Building efficient communication networks that can enable the emerging wireless applications is a national priority for the United States and many other countries around the world. Future wireless communication networks will be required to provide significantly higher data rates compared to the current networks and to support highly-mobile low-latency applications such as virtual/augmented reality and autonomous vehicles. To respond to these requirements, this project will leverage machine learning to design next-generation wireless communication systems that can efficiently increase the number of antennas at the transmitters and receivers, which is essential for supporting high data rates. This project will also make use of the other sensory information such as GPS positions and camera images to improve the performance of the wireless communication networks. On the educational front, this project will lead to the development of undergraduate and graduate course materials and will provide opportunities for enhancing the basic engineering skills of undergraduate students through engaging them into building proof-of-concept prototypes. Further, the proposed work in this project is expected to have impact on a number of areas through the technology transfer to industry and the public release of all the machine learning datasets and algorithmic implementations developed during the project.

The main goal of this project is to enable highly-mobile large-scale MIMO systems in realistic dynamic environments. To achieve this goal, fundamentally new machine learning approaches that are capable of efficiently leveraging the prior system observations and the side multi-modal sensory information will be developed. The project has several inter-related thrusts: (i) Developing statistical channel prediction approaches for fast yet robust channel acquisition in dynamic massive MIMO systems; (ii) characterizing the fundamental conditions under which across-frequency and across-space channel prediction is feasible; (iii) Designing robust and adaptive multi-user precoding for highly-mobile massive MIMO systems leveraging learning-based conditional channel covariance prediction; (iv) Developing a novel multi-modal learning framework for fast mmWave beam prediction in line-of-sight and non-line-of-sight scenarios, relying on the efficient fusion of various sensory data, such as positions, sub-6GHz channels, and visual data; (v) Investigating the design of efficient multi-modal learning approaches and algorithms for dynamic mmWave blockage prediction using advanced visual scene understanding and end-to-end learning.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Electrical, Communications and Cyber Systems (ECCS)
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Zhengdao Wang
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Arizona State University
United States
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