Next-Generation Wireless Networks and Systems
Next-generation wireless networks will support Gbps+ data rates and sub-millisecond latency, which can enable a broad range of real-time applications including AR/VR, autonomous driving, and smart cities. Bringing future wireless networks to reality requires significant research across all layers of the network stack. Our research focuses on both the theoretical and experimental aspects of a wide range of enabling technologies including millimeter-wave communications, massive-antenna systems, optical-wireless communications, integrated communication and sensing, and edge cloud and computing. We also design practical, efficient, and scalable algorithms and systems, and develop customized prototypes and testbeds (such as the NSF-funded PAWR COSMOS Platform) to evaluate their performance in real-world scenarios.
AI/ML-enabled Wireless and Optical Networking
As we are embracing 5G and beyond-5G mobile networks, their network infrastructures are in need of a revolution in order to fulfill the massive computation needs, increasing hardware and software complexities, and the large volume of cross-domain data. By leveraging the ongoing revolution in Artificial Intelligence (AI) and machine learning (ML), our research focuses on AI- and ML-powered wireless networking at the edge, with the goal to create a new adaptable, scalable, and performance-aware mobile network infrastructure that can provide flexible services for heterogeneous use cases. We will also explore a data-driven approach to complement and augment existing algorithmic alternatives in the design of next-generation wireless networks and systems.
Spectrum Monitoring and Coexistence
The expanded spectrum usage in the 5G and beyond-5G eras inevitably calls for the coexistence of commercial services (e.g., cellular networks), non-commercial active users (e.g., weather satellite and GPS), and passive users (e.g., radio astronomy), leading to interference for both active and passive users. Our research focuses on the design of a cooperative network system for spectrum sharing and coexistence, where each receiver (RX) can detect interference in real-time, identify the type and source of interference, and more importantly, react to the interference by adopting selective interferer nulling, leveraging an intelligent network control architecture and machine learning (ML) techniques.
Prototyping and Testbed Development
Our research projects involve extensive experimentation of various hardware and software components, including the programmable software-defined radios (e.g., USRP) in sub-6 GHz and millimeter-wave frequencies, edge servers, high-speed coherent transceivers, etc. Our lab is equipped with a variety of these wireless, compute, and optical hardware. In addition, we also leverage the city-scale PAWR COSMOS Platform for various measurements and experiments.
Acknowledgments: Our research projects are supported in part by NSF grants ECCS-2434131 (NewSpectrum), CNS-2330333 (EAGER), AST-2232458 (SII-NRDZ), CNS-2211944 (CISE Core), CNS-2128638 (SWIFT), and CNS-2112562 (NAI), the SRC-DARPA JUMP 2.0 program (CUbiC – Center for Ubiquitous Connectivity), a Duke Science & Technology LAUNCH Grant, a Pratt Engineering Beyond the Horizon Initiative Grant, a Google Research Scholar Award, an IBM Academic Award, an NVIDIA Academic Grant, NTT Research Grants, an ACM SIGMOBILE Student Community Grant, and NEC Labs America Research Gifts. The findings, positions, or opinions of our research projects do not necessarily represent the official policy of any of these organizations.
