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Wireless & Secure Systems

Wireless & Security Systems

ECE research in wireless and secure systems explores creative, efficient ways to meet the increasing demand for secure, efficient wireless access. These include dynamic methods for sharing the finite electromagnetic spectrum, authenticating cryptographic signatures, ensuring physical-layer resilience, harnessing new technology and artificial intelligence to improve wireless performance and protect wireless devices, and leveraging machine learning techniques to design the next generation of powerful wireless communication systems.

Associated Faculty

Robert W. McGwier

Carl B. Dietrich

Lingjia Liu

Jeffrey H. Reed

Jung-Min (Jerry) Park

Joseph G. Tront

R. Michael Buehrer


Highlighted Research

ECE research is reimagining digital signal processing within the context of deep learning. By replacing signal processing logic with neural networks trained to perform the same task, we can eliminate the need for traditional serial processing, enabling significant latency and computational complexity reductions. Similarly, an entirely new world of cognitive radio is possible when the signal processing environment is built natively on top of a machine learning engine.

ECE researchers are studying the communications and networking requirements of V2X communication, which is the communication between a vehicle and anything that may interact with it—another vehicle, infrastructure, the cloud, or a passing pedestrian. V2X has applications in vehicular safety, efficient traffic management, and infotainment, but our research currently focuses on vehicular safety applications.

In the future, cars will be able to communicate with most aspects of their surroundings, including the roads, buildings, and other vehicles.

As the demand for spectrum access skyrockets, 5 GHz bands have emerged as the most coveted bands for launching new wireless applications and services, and ECE researchers are devising ways that heterogeneous wireless technologies can coexist there.

ECE investigators and colleagues will be deploying an instrumentation suite that will expand Virginia Tech’s COgnitive Radio NET-work (CORNET) testbed to include airborne mobile radio frequency (RF) nodes to create an outdoor testbed for RF experimentation with unmanned aircraft systems (UAS).

Virginia Tech’s COgnitive Radio NETwork (CORNET) testbed allows researchers to test sophisticated cognitive radio networks, generating the data they need to explore and optimize new technologies.

In contested and congested environments, meeting quality of service requirements for wireless systems can be challenging. We are investigating distributed, optimal approaches to resource allocation in scenarios such as LTE carrier aggregation, radar spectrum sharing, and M2M communications for industrial control systems. Solutions range from straightforward utility maximization to secure auction techniques.

Resource allocation is challenging when considering the many users of a cellular network, like this system model with two groups of users.

ECE researchers are using brain-inspired machine learning techniques to increase the energy efficiency of wireless receivers by combining multiple-input multiple-output (MIMO) techniques with orthogonal frequency division multiplexing (OFDM). Using artificial neural networks, we are creating a completely new framework by detecting transmitted signals directly at the receiver, which minimize inefficiency.