【演講】2019/11/19 (二) @工四816 (智易空間)，邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan） 演講「Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management」
IBM中心特別邀請到Prof. Geoffrey Li（Georgia Tech, USA）與Prof. Li-Chun Wang（NCTU, Taiwan）前來為我們演講，歡迎有興趣的老師與同學報名參加！
演講標題：Deep Learning based Wireless Resource Allocation／Deep Learning in Physical Layer Communications／Machine Learning Interference Management
演 講 者：Prof. Geoffrey Li與Prof. Li-Chun Wang
時 間：2019/11/19(二) 9:00 ~ 12:00
地 點：交大工程四館816 (智易空間)
活動報名網址：https://forms.gle/vUr3kYBDB2vvKtca6
報名方式：
費用：（費用含講義、午餐及茶水）
1.費用：(1) 校內學生免費，校外學生300元/人 (2) 業界人士與老師1500/人
2.人數：60人，依完成報名順序錄取（完成繳費者始完成報名程序）
※報名及繳費方式：
1.報名：請至報名網址填寫資料
2.繳費：
（1）親至交大工程四館813室完成繳費(前來繳費者請先致電)
（2）匯款資訊如下：
戶名: 曾紫玲（國泰世華銀行 竹科分行013）
帳號: 075506235774 (國泰世華銀行 竹科分行013）
匯款後請提供姓名、匯款時間以及匯款帳號後五碼以便對帳
※將於上課日發放課程繳費領據
聯絡方式：曾紫玲 Tel：03-5712121分機54599 Email：tzuling@nctu.edu.tw
Abstract:
1.Deep Learning based Wireless Resource Allocation
【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, I will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non-linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.
2.Deep Learning in Physical Layer Communications
【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.
3.Machine Learning Interference Management
【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle -to-vehicle networks, and unmanned aerial vehicle (UAV) networks.
Bio:
Dr. Geoffrey Li is a Professor with the School of Electrical and Computer Engineering at Georgia Institute of Technology. He was with AT&T Labs – Research for five years before joining Georgia Tech in 2000. His general research interests include statistical signal processing and machine learning for wireless communications. In these areas, he has published around 500 referred journal and conference papers in addition to over 40 granted patents. His publications have cited by 37,000 times and he has been listed as the World’s Most Influential Scientific Mind, also known as a Highly-Cited Researcher, by Thomson Reuters almost every year since 2001. He has been an IEEE Fellow since 2006. He received 2010 IEEE ComSoc Stephen O. Rice Prize Paper Award, 2013 IEEE VTS James Evans Avant Garde Award, 2014 IEEE VTS Jack Neubauer Memorial Award, 2017 IEEE ComSoc Award for Advances in Communication, and 2017 IEEE SPS Donald G. Fink Overview Paper Award. He also won the 2015 Distinguished Faculty Achievement Award from the School of Electrical and Computer Engineering, Georgia Tech.
Li-Chun Wang (M'96 -- SM'06 -- F'11) received Ph. D. degree from the Georgia Institute of Technology, Atlanta, in 1996. From 1996 to 2000, he was with AT&T Laboratories, where he was a Senior Technical Staff Member in the Wireless Communications Research Department. Currently, he is the Chair Professor of the Department of Electrical and Computer Engineering and the Director of Big Data Research Center of of National Chiao Tung University in Taiwan. Dr. Wang was elected to the IEEE Fellow in 2011 for his contributions to cellular architectures and radio resource management in wireless networks. He was the co-recipients of IEEE Communications Society Asia-Pacific Board Best Award (2015), Y. Z. Hsu Scientific Paper Award (2013), and IEEE Jack Neubauer Best Paper Award (1997). He won the Distinguished Research Award of Ministry of Science and Technology in Taiwan twice (2012 and 2016). He is currently the associate editor of IEEE Transaction on Cognitive Communications and Networks. His current research interests are in the areas of software-defined mobile networks, heterogeneous networks, and data-driven intelligent wireless communications. He holds 23 US patents, and have published over 300 journal and conference papers, and co-edited a book, “Key Technologies for 5G Wireless Systems,” (Cambridge University Press 2017).
model d wireless 在 [滑鼠] Glorious Model D Wireless 無線洞洞滑鼠 - 批踢踢實業坊 的推薦與評價
網誌無廣告版
https://ohmy-bear.blogspot.com/2021/10/glorious-model-d-wireless.html
PTT好讀版
https://www.ptt.cc/bbs/Key_Mou_Pad/M.1634742347.A.8E6.html
以下正文
我個人是非常難以忍受為了減輕重量將外殼打滿洞，讓人密集恐懼症發作的滑鼠。
但我的手怎麼就這麼不爭氣呢？當初看到某硬在預購，怎麼就著了魔把卡刷下去了！？可
能是看上輕量、無線及白色外觀吧
總之現在收到了這顆滑鼠，來分享一下。
先提醒一下，以下會出現洞洞滑鼠的照片，已經盡量減少拍到洞洞，但仍有數張整體照片
難以避免，如果會感到不適請斟酌觀看。
台灣代理商版本，保固兩年。
背面規格簡介，使用2.4GHz無線及有線雙模式連接、重量69g、電池續航71小時，右手專
用設計，尺寸128mm*67mm*42mm，算是相當大的滑鼠。
***下方洞洞照片警告***
不得不說內包裝看起來有點LOW，亮面塑膠裝載滑鼠本體，塑膠殼下方還可能因為太薄而
變形...
配件一覽，包括柔軟的傘繩材質傳輸線、額外鼠腳貼及USB延長接收器。
USB接收器預裝在USB延長接收器上，延長接收器與滑鼠相同為白色，隱約還能看到
Glorious的LOGO。
***下方洞洞照片警告***
滑鼠本體，鼠背為了減輕重量而開了不少六角形的洞，因為內部零件還有些黑色部分，所
以洞洞部分有些是透出白色，有些是透出黑色部分，看起來真是噁...
個人認為黑色款應該視覺效果會讓人舒服一些，而且目前能貼住洞洞的滿版止滑貼紙是黑
色，買白色滑鼠然後貼黑色止滑貼！？
左右鍵採分離設計，微動據中國資料為凱華GM 8.0微動，壽命為8千萬次。滾輪材質為黑
色橡膠，外觀跟手感個人都覺得較為....樸實無華，左右鍵中間還一顆DPI切換按鍵，一
樣採用黑色外觀。
左側設有兩顆側鍵，後方亦設有廠商LOGO。
右側則式前方有Glorious字樣。由前後兩張照片可看出Glorious Model D Wireless 左右
側都沒有額外設計止滑橡膠或紋路
***下方洞洞照片警告***
底部也為了減輕重量開了不少洞，底部除了感應器外僅有滑鼠開關及DPI指示燈，沒有收
納USB接收器的設計。
有別於目前流行的大面積鼠腳，Model D Wireless採用4個小鼠腳的設計方式，不過如果
嫌不夠也是可以貼上附贈的2片鼠腳。
就Glorious Model D Wireless的外觀來說，扣除洞洞部分我個人是覺得不錯，雖然左右
邊沒有止滑設計，但整體外觀更趨於一致，且表面的霧面塗層還是有一定的止滑效果。
但還是被我發現了小缺點，在這個左側、鼠背及底部三界交會的地方，竟然有些外殼組裝
品質欠佳的部分，會稍微刮手啊...
上機通電，Model D Wireless總共有3區幻彩燈效，包括滾輪及側邊兩條光帶。個人覺得
燈效算好看，就是跟RAZER的MAMBA有點撞臉。
進入官網下載驅動。
話說如果嫌Model D跟 Model O太大的使用者，官網也預告縮小版的Model D-跟 Model O-
也即將推出無線版本。
驅動界面介紹，左方一排功能ICON，最上方的Glorious LOGO會跳出瀏覽器連結到官網，
中間類似WIN的符號可能是針對WIN 11的功能吧，我在WIN 10系統下按了沒反應。下方滑
鼠圖案則是按了後就進入Model D Wireless設定頁面，如果嫌ICON小可以直接點中間的
Model D 滑鼠圖案，也可進入設定頁面。
下方齒輪式進入驅動程式的設定，可以設定是否隨WIN啟動及啟動後最小化至工具列。目
前語言的選項沒有中文，以後可能也不會有(吧？)
可以設定3個不同的設定檔，應該是可以存在滑鼠內帶著走，這部分還需要確認。
滑鼠可設定的部分包括燈效、按鍵功能及性能。
共有8種燈效，也能設定速度及亮度，不過個人認為Glorious Mode是裡面唯一能打的。
按鍵部分可調部分還不少，也還滿直覺的，就可惜沒中文可能會對些許人造成阻礙。
Model D Wireless 共有6顆按鍵，各按鍵皆可分別設定功能。
設定成組合鍵功能個人覺得相當方便。
這個鍵盤功能讓我有點混亂，Profile cycle我會理解為滑鼠設定檔切換，還挺合理的，但
是Layer cycle的功能我就就不是很確定了，這兩個功能又與鍵盤有關係嗎？
其中一個有趣的功能是電量確認，按下後滾輪燈效會閃爍，中電量時為黃色，滿電量時為
綠色。不過按下確認鍵後會一直閃直到再次按下，應該設計成閃爍幾次後就熄滅較佳。
Model D Wireless 號稱採用與Pixert聯手研發的BAMF感應器，具有最高19000 的DPI，能
設置4檔不同的DPI，且以顏色作為區分。
但是切換DPI後，只能翻過滑鼠，透過底部的DPI指示燈了解當前的DPI，使用上也是會造
成些許不方便。
***下方洞洞照片警告***
今年陸續購入的幾款白色版輕量無線滑鼠大合照(G304白色因為左鍵故障丟了，可惜！)
最近大概是白色控發作吧，買買買，都買。
是有想過是否一併入手SS廠的AEROX 3 Wireless Ghost 白色限量版，但那款洞洞真的太
多，已經到生理無法接受的程度了。(亂說)
幾款高階(貴)輕量無線滑鼠比較，羅技G PRO Superlight 及Roccat Kone Pro Air都屬於
中大型滑鼠，這兩款鼠都算是身形飽滿，適合趴握，但都是鼠屁股比較高，Model D
Wireless則是鼠背較高，趴握時鼠背不是抵在手心而是稍微往上抵在掌丘的位置，不過仍
是不錯的握持手感，側鍵相比羅技(太小)及ROCCAT(太高)更為適中，使用時也沒有誤觸問
題。
另一點關於表面塗層，Model D Wireless的霧面塗層有些許的止滑效果，個人覺得手感比
偏滑的G PRO Superlight及超滑的Kone Pro Air都還要更好，
不過也可能是個人的怪習慣，沒事會用指甲去摳摳滑鼠滾輪、用手指去滑滑左鍵，所以雖
然有人說Kone Pro Air的塗層很威猛，但他真的有夠滑，那股溜的手感至今我都還不能習
慣。
結論
使用兩天下來覺得Model D Wireless 扣除洞洞部分真的是個不錯的滑鼠，右手專用外型
加上輕量設計深得我心，左右鍵手感偏扎實，聲音略低沉，個人是還滿愛的。G PRO
Superlight及G304都屬於清亮偏尖銳的點擊聲，在激烈點擊時我都怕坐隔壁的老婆大人嫌
吵。
續航力我還沒測試出來是否如原廠標示，這兩天使用頻率不算高，電量沒動多少，不過剛
拿到時電量是61%，大概充了10分鐘就到90%多，充電速度還算快。
優點
1、右手專用設計。
2、尺寸偏大但仍維持69g輕巧重量。
3、3區幻彩RGB燈效。
4、價格TWD 3,190 還算適中。
缺點
1、密集恐懼症洞洞外觀(個人觀點)。
2、沒有USB接收器收納設計。
3、外殼組裝品質有改善空間(不知道是否為個案)。
4、黑色版比白色版好看(個人鍵盤觀察所得結論)。
--
※ 發信站: 批踢踢實業坊(ptt.cc), 來自: 220.129.146.173 (臺灣)
※ 文章網址: https://www.ptt.cc/bbs/Key_Mou_Pad/M.1634742347.A.8E6.html
※ 編輯: Ohmy (220.129.146.173 臺灣), 10/20/2021 23:05:59
其實這隻不是我的主力鼠 所以這周末才拿出來認真試電量 測試時燈效全關
周末幾天用下來 五晚上用了2-3小時 六整天用了4-5小時 日整天用了6-7個小時
五晚上用完放著沒關電源 隔天還是100%
六晚上用完94% 放著到隔天還是94%
日用了整天目前是84% 晚上再放著看看 放到周一晚上下班後來看剩多少電
更新
周一早上看83% 晚上9點左右看是81%
看起來電量消耗是還好 大概就符合原廠說的71hr吧(?) 待機目前看沒有猛噴電
※ 編輯: Ohmy (36.230.172.26 臺灣), 10/25/2021 20:55:47
... <看更多>