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Kniha Wireless Communication Using Deep Learning Techniques for Neuromorphic VLSI Computing Ziad El-Khatib

Wireless Communication Using Deep Learning Techniques for Neuromorphic VLSI Computing

DE

Autor: Ziad El-Khatib, Sherif Moussa
Jazyk: Angličtina
Vazba: Brožovaná
Vydavatel: Springer, Berlin
Dostupnost: Skladem u dodavatele
Odesíláme za 8-11 dnů
1 120 Kč
This book describes Deep Learning-based architecture design for intelligent wireless communication s...

Informace o knize

Autor
Ziad El-Khatib, Sherif Moussa
Jazyk
Angličtina
Vazba
Kniha - Brožovaná
Vydáno
2026
Stránek
99
EAN
9783031738029
Enbook ID
50792867
Vydavatel
Springer, Berlin
Hmotnost
233
Rozměry
168 x 6 x 240

Kompletní popis

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.