Lingjia Liu Recieves NSF/Intel-Funded Machine Learning for Wireless Networking Systems (MLWiNS) Award | ECE | Virginia Tech


Lingjia Liu Recieves NSF/Intel-Funded Machine Learning for Wireless Networking Systems (MLWiNS) Award

Artificial intelligence (AI) has a transformational effect in every industry and will likely be the foundation of a fourth industrial revolution. On the other hand, the application of AI and its associated machine learning (ML) tools within wireless networks, while promising, is still in its nascent stages.

The National Science Foundation, in partnership with Intel, has announced $9 Million in awards to 22 teams around the United States for the Machine Learning for Wireless Networking Systems (MLWiNS) program. The purpose of setting up the government and industry partnership program is to explore the use of Machine Learning techniques to advance wireless network design and operations to accelerate innovation in wireless systems.

Lingjia Liu from the Bradley Department of Electrical and Computer Engineering at Virginia Tech will lead the project entitled "Deep Neural Networks Meet Physical Layer Communications Learning with Knowledge of Structure" in collaboration with Lizhong Zheng from the Electrical Engineering and Computer Science Department at Massachusetts Institute of Technology.

This multi-university collaborative project focuses on the physical layer of wireless networks, where analytical solutions, clearly defined performance benchmarks, as well as clearly isolated modeling deficiencies are usually present. The critical challenge is to utilize the analytical results whenever possible and focus the powers of machine learning and artificial intelligence on those parts of the system where the conventional approach fails, including non-linear, non-Gaussian, and non-stationary elements. The two researchers will exploit information-theoretic tools to develop new algorithms that can better address non-linear distortions and relax simplifying assumptions on the noise and impairments encountered in wireless networks.

The MLWiNS award is an extremely competitive program with a very low funding rate. The total budget of the project is $810,000 for a period of three years. Liu and Virginia Tech's share of the award is approximately $410,000.

Liu, Associate Professor within the Bradley Department of Electrical & Computer Engineering at Virginia Tech, stated, "Some fundamental difficulties remain in applying learning-based data-driven techniques in communication problems. They generally cannot match the results from the more conventional model-based approaches in offering insights to understand entire families of communication problems."

"5G and Beyond networks need to support throughput, density, and latency requirements that are orders of magnitudes higher than what current wireless networks can support. They also need to be secure and energy-efficient," said Margaret Martonosi, Assistant Director for Computer and Information Science and Engineering (CISE) at the National Science Foundation.

Martonosi continued, "The MLWiNS program was designed to stimulate novel machine learning research that can help meet these requirements. The awards announced today seek to apply innovative machine learning techniques to future wireless network designs to enable such advances and capabilities."

Gabriela Cruz Thompson, Director of university research and collaborations at Intel Labs, added, "Since 2015, Intel and NSF have collectively contributed more than $30 million to support science and engineering research in emerging technology areas. MLWiNS is the next step in this collaboration and has the promise to enable future wireless systems that serve the world's rising demand for pervasive, intelligent devices."

Thyaga Nandagopal, Deputy Division Director at National Science Foundation, commented, "The intersection of AI and Wireless has the potential to simplify a complex decision-making domain (i.e., wireless networking) that touches upon policy, technology, economics, and human factors. This program will hopefully show the way forward.

Read More About the Full Program on Forbes.

Written by Greg Atkins