Choon Y. Tang, Ph.D. - US grants

The objective of this research is to develop a new technique for finite bit representation to enable high-performance signal processing for remote sensing systems. The technique for finite bit representation is based on multidimensional continued fractions coupled with an architectural design strategy that does not use multipliers. Multidimensional continued fractions have been known within the mathematical community for some time, but have largely been unexplored by the engineering community until recently. Simultaneous rational representations are a member of the multidimensional fraction family and are employed to create fixed integer transforms with computationally optimal representations.

This research seeks to provide an approach that can decrease the size, weight, and cost of sensor systems and increase their spatial and temporal resolution. Various hardware platforms are to be explored during the project. Of these, field programmable gate arrays and digital signal processors are likely the most compelling for fast, low-power devices.

The principal investigator is currently serving as the faculty advisor for the American Indians in Science and Engineering Society chapter at the University of Oklahoma and the co-principal investigator is currently serving as a mentor for two high-school robotics teams near Oklahoma City. In addition, the students from the Society of Women Engineers, and the 436 students in the university?s Multicultural Engineering Program are encouraged to participate in this project. Remote sensing systems include satellite, light detection and ranging (LIDAR), infra-red, and radar. As such, this project helps students and researchers learn more about the environment in which they live. Experiments utilize the National Weather Radar Testbed that is located in Oklahoma.

The objective of this research is to address a pressing need for techniques that control the power outputs of large-scale, grid-interconnected wind farms, so that wind energy may be utilized efficiently, reliably, and economically. The approach is based on a blend of advanced theories in nonlinear control, energy conversion, and power system reliability, which involves theoretical research and algorithm development, including modeling of wind farms for power system operation, seamless control of such farms so that they can operate in both the maximum power tracking and power regulation modes, and analysis of their dynamic power output characteristics.

This project bridges the gap between advanced control theory and power system engineering practice, by providing novel control technologies for enabling integration of large-scale uncertain wind generation into the power system. The research requires state-of-the-art modeling of electric machines, wind turbines, and power system reliability. In addition, the research supports nationwide development in wind energy and addresses a set of theoretical challenges in advanced nonlinear control.

This project promotes national policy on renewable energy, by providing solutions to several defining issues in reliable and economic utilization of wind resources. It also promotes integration of interdisciplinary research with education at state universities. The integration will be realized through incorporation of new topics in traditional energy conversion and power systems courses, field trips to actual wind farms, graduate research mentorship, design of a new graduate course, and involvement of underrepresented groups. The principal investigator is also currently a mentor at two high-school robotics teams.

The objective of this research award is to develop efficient, robust, and scalable distributed algorithms, which allow agents in a mobile ad hoc or sensor network to collaboratively and asynchronously solve convex and separable constrained optimization problems over the network. The approach is based on a blend of tools and ideas from control theory, optimization theory, and wireless networking, bringing together notions of feedback iteration control, Lyapunov stability concepts, networked dynamical systems, Lagrange multiplier methods, and protocol design techniques. The problems to be addressed include general convex program and its two important special cases, linear and quadratic programs. The models to be studied include models with static and dynamic problem data, agent memberships, and network topologies. The deliverables include a collection of distributed algorithms, theoretical analysis of their behaviors, computer simulation of their performances, documentation of the research outcomes, and integration of the research with high school, undergraduate, and graduate education.

If successful, the results of this research will enhance the capability and functionality of mobile ad hoc and wireless sensor networks, enabling agents to cooperatively accomplish sophisticated tasks that require fusion of information, decentralized decision making, and coordination of actions. These will, in turn, broaden the scientific and engineering applications of such networks. The results will also make contributions to control theory by creating a new paradigm in control of multi-agent systems, namely, Lyapunov-based feedback control of when to communicate and compute, so that agents can achieve optimized global behavior quickly through minimal local interactions. Integration of the research with education will benefit high school students via outreach activities and robotics competition mentorship, undergraduate students via classroom instruction and an NSF Research Experiences for Undergraduates grant, and graduate students via research supervision and offering of a new graduate course.

A key requirement from emerging next-generation (5G) cellular networks is that they must be rapidly adaptable to a broad range of use cases. This problem calls for a big shift in the way cellular radio access network architectures are designed and operated. One way forward is to explore novel radio access architectures such as Control and Data Plane Split Architecture (CDSA), and equip them with next generation Pro-active Self-Organizing Network functions (P-SON). Under this approach, small cells may operate on millimeter wave bands and provide high data rates to users on demand. Overlaid macro-cells that operate on sub-6 Gigahertz (GHz) frequency bands provide control functions to support these users while providing lower data rates. To translate this approach into pragmatic architectures and solutions, there is a need for a purpose-built testbed, as most existing testbeds don't have the flexibility and end-to-end programmability to investigate CDSA and P-SON. This project will develop TurboRAN: Testbed for Ultra-Dense-Multi-Band Control and Data Plane Split Radio Access Networks of the Future that aims to meet these needs.

Impacts of TurboRAN include: 1) Enabling experimental research on P-SON, CDSA, and its variants, among various other system-level aspects of 5G wireless systems and beyond, with reproducible results; 2) Validating theoretical- and simulation-based systems research conducted by the wireless community; 3) Providing open access to cellular system control and user planes in a multi-tier cellular network; 4) Training a generation of wireless engineers leveraging innovative and experimental teaching; 5) Testing novel approaches and protocols for developing prototypes and shortening the research cycle towards rapid commercialization trajectory; 6) Developing joint academic-industry programs for conducting research aimed at rapid production uptake and promoting standards and thus acting as an innovation gateway. The proposed project is led by the team at The University of Oklahoma in conjunction with a number of domestic and international academic and industry partners interested in facilitating experimental investigation on future cellular systems.

TurboRAN will comprise three layers of cells, supporting operation in sub-6GHz bands as well as in millimeter wave bands, with conventional heterogeneous as well as C-RAN implementation. It will support MIMO, mobile access points and diversity of user devices. In addition to end-to-end programmability and flexibility for modelling a variety of futuristic heterogeneous cellular system scenarios, another unique feature of the TurboRAN project will be the integrated big data processing capability to explore the potential of untapped cellular control and user plane data for designing a proactive next-generation self-organizing network.