Wireless Communication Using Deep Learning Techniques for Neuromorphic VLSI Computing
暫譯: 使用深度學習技術進行無線通信的神經形態VLSI計算
El-Khatib, Ziad, Moussa, Sherif
- 出版商: Springer
- 出版日期: 2025-01-16
- 售價: $2,310
- 貴賓價: 9.5 折 $2,195
- 語言: 英文
- 頁數: 99
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 3031737997
- ISBN-13: 9783031737992
-
相關分類:
VLSI、DeepLearning
海外代購書籍(需單獨結帳)
商品描述
This book describes Deep Learning-based architecture design for intelligent wireless communication systems and specifically for Deep Learning-based receiver design. Deep Learning-based architecture design utilizes Deep Learning (DL) techniques to reformulate the traditional block-based wireless communication architecture. Deep Learning-based algorithm design utilizes Deep Learning methods to speed up the processing at a guaranteed high accuracy performance. Automatic signal modulation classification in AI-based wireless communication can be done using deep learning techniques to improve dynamic spectrum allocation. Automatic signal modulation recognition in wireless communication is described using Deep Learning techniques to improve resource shortage and spectrum utilization efficiency. Moreover, using deep learning neural network circuit methods and doing parallel computations on hardware can reduce costs. Spiking neural network (SNN) provides a promising solution for low-power hardware for neuromorphic computing. Spiking Neural Networks circuit functions with a pre-trained network's weights consume less power. Spiking neural network is more promising than other neural networks that can pave a new way for low-power computing applications. Analog VLSI is utilized to design spiking neural networks circuits such as silicon synapse and CMOS neuron.
商品描述(中文翻譯)
本書描述了基於深度學習的智能無線通信系統架構設計，特別是基於深度學習的接收器設計。基於深度學習的架構設計利用深度學習（Deep Learning, DL）技術重新構建傳統的區塊式無線通信架構。基於深度學習的演算法設計利用深度學習方法加速處理，同時保證高準確度的性能。在基於人工智慧（AI）的無線通信中，自動信號調變分類可以使用深度學習技術來改善動態頻譜分配。無線通信中的自動信號調變識別使用深度學習技術來改善資源短缺和頻譜利用效率。此外，使用深度學習神經網絡電路方法並在硬體上進行並行計算可以降低成本。脈衝神經網絡（Spiking Neural Network, SNN）為低功耗神經形態計算的硬體提供了一個有前景的解決方案。脈衝神經網絡電路功能使用預訓練網絡的權重消耗更少的電力。脈衝神經網絡比其他神經網絡更具潛力，為低功耗計算應用開辟了新途徑。類比VLSI被用來設計脈衝神經網絡電路，如矽突觸和CMOS神經元。
作者簡介
Dr. Ziad El-Khatib PhD in Electrical and Computer Engineering from Carleton University Canada. Assistant professor at Canadian University Dubai. Dr. Ziad El-khatib received his M.A.Sc. from Carleton University Canada and his B.A.Sc. in Electrical Engineering from University of Ottawa Canada. He has several years of industry design experience in the field of communication integrated circuits and semiconductors at various companies including Nortel Networks, Harris Corporation, Corel Corporation, Chrysalis-ITS Semiconductor and Itron Inc. USA where he was also an adjunct professor. He was assistant professor in the faculty of Electrical and Computer Engineering at Rochester Institute of Technology Dubai. He is currently assistant professor in the faculty of Electrical and Computer Engineering at Canadian University Dubai. His research interests include silicon based integrated circuits for radio frequency communications and deep learning AI-based radio systems for wireless AI communications. He has a book published through Springer on radio frequency amplification and linearization techniques and numerous IEEE journal and conference papers.
Dr. Sherif Moussa PhD in Electrical and Computer Engineering from University of Quebec Trois-Riviers, Canada. Associate professor at Canadian University Dubai. Dr. Sherif Moussa received his PhD in Electrical and Computer Engineering from University of Quebec Trois-Riviers, Canada, and his MSc degree in Electrical and Computer Engineering form University of Waterloo, Canada. His research areas are wireless communication, computer networks, and VLSI design. His research specifically focuses on MIMO-OFDM algorithms, multiple access OFDM, FPGA design and optimization. Dr. Moussa joined CUD in 2007 where he currently is working as an Associate Professor at School of Engineering. Prior to joining CUD, he was a lecturer at School of Engineering, Centennial College, Toronto, Canada. Dr. Moussa is currently an active researcher who published in many international journals and conferences related to his field and he also currently serve as a reviewer and technical committee member for many international conferences.
作者簡介(中文翻譯)
Dr. Ziad El-Khatib 擁有加拿大卡爾頓大學的電機與計算機工程博士學位。現任加拿大大學杜拜校區的助理教授。Dr. Ziad El-Khatib 於卡爾頓大學獲得碩士學位（M.A.Sc.），並在加拿大渥太華大學獲得電機工程學士學位（B.A.Sc.）。他在通信集成電路和半導體領域擁有多年的行業設計經驗，曾在多家公司工作，包括 Nortel Networks、Harris Corporation、Corel Corporation、Chrysalis-ITS Semiconductor 和 Itron Inc.（美國），並曾擔任兼任教授。他曾在羅切斯特理工學院杜拜校區的電機與計算機工程系擔任助理教授。目前，他是加拿大大學杜拜校區電機與計算機工程系的助理教授。他的研究興趣包括基於矽的射頻通信集成電路以及基於深度學習的無線 AI 通信射頻系統。他出版了一本關於射頻放大和線性化技術的書籍，並在多個 IEEE 期刊和會議上發表了多篇論文。
Dr. Sherif Moussa 擁有加拿大魁北克大學特魯瓦河的電機與計算機工程博士學位。現任加拿大大學杜拜校區的副教授。Dr. Sherif Moussa 在魁北克大學特魯瓦河獲得電機與計算機工程博士學位，並在加拿大滑鐵盧大學獲得電機與計算機工程碩士學位（MSc）。他的研究領域包括無線通信、計算機網絡和 VLSI 設計。他的研究特別專注於 MIMO-OFDM 演算法、多重存取 OFDM、FPGA 設計與優化。Dr. Moussa 於 2007 年加入加拿大大學杜拜校區，目前在工程學院擔任副教授。在加入加拿大大學杜拜校區之前，他曾在加拿大多倫多的 Centennial College 工程學院擔任講師。Dr. Moussa 目前是一位活躍的研究者，已在許多國際期刊和會議上發表與其領域相關的研究，並且目前擔任多個國際會議的審稿人和技術委員會成員。
