"How can machine learning help the design of future communication networks - and how can future networks meet the demands of emerging machine learning applications? Discover the interactions between two of the most transformative and impactful technologies of our age in this comprehensive book. First, learn how modern machine learning techniques, such as deep neural networks, can transform how we design and optimize future communication networks. Accessible introductions to concepts and tools are accompanied by numerous real-world examples, showing you how these techniques can be used to tackle longstanding problems. Next, explore the design of wireless networks as platforms for machine learning applications - an overview of modern machine learning techniques and communication protocols will help you to understand the challenges, while new methods and design approaches will be presented to handle wireless channel impairments such as noise and interference, to meet the demands of emerging machine learningapplications at the wireless edge"--
Arvustused
'Recommended.' J. Brzezinski, Choice
Muu info
Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.
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ix | |
| Preface |
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xiii | |
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1 Machine Learning and Communications: An Introduction |
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1 | (22) |
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Part I Machine Learning for Wireless Networks |
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2 Deep Neural Networks for Joint Source-Channel Coding |
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23 | (32) |
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55 | (22) |
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4 Channel Coding via Machine Learning |
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77 | (33) |
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5 Channel Estimation, Feedback, and Signal Detection |
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110 | (35) |
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6 Model-Based Machine Learning for Communications |
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145 | (37) |
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7 Constrained Unsupervised Learning for Wireless Network Optimization |
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182 | (30) |
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8 Radio Resource Allocation in Smart Radio Environments |
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212 | (19) |
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9 Reinforcement Learning for Physical Layer Communications |
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231 | (54) |
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10 Data-Driven Wireless Networks: Scalability and Uncertainty |
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285 | (32) |
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11 Capacity Estimation Using Machine Learning |
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317 | (36) |
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Part II Wireless Networks for Machine Learning |
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12 Collaborative Learning over Wireless Networks: An Introductory Overview |
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353 | (32) |
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13 Optimized Federated Learning in Wireless Networks with Constrained Resources |
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385 | (24) |
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14 Quantized Federated Learning |
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409 | (25) |
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15 Over-the-Air Computation for Distributed Learning over Wireless Networks |
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434 | (23) |
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16 Federated Knowledge Distillation |
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457 | (29) |
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17 Differentially Private Wireless Federated Learning |
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486 | (26) |
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18 Timely Wireless Edge Inference |
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512 | (27) |
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| Index |
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539 | |
Yonina C. Eldar is a professor of Electrical Engineering at the Weizmann Institute of Science, where she heads the Center for Biomedical Engineering and Signal Processing. She is also a visiting professor at MIT and at the Broad Institute, and an adjunct professor at Duke University. She is a member of the Israel Academy of Sciences and Humanities, an IEEE fellow, and a EURASIP fellow. Andrea Goldsmith is the Dean of Engineering and Applied Science and the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University. She is a member of the US National Academy of Engineering and the American Academy of Arts and Sciences. In 2020, she received the Marconi Prize. Deniz Gunduz is a professor of Information Processing in the Electrical and Electronic Engineering Department of Imperial College London in the UK, where he serves as the Deputy Head of the Intelligent Systems and Networks Group. He is also a part-time faculty member at the University of Modena and Reggio Emilia in Italy. H. Vincent Poor is the Michael Henry Strater University Professor at Princeton University. He is a member of the US National Academy of Engineering and the US National Academy of Sciences. In 2017, he received the IEEE Alexander Graham Bell Medal.
