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

Dr. Yuanming Shi received the B.S. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. He received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), in 2015. Since September 2015, he has been with the School of Information Science and Technology in ShanghaiTech University, where he is currently a tenured Associate Professor. He visited University of California, Berkeley, CA, USA, from October 2016 to February 2017. Dr. Shi is a recipient of the 2016 IEEE Marconi Prize Paper Award in Wireless Communications, and the 2016 Young Author Best Paper Award by the IEEE Signal Processing Society. He is an editor of IEEE Transactions on Wireless Communications. His research areas include optimization, statistics, machine learning, signal processing, and their applications to 6G, IoT, AI and FinTech. In particular, at IEEE Global Communications Conference 2019, Dr. Shi gave a 3-hours tutorial titled "Mobile Edge Artificial Intelligence: Opportunities and Challenges .

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