Research Profiles

This collaborative proposal between the University of Texas at Dallas, Qatar University, and Rutgers University aims to address the challenge of providing high-performance, low-cost broadband access anytime, anywhere. The proposal aims to mitigate interference, a major performance-limiting impairment in wireline and wireless systems, by proposing a new low-complexity interference mitigation framework with performance guarantees. The proposal is transformative in nature as it connects the future of broadband communications to the mathematics of compressive sensing theory, and it also makes fundamentally novel contributions to the field of CS by connecting the mathematics of CS theory to the mathematics of finite frame theory. Successful outcomes of this proposal will aid in bringing the broadband revolution to its logical conclusion, generate valuable intellectual property for Qatar, help train Qatari students to become leaders in technological innovations of the future, and have broader impacts on oil and gas exploration projects in and around Qatar.

The project addressed research issues related to power conversion, control, and communications in an islanded micro-grid distributed generation system, with multiple electric vehicles (EVs) connected to the grid. The research focused on integrating smart electric chargers with the micro-grid controller while accounting for the variable nature of the renewable energy sources. The interactions between the Electric Vehicle Supply Equipment and the micro-grid were studied in a real-time simulation environment, and various battery-charging power conversion systems were analyzed for efficiency and performance. The project also explored the use of silicon carbide super junction transistors for power-stabilizing converters and demo EV charging converters, as well as the improvement of communication reliability in vehicle-to-grid (V2G) systems.

Data centers are critical infrastructure that provide cloud computing services to users and organizations around the world. The design of data center networks that interconnects massive numbers of servers and provides efficient and fault-tolerant routing services to upper-layer applications is a fundamental challenge for both academia and industry. A new type of data center interconnect, LacoNet, is proposed to combine the advantages of previous architectures while avoiding their limitations. The project aims to develop LacoNet, investigate its architectural and topological properties, design routing algorithms, develop protocols and active queue management schemes to provide QoS, and establish a benchmarking testbed to evaluate and compare various data center architectures under realistic and practical environments. The proposal reviews existing work related to the project and is divided into work done by others and work done by the Principal Investigator (PI). The proposed LacoNet architecture aims to solve several issues, including scalability, performance, cost, and QoS.

The world's growing population has triggered a healthcare revolution, leading to a shift towards eHealth solutions. The use of interconnected digital devices in healthcare, enabled by the "Internet of Things" (IoT), offers vast opportunities for preventive and continuing care, while reducing overcrowding in hospitals. However, this also creates new security threats, making it essential to implement appropriate security measures to protect patient data. This project aims to investigate the security of eHealth/mHealth systems and propose tailored solutions that strike a balance between security and usability, considering the limitations of IoT devices, including power consumption and processing power, and their reliance on backscatter transmission. The proposed solutions will focus on protecting patient information during wireless transmission from wearable sensors to local controllers, and from controllers to healthcare systems' servers, as well as during emergency response teams' movement.

This research proposal aims to develop a Secure Federated Edge Intelligence Framework to optimize the experience of Federated Edge Learning (FEEL) under network, data, and spectrum constraints while enhancing security and privacy. FEEL allows machine learning models to be trained by aggregating local learning models at the edge servers instead of users' raw data to preserve users' privacy. The proposed framework consists of three sub-objectives, developing reliable and resource-efficient edge intelligence algorithms, efficiently managing RF spectrum resources, and securing the FEEL and enhancing users' privacy. The developed FEEL algorithms will be tested against real-world datasets, and a software-defined radio-based prototype will be implemented for smart spectrum management.

The rise of chronic diseases has put pressure on healthcare systems to provide patient-centered services. In Qatar, non-communicable chronic diseases are responsible for most mortalities, with cardiovascular disorders being the leading cause. The proposal aims to address this problem by introducing non-intrusive smart IoT sensors to monitor cardiac conditions and detect irregularities early through an AI model. The proposed approach will allow for continuous home-monitoring of ECG signals and timely interventions by physicians, while preserving patient privacy. The outcomes of the project are expected to deliver affordable and privacy-preserving solutions supporting the delivery of early warning on cardiovascular anomalies.

During this research project, the focus was on studying incremental relaying technique for single carrier transmission and extending it to broadband channels. The project also investigated the effect of I/Q imbalance on the outage probability of relaying networks. Outage probability and symbol error rate were derived analytically and verified by simulations. The findings of this study contribute to the development of more efficient and reliable relaying systems, which could have implications for future telecommunications technologies.