- A C-V2X Platform Using Transportation Data and Spectrum-Aware Sidelink Access , 2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) (2021)
- FracBot Technology for Mapping Hydraulic Fractures , SPE JOURNAL (2021)
- GCN-CNVPS: Novel Method for Cooperative Neighboring Vehicle Positioning System Based on Graph Convolution Network , IEEE ACCESS (2021)
- SDVEC: Software-Defined Vehicular Edge Computing with Ultra-Low Latency , IEEE COMMUNICATIONS MAGAZINE (2021)
- TULVCAN: Terahertz Ultra-broadband Learning Vehicular Channel-Aware Networking , IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (IEEE INFOCOM WKSHPS 2021) (2021)
- Wireless Networked Multirobot Systems in Smart Factories , PROCEEDINGS OF THE IEEE (2021)
- Eco-Vehicular Edge Networks for Connected Transportation: A Distributed Multi-Agent Reinforcement Learning Approach , 2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL) (2020)
- Unsupervised ResNet-Inspired Beamforming Design Using Deep Unfolding Technique , 2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) (2020)
- Software-Defined architecture for QoS-Aware IoT deployments in 5G systems , AD HOC NETWORKS (2019)
- A Software-Defined Networking based Architecture for QoS-Aware IoT Communication in 5G Systems , (2018)
This project investigates programmable edge to cloud infrastructure and management tools, which will ensure end-to-end transmission performance. The work focuses on integrating optical core networks and edge networks and provides time-efficient management and orchestrations to handle diverse service-level agreements of multiple concurrent end-to-end traffic flows.
This project aims to investigate and demonstrate the loss of a satellite node of multiple satellites (cluster or swarm) networking, such as MIMO or others, with reloading ground usersâ€™ traffic on a prioritized basis under the evolving threats. The new spatial dimension of satellite swarms unprecedentedly takes the systemâ€™s resilience and self-healing capability to the next level.
This project aims at addressing the technical issues in the Space-Based Adaptive Communications Node (Space-BACN) by providing reliable connections between satellites in different constellations managed by diverse operators, including the government and the commercials, using optical inter-constellation links. Space-BACN should offer a coordination protocol and APIs to serve satellites in different constellations using a hierarchical architecture to enable cross-constellation connectivity.
This project aims to develop AI-native federated slicing and orchestration that enables the synergy of federated AI and network slicing for mega-constellation management and AI/real-time services. A federated edge learning setting facilitates multi-tier, multi-domain AI executions by jointly optimizing resource and networking configurations of ground and space tiers and multiple constellations. Hence, the proposed designs will create high-throughput, reliable end-to-end transmissions for global connectivity.
Proliferated low earth orbit (p-LEO) satellite constellations have emerged as enabling technologies for 6G networks due to their potential in global coverage and ubiquitous services, especially for rural and remote areas. The need for access equality via p-LEO satellitesÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ high communications capacity with ultra-wide ranges is more urgent than ever, as we have abruptly switched to remote living in the past year due to the pandemic. However, no convergent solution currently exists for p-LEO-based satellite networking infrastructures that leverage the synergy of space and terrestrial systems. Fundamental architectural, management and operational changes are still urgently required to realize such ground-space ecosystems. This project presents a serverless software-defined architecture that dynamically orchestrates communications and computation resources for a diverse set of 6G service-level agreements. This project provides a multi-tier machine learning framework that uses a unified control platform to optimize networking and resource configurations according to space and ground tiersÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢ peculiarities and interactions. The intelligence within collaborative learners (i.e., ground stations and satellites with computing capabilities) can realize efficient broadband access for multiple ground users. The proposed architecture can further establish high-throughput, reliable end-to-end transmissions through software-defined internetworking for global connectivity. The innovative use cases and ecosystem enhancement, including Ka-band adoptions, automatic network slicing, application-defined interfaces, are also investigated to boost end-to-end performance and significantly impact future human society.
This project aims at providing a feasibility study about anti-jamming techniques for newly emerged MIMO p-LEO satellite (or LEO satellite swarm) systems. LEO satellites located 1,200 km above the EarthÃ¢â‚¬â„¢s surface provide ultra-wide coverage and light-of-sight (LOS) communications to ground terminals. By equipping multiple antennas on satellites and ground terminals, MIMO LEO satellites can exploit MIMO technologies with the LOS channels to enable more robust air-to-ground communications with higher throughput. This research will introduce an in-depth investigation and propose novel algorithms to realize the potential of anti-jammed MIMO p-LEO satellites.
Center for Excellence on Connected Autonomous Vehicles NC-CAV with Project 1 CAV Impacts on Traffic Intersection Capacity and Transportation Revenue Collections, Project 2 Assessing NC Readiness for CAVs in Traditional and Emerging infrastructure needs, and Project 3 Developing and implementing CAV-UAV Collaboration for Advancing the Transportation systems.
The steep traffic growth on the Internet (+20-30%/year) continues and is accelerated due to the COVID-19 pandemic. The widespread teleconferencing and working at remote sites further boost traffic globally by 50% in a year. To fully take advantage of the 5G and future 6G mobile communication infrastructure and the considerable computation and storage resources in data centers, we have to bridge these in an end-to-end manner with minimized latency and broad bandwidth. This project will develop future programmable and resilient converged wireless-optical networks for next-generation wireless applications and mission-critical services.
This project will employ in-network computation at 5G wireless edges and enable data processing to occur in the middle of the transmissions between multi-agents and remote cloud servers. Thus, it forges effective convergence of communication, computing, and learning with regard to wireless link bandwidths, available computing power, and collected data's statistical features. Also, this project will provide a privacy-preserving framework from decentralized data by exploiting in-network processing operations. The proposed framework can achieve sophisticated and heavy-loaded machine learning algorithms through multiple low-end control units. It, in turn, preserves the data privacy for massive mobile user information in ICT or sensing information and intelligent manufacture/control commands in industrial scenarios. On the other hand, the proposed solution can also offload computation to the networking infrastructure, releasing the burden of multi-agents.
Aiming at UNÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s SDGs #9, this project will initiate collaborative research discussion about developing scalable and resilient cyberinfrastructure for UAV-enabled wireless networks in mission-critical services. The PIs will discuss several core research items in this initial discussion via five in-person team meetings, i.e., one in Nagoya (Spring), one in Raleigh (Summer), one in Adelaide (Fall), and two pass-by visits in Hsinchu, Taiwan. Moreover, PIs intend to seek external grants based on the initial discussion, reinforcing institutions strategic research partnerships.