Using AI to hunt for slow zones in overcrowded networks | ECE | Virginia Tech


Using AI to hunt for slow zones in overcrowded networks

Lingjia Liu at his desk.
LINGJIA LIU, ECE Associate Professor, explores new ways to meet the skyrocketing demand for wireless spectrum.

Within the next four years, an estimated 29 billion connected devices will compete for a slice of the already overcrowded radio spectrum, according to industry trend predictions like the Ericsson Mobility Report.

By improving spectrum efficiency, tapping unoccupied channels, and establishing protocols for sharing previously restricted bands, ECE's Lingjia Liu is exploring new ways to meet the skyrocketing demand.

Minimizing interference and allocating spectrum

Liu, who joined the department as an associate professor in 2017, is developing methods to manage wireless interference (which can mean dropped calls or poor connections) and find the best way to allocate spectrum access in heavily used networks.

He is incorporating a technique called spatial sensing for device-to-device communication, which allows devices to identify and use local empty spaces and periods of low traffic within the network bands. Short-range and local communication strategies like this help optimize spectrum and energy use.

Liu plans to develop a framework for analysis and design based on detection theory and stochastic geometry. After determining the best design and techniques to use, Liu's team will evaluate the effects of this strategy on overall network performance.

Tapping temporary availability for short-term communication

Visualization of network
Spatial sensing for device-to-device communication can help optimize spectrum and energy use.

Many researchers have been exploring meeting wireless demand by increasing the availability of the current infrastructure. However, "current hardware platforms present formidable challenges for supporting high-computational complexity and low-power consumption," says Liu.

Liu and Yang (Cindy) Yi, an assistant professor in the ECE department, are developing another option inspired, in part, by the human brain.

They are designing a network architecture that allows devices to dynamically search for spectrum bands that are temporarily not at full capacity in their area and use them for short-range communications.

Because dynamic spectrum access is so computationally complex, the proposed network's computing devices will mimic the neurobiological architecture of the human brain—one of the most efficient and sophisticated systems in the known universe.

This new wireless network design shifts the focus from a centralized base-station-controlled approach to a more decentralized system, says Liu. In the new model, individual users will play stronger roles in spectrum access, changing the network topology by using smart computing devices.

"In this way, we will enable our nation's next-generation wireless network in an intelligent, spectrum-efficient, and energy-efficient dynamic spectrum environment," says Liu.

Lingjia Liu

  • Joined ECE August 2017
  • Associate professor, University of Kansas, 2016-2017
  • Assistant professor, University of Kansas, 2011-2016
  • Senior research/standard engineer, Samsung Research America, 2008-2011
  • Ph.D., electrical and computer engineering, Texas A&M University, 2008
  • B.S., electronic engineering, Shanghai Jiao Tong University, 2003
  • Summer Visiting Faculty Fellow, U.S. Air Force Research Laboratory, 2013-2017
  • Best Paper Award, IEEE Globecom, 2016
  • Individual Gold Medal, Samsung Telecommunications America, 2011