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shi yuanming; yang kai; yang zhanpeng; zhou yong - mobile edge artificial intelligence

Mobile Edge Artificial Intelligence Opportunities and Challenges

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Lingua: Inglese
Pubblicazione: 08/2021

Note Editore

Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains.

As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources.

  • Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission
  • Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface
  • Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning


I. Introduction and Overview 1. Primer on Artificial Intelligence 2. Overview of Edge AI Systems

II. Edge Inference 3. Model Compression for On-Device Inference 4. Wireless MapReduce for Device Distributed Inference 5. Wireless Cooperative Transmission for Edge Inference

III. Edge Training 6. Over-the-Air Computation for Federated Learning 7. Blind Over-the-Air Computation for Federated Learning 8. Reconfigurable Intelligent Surface Aided Federated Learning System

IV. Future Directions 9. Communication-Efficient Algorithms for Edge AI 10. Future Research Directions


Dr. Yuanming Shi is a tenured Associate Professor at the School of Information Science and Technology in ShanghaiTech University, China. He is an editor of IEEE Transactions on Wireless Communications and IEEE Journal on Selected Areas in Communications. His research areas include optimization, statistics, machine learning, signal processing, and their applications to 6G, IoT, and AI.
Mr. Kai Yang is currently at JD.com, Inc., China. His research interests include big data processing, mobile edge/fog computing, mobile edge artificial intelligence and dense communication networking. He has developed a wireless distributed computing framework for edge inference, and an over-the-air computation approach for edge federated machine learning.
Mr. Zhanpeng Yang is shortly to join the School of Information Science and Technology, ShanghaiTech University. He mainly focuses on developing reconfigurable intelligence surface based 6G wireless technologies for mobile edge AI systems.
Dr. Yong Zhou is an Assistant Professor at the School of Information Science and Technology, ShanghaiTech University China. His research areas include optimization, machine learning, analysis, and resource allocation, with applications to beyond 5G, IoT, and edge networks.

Altre Informazioni



Condizione: Nuovo
Dimensioni: 229 x 152 mm
Formato: Brossura
Pagine Arabe: 206

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