vlsi wireless communication (51 Ergebnisse)

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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Hardcover. Zustand: new. Hardcover. 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 networks 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardback. Zustand: New. 2024 ed. 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.

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hardcover. Zustand: New.

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Hardcover. Zustand: Brand New. 150 pages. 9.44x6.61x9.69 inches. In Stock.

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Paperback or Softback. Zustand: New. VLSI for Wireless Communication. Book.

Hardcover. Zustand: new. Hardcover. 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 networks 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Buch. Zustand: Neu. Neuware -VLSI for Wireless Communication, Second Edition, an advanced level text book, takes a system approach starting with an overview of the most up to date wireless systems and the transceiver architecture available today. Wireless standards are first introduced (updated to include the most recent 3G/4G standards in the second edition), and translates from a wireless standard to the implementation of a transceiver. This system approach is particularly important as the level of integration in VLSI increases and coupling between system and component design becomes more intimate.VLSI for Wireless Communication, Second Edition, illustrates designs with full design examples. Each chapter includes at least one complete design example that helps explain the architecture/circuits presented in this text. This book has close to 10 homework problems at the end of each chapter. A complete solutions manual is available on-line.VLSI for Wireless Communication, Second Edition, is designed as a primary text book for upper-undergraduate level students and graduate level students concentrating on electrical engineering and computer science. Professional engineers and researchers working in wireless communications, circuit design and development will find this book valuable as well.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 572 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -VLSI for Wireless Communication, Second Edition, an advanced level text book, takes a system approach starting with an overview of the most up to date wireless systems and the transceiver architecture available today. Wireless standards are first introduced (updated to include the most recent 3G/4G standards in the second edition), and translates from a wireless standard to the implementation of a transceiver. This system approach is particularly important as the level of integration in VLSI increases and coupling between system and component design becomes more intimate.VLSI for Wireless Communication, Second Edition, illustrates designs with full design examples. Each chapter includes at least one complete design example that helps explain the architecture/circuits presented in this text. This book has close to 10 homework problems at the end of each chapter. A complete solutions manual is available on-line.VLSI for Wireless Communication, Second Edition, is designed as a primary text book for upper-undergraduate level students and graduate level students concentrating on electrical engineering and computer science. Professional engineers and researchers working in wireless communications, circuit design and development will find this book valuable as well.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 572 pp. Englisch.

Buch. Zustand: Neu. Neuware Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 116 pp. Englisch.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Hardcover. Zustand: new. Hardcover. 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 networks 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.