The fifth generation (5G) network offers enhanced mobile broadband (eMBB), critical machine type communications (cMTC), massive machine type communications (mMTC), and fixed wireless access services. Promising to provide much higher data volume, network energy efficiency and spectral efficiency, and much lower latency than the fourth generation (4G) network, 5G aims to use one network to support multiple industries and use cases. Real-time automation, enhanced video services, monitoring and tracking, connected vehicles, hazard and maintenance sensing, smart surveillance, remote operations, autonomous robots, augmented reality (AR), top the list. Mission-critical drones, smart factory/manufacturing, next-generation virtual and augmented reality (VR/AR) glasses, low-complexity massive IoT devices such as sensors and meters, have been identified as the leading killer applications enabled by 5G.
Demanding QoS needs to be guaranteed in the above killer 5G applications. Many challenges need to be addressed to achieve this, e.g., the wireless hostile environment with a lot of operating and moving machines, blockages, environment noises and interferences, the high dimensionality and coupled interaction of the optimizing parameters, the fragmented radio resources and spectrum access, etc. This motivates us to research in applying cognitive learning and AI to learn and adapt to the dynamically changing environment and deliver the requested QoS with the optimal spectrum and power efficiency. Specifically, we work on radio access network, network slicing and virtualization, and build a research test bed to validate our proposed advanced features.
While one of our research focus is to optimize the 5G and beyond systems and networks, we also study how 5G and beyond systems and network enable further intelligence and additional services. Our current work in the latter includes 5G-based fine-grain localization and tracking, joint radar-communication design, etc.