According to Penn State researchers, the development of millimeter-wave communication technologies could significantly improve the speed, latency, and reliability of wireless communications. Jing Yang, assistant professor of electrical engineering, received a four-year, $800,000 National Science Foundation grant to develop machine learning and large-scale optimization-based schemes to improve the technology.
“Millimeter wave spectrum offers 200 times more bandwidth than microwave frequencies, offering substantial promise toward the future of wireless networks, noting that the technology has not been widely adopted due to issues involving how the data propagates along its communication path.”
Jing Yang, assistant professor of Penn State electrical engineering
The millimeter-wave spectrum operates on a higher frequency than microwave communication and provides a larger bandwidth, enabling it to process more data. Some disruptive applications can be accommodated using this frequency band, such as collaborative autonomous driving and augmented and virtual reality.
For example, raw collaborative sensor data sharing in autonomous driving can enable some safety and cloud-controlled driving applications. Similarly, tracking farm equipment with high-definition sensor data from crops in precision agriculture and the tracking robotic equipment in the mining industry can benefit people, processes, and environmental conditions because of the high bandwidth requirement from the millimeter-wave spectrum.
One of the challenges of the research had been the sensitive nature of the millimeter-wave spectrum’s high frequency. Cellphones, for example, operate on a lower frequency, and blocking them with a hand or using them while walking will not impact their wireless connection. However, the millimeter-wave spectrum is sensitive to blockage from obstacles, including users’ bodies near the device.
“We have to estimate which channel of communication is good — and do this very quickly, Channel estimation has to be done at a very fast rate. By leveraging machine learning and optimization, we’re finding computationally efficient ways to estimate the channel quickly and enable seamless handovers and connectivity as devices move.”
It is not too much of a problem in a manufacturing facility because the objects are stationary. The challenge appears when the devices are moving. Jing Yang and two co-principal investigators address this issue by developing approaches to make communication as fast, stable, and continuously reliable as possible.
