * paper IP RAN Hybrid Provisioning in 5G Backhaul Network * 6/25 * 處理實驗室財產 * 6/27 * paper審查IP RAN Hybrid Provisioning in 5G Backhaul Network * 6/29 * 處理、統整學期末實驗課資料 * paper審查IP RAN Hybrid Provisioning in 5G Backhaul Network英文版 * survey Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * 6/30 * Best Reinforcement Learning Tutorials, Examples, Projects, and Courses * Recommendation Systems using Reinforcement Learning * 7/1 * 莫凡python * 7/2 * 與John討論repeater計畫air傳輸之事項 * 莫凡python * 7/5 * 去展連拿repeater * 莫凡python * survey Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * 7/6 * 莫凡python * survey paper Multiple Channel Access using Deep Reinforcement Learning for Congested Vehicular Networks * survey paper DQN-Based Power Control for IoT Transmission against Jamming * 7/7 * 採購splitter * survey Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * 7/9 * survey Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * 7/10 * survey Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * 7/12 * survey Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * survey A Progressive Single Channel Reservation Scheme for URLLC in Unlicensed Band * 7/13 * Make paper reports ppt * survey policy gradient * survey A Progressive Single Channel Reservation Scheme for URLLC in Unlicensed Band * 7/14 * Make paper reports ppt * survey multipath DNN * survey A Progressive Single Channel Reservation Scheme for URLLC in Unlicensed Band * think select the best channel over RL * 7/15 * Make paper reports ppt * think select the best channel over RL * report Reinforcement learning‑based hybrid spectrum resource allocation scheme for the high load of URLLC services * 7/16 * survey 莫凡python Reinforcement learning * 7/19 * Go to the company to test the zc sequence in the repeater project through air transmission * 7/20 * survey python pandas * 7/22 * survey python pandas * 7/23 * survey python pandas、Matplotlib * 7/26 * ask the salse of woken about 3-way spliter and survey the spec of 3-way spliter * 8/6 * Change the laboratory computer from HDD to SSD * 8/10 * buy 4-way spliter and termination * Look at the code of [https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5_Deep_Q_Network/RL_brain.py](https://) * 8/11 * Look at the code of [https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5_Deep_Q_Network/RL_brain.py](https://) * 8/12 * survey paper Radio Resource Scheduling for 5G NR via Deep Deterministic Policy Gradient * update the property of our lab ## **Renew** * 10/6 * talk with John about the content of repeater project * deal with the property of MWNL * survey tensorflow * read the ppt of random access analysis * prepare the DRL over P-SCR ppt for report tomorrow * 10/7 * review the repeater project of simulation and teach John * Discuss the DRL plan with teachers and John * 10/13 * Hung-yi Lee meachine learning video 2 episode * survey use C code in python * read paper about hidden node probability * 10/14 * Hung-yi Lee meachine learning video 2 episode * 10/15 * Dealing with the scrapping of MWNL property * 10/18 * survey use C code in python * survey pytorch but still want to use tensorflow * meeting with akon about the RL project * 10/19 * Hung-yi Lee meachine learning video 1 episode