Machine Learning and Wireless Communications
-10%
portes grátis
Machine Learning and Wireless Communications
Guenduez, Deniz; Eldar, Yonina C.; Poor, H. Vincent; Goldsmith, Andrea
Cambridge University Press
08/2022
554
Dura
Inglês
9781108832984
15 a 20 dias
1200
Descrição não disponível.
Preface; 1. Machine learning and communications: an introduction Deniz Guenduez, Yonina Eldar, Andrea Goldsmith and H. Vincent Poor; Part I. Machine Learning for Wireless Networks: 2. Deep neural networks for joint source-channel coding David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Guenduez and Andrea Goldsmith; 3. Neural network coding Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak and Muriel Medard; 4. Channel coding via machine learning Hyeji Kim; 5. Channel estimation, feedback and signal detection Hengtao He, Hao Ye, Shi Jin and Geoffrey Y. Li; 6. Model-based machine learning for communications Nir Shlezinger, Nariman Farsad, Yonina Eldar and Andrea Goldsmith; 7. Constrained unsupervised learning for wireless network optimization Hoon Lee, Sang Hyun Lee and Tony Q. S. Quek; 8. Radio resource allocation in smart radio environments Alessio Zappone and Merouane Debbah; 9. Reinforcement learning for physical layer communications Philippe Mary, Christophe Moy and Visa Koivunen; 10. Data-driven wireless networks: scalability and uncertainty Feng Yin, Yue Xu and Shuguang Cui; 11. Capacity estimation using machine learning Ziv Aharoni, Dor Zur, Ziv Goldfeld and Haim Permuter; Part II. Wireless Networks for Machine Learning: 12. Collaborative learning on wireless networks: an introductory overview Mehmet Emre Ozfatura, Deniz Guenduez and H. Vincent Poor; 13. Optimized federated learning in wireless networks with constrained resources Shiqiang Wang, Tiffany Tuor and Kin K. Leung; 14. Quantized federated learning Nir Shlezinger, Mingzhe Chen, Yonina Eldar, H. Vincent Poor and Shuguang Cui; 15. Over-the-air computation for distributed learning over wireless networks Mohammad Mohammadi Amiri and Deniz Guenduez; 16. Federated knowledge distillation Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim and Mehdi Bennis; 17. Differentially private wireless federated learning Dongzhu Liu, Amir Sonee, Stefano Rini and Osvaldo Simeone; 18. Timely wireless edge inference Sheng Zhou, Wenqi Shi, Xiufeng Huang and Zhisheng Niu.
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Preface; 1. Machine learning and communications: an introduction Deniz Guenduez, Yonina Eldar, Andrea Goldsmith and H. Vincent Poor; Part I. Machine Learning for Wireless Networks: 2. Deep neural networks for joint source-channel coding David Burth Kurka, Milind Rao, Nariman Farsad, Deniz Guenduez and Andrea Goldsmith; 3. Neural network coding Litian Liu, Amit Solomon, Salman Salamatian, Derya Malak and Muriel Medard; 4. Channel coding via machine learning Hyeji Kim; 5. Channel estimation, feedback and signal detection Hengtao He, Hao Ye, Shi Jin and Geoffrey Y. Li; 6. Model-based machine learning for communications Nir Shlezinger, Nariman Farsad, Yonina Eldar and Andrea Goldsmith; 7. Constrained unsupervised learning for wireless network optimization Hoon Lee, Sang Hyun Lee and Tony Q. S. Quek; 8. Radio resource allocation in smart radio environments Alessio Zappone and Merouane Debbah; 9. Reinforcement learning for physical layer communications Philippe Mary, Christophe Moy and Visa Koivunen; 10. Data-driven wireless networks: scalability and uncertainty Feng Yin, Yue Xu and Shuguang Cui; 11. Capacity estimation using machine learning Ziv Aharoni, Dor Zur, Ziv Goldfeld and Haim Permuter; Part II. Wireless Networks for Machine Learning: 12. Collaborative learning on wireless networks: an introductory overview Mehmet Emre Ozfatura, Deniz Guenduez and H. Vincent Poor; 13. Optimized federated learning in wireless networks with constrained resources Shiqiang Wang, Tiffany Tuor and Kin K. Leung; 14. Quantized federated learning Nir Shlezinger, Mingzhe Chen, Yonina Eldar, H. Vincent Poor and Shuguang Cui; 15. Over-the-air computation for distributed learning over wireless networks Mohammad Mohammadi Amiri and Deniz Guenduez; 16. Federated knowledge distillation Hyowoon Seo, Seungeun Oh, Jihong Park, Seong-Lyun Kim and Mehdi Bennis; 17. Differentially private wireless federated learning Dongzhu Liu, Amir Sonee, Stefano Rini and Osvaldo Simeone; 18. Timely wireless edge inference Sheng Zhou, Wenqi Shi, Xiufeng Huang and Zhisheng Niu.
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