Associate Professor Mingyue Ji

University of Utah electrical and computer engineering associate professors Rong-Rong Chen and Mingyue Ji have received a $400,000 grant from the National Science Foundation to support the development of new technologies to build smart radio environments. This grant is part of a partnership between the U.S., Northern Ireland, and the Republic of Ireland.

Partners in Northern Ireland include Dr. Dmitry Zelenchuk and Dr. Muhammad Ali Babar Abbasi from Queen’s University of Belfast (QUB), and partners in the Republic of Ireland include Dr. Arman Farhang from Trinity College, Dublin (TCD).

Chen and Ji’s grant, titled “Smart Radio Environments with Reconfigurable Intelligent Surfaces,” seeks to enhance and improve mmWave communication through the development of an innovative Reconfigurable Intelligent Surface (RIS)—aided system.

Associate Professor Rong-Rong Chen

“If you look at the next generation communication systems, due to increasing requirements for data rates and the great demand for a variety of services, we are moving into very high frequencies – in the mmWave frequency range, due to the massive bandwidth availability (at least 800MHz),” explains Chen. “When we move into mmWave bands, there are lots of challenges because mmWave communication suffers from severe signal blockage and high directionality, so the signal transmission is often unreliable.”

“In this project, we will address this issue by developing a new technology called ‘Reconfigurable Intelligent Surfaces’. An RIS is typically a very large planar surface consisting of a high number of passive elements,” says Chen. “Each of these elements can be individually tuned and adjusted, allowing the RIS to re-engineer a wireless channel suffering from blockage by controlling where the signals are going to make the combined signal stronger.”

The challenges of using RIS-aided mm-Wave communication include the limited phase accuracy in current hardware implementations, severe Radio Frequency (RF) impairments, and fast time varying, atypical interference patterns, and non-linearity nature of the system.

The key idea of Chen and Ji’s approach is to apply modern machine learning techniques including deep learning, meta-learning, reinforcement learning, and more to various design aspects in the proposed network, from the physical layer to the Medium Access Control (MAC) layer. Dr. Farhang from TCD will be focusing on the practical signal design perspective to combat the severe RF impairments, while Dr. Zelenchuk and Dr. Abbasi of QUB will study the design of RIS radio hardware.

“The outcome of this project will lead to increased coverage of mmWave wireless networks and provide the desired reliability that many 6G applications will require.” says Chen. “These improvements will be applicable to many environments and devices, not only cellular networks.”

The NSF grant will fund this project for three years. To view the project abstract and grant details from NSF, click here.

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