Thomas E. Fuja - US grants

EIA-9977387
Costello, Daniel J.
Collins, Oliver
University of Notre Dame

MRI: Acquisition of Instrumentation for an Experimental Radio Facility in Support of Wireless Digital Communications Research

A team of researchers proposes to construct a facility to provide two-way digital communications capabilities which will consists of two base stations, a number of mobile units and all test equipment necessary to effect narrowband transmission over a geographical area of several square miles. This facility will provide a resource that is rare in academia - an actual operational testbed for research into the algorithms and devices that make wireless digital communications possible. Some of the issues that will be investigated with this facility include: (i) the effectiveness of various error control schemes in a wireless environment, (ii) the design and testing of novel communication devices, circuits and architectures based on compound semiconductor technology, (iii) the ''synergy'' arising from use of spatial diversity in conjunction with coded modulation and interference mitigation, and (iv) experimental testing of proposed new techniques for anticipating the ''deep fades'' that afflict mobile communications.

This project focuses on the physical layer of digital communication
system design - in particular on the design of graph-based error
control coding techniques for noisy, fading channels, such as those
encountered in wireless communication systems. The research
addresses those issues that have made practical implementation of
graph-based coding techniques problematic. Those issues include:

The development of LDPC and turbo codes with algebraic structure,
suitable for high speed implementation.
The development of LDPC and turbo codes with moderate block length,
suitable for practical real-time applications.
The design of bandwidth efficient coded modulation schemes
incorporating these new code constructions in order to "fatten the
bit pipe" in high data rate applications.
The adaptation of these new coding schemes for use in the fading
environment that characterizes wireless channels.

This award provides funding for a three-year Combined Research-Curriculum Development (CRCD) program, entitled "Real-World Wireless Communications in the Graduate and Undergraduate Curriculum," at the University of Notre Dame, under the direction of Dr. Thomas E. Fuja. The overall objective of this project is to bring elements of the Notre Dame digital communications research and the capabilities created by the wireless facility, the WAND Lab, into the undergraduate and graduate electrical engineering curriculum at Notre Dame.

Multihop transmission is increasingly being incorporated into modern wireless communication networks. These networks are central to our nation's future communications and monitoring infrastructures. The
basic motivation for multihop is that transmissions occur over shorter distances -- and therefore with higher received signal strength -- via many intermediate nodes rather than over longer distances -- and
therefore with lower received signal strength -- between the source and destination of the information. However, multihop transmission involves complex interactions among channel coding at the physical
layer, distributed channel access at the link layer, and multihop routing at the network layer. These techniques have been studied largely in isolation by different communities, whereas this project
focuses on their interaction, especially in delay-constrained scenarios.

This research involves models for general wireless multihop networks, and develops tradeoffs for transmission along an individual routes of up to M + 1 nodes. Transmission between the end nodes can occur in a single hop, or up to M hops. Multihop transmission increases the received signal-to-noise ratio (SNR) at intermediate nodes; however, this observation does not take into account the important practical issues of power and bandwidth allocation, end-to-end delay, error propagation, or interference induced by other transmitters. Among other results, preliminary research indicates that the benefits of multihop are eroded by these issues, especially for high spectral efficiency, i.e., high data rates relative to the available bandwidth. The investigators take a comprehensive look at multihop transmission from the point of view of communication theory, mathematical networking, and networking practice, with the goal of offering solutions that will impact a major part of our society.

Collaborative Research:
New Directions in Graph-Based Code Design

Abstract

This collaborative research focuses on the physical layer of digital communication system design ? in particular on the analysis, design, and implementation of capacity-approaching low-density parity-check (LDPC) codes for practical communication environments. In the last ten years, the area of channel coding has undergone a revolutionary change with the growing popularity of graph-based codes and iterative decoding algorithms. These coding methods, which include both turbo codes and LDPC codes, approach the limits of channel coding performance promised by Shannon in his landmark 1948 paper. Currently, these codes are in the process of replacing conventional error control techniques in numerous digital communication and storage standards, including, among others, deep-space communication, next-generation wireless transmission, last-mile cable transmission, digital video broadcasting, and high-density digital magnetic recording.

The research addresses several issues related to graph-based codes. In particular, it focuses on the analysis, design, and implementation of LDPC convolutional codes, which have several advantages compared to LDPC block codes, but have not received much attention from the research community. Conventional convolutional codes, on the other hand, have had a transformative effect in numerous practical communication environments, and the same is likely to be true in the capacity-approaching world of LDPC codes. The project emphasizes bridging the gap between advanced theoretical research and realistic practical implementations. In particular, it is concerned with adapting LDPC convolutional code designs to various industry standards that require flexibility in both frame length and code rate and with developing VLSI implementations of hardware decoders that can be tested under real operating conditions.

Engineering - Electrical (55)

The project is creating a new approach for teaching systems engineering within the electrical engineering (EE) curriculum. The approach makes use of a hardware/software platform and accompanying curricular innovations to bring a more engaged, hands-on, exploratory focus to what is probably the most convention-bound component of a typical undergraduate electrical engineering education. Unlike previous attempts to create a less abstract and more engaging systems engineering curriculum, which focused almost entirely on software tools (e.g., MATLAB), the proposed project is creating a hardware/software platform and using that platform to vertically integrate key systems concepts across the undergraduate curriculum. The platform is letting students explore systems applications using a variety of hardware devices such as signal generators, filters, A/D and D/A converters, sensors, and a microprocessor. The platform contains an interface to a student's MP-3 player and cell phone to bring home the relevance of the systems perspective to modern technology. To allow students to see the signals they are generating and manipulating, the platform includes a suite of visualization software. The project is being evaluated using an assortment of tools including an established signals and systems concept inventory along with the analysis of student products and enrollments numbers. The investigators are disseminating their results by posting their material on a website, by publication and presentation in engineering education venues, and through the investigators connection with the signals and systems concept inventory community. Broader impacts include the dissemination of the material, the focus on Hispanic students, and the outreach effort to high school students.

This research investigates a new approach to protecting the reliability of digital communication and digital storage systems. This approach takes advantage of recent work (by the research team and others) that formulates the "encoding" and "decoding" of data in terms of a novel graphical representation; this formulation has several advantages over existing techniques for insuring data integrity, including better performance at very low power and the absence of an 'error floor', i.e., the ability to consistently (and significantly) lower the decoded error probability with incremental expenditures of power. The ultimate goal of the research is more reliable delivery of digital data, text, computer files, speech and audio signals, video, etc. - using devices that require less power (and thus have longer battery life) and shorter processing delay.

More specifically, the research investigates the use of spatially coupled sparse codes - channel (error control) codes with a sparse parity check representation formed by coupling together a chain of small "protographs". This approach, which was pioneered by the research team in the context of terminated low-density parity check convolutional codes, has recently been shown to possess a unique combination of properties - iterative decoding performance that approaches channel capacity and minimum distance that grows linearly with block length - as the code size gets large. The research follows four tracks: (1) the design and analysis of low latency/memory decoding strategies; (2) decoded error probability performance guarantees; (3) the development and analysis of spatially coupled sparse codes with algebraic structure; and (4) the application of spatial coupling outside the immediate domain of channel coding, including cooperative diversity, compressed sensing, and multi-terminal source/channel coding.

This project aims to transform small and medium-sized manufacturing enterprises (SMEs) in the U.S. by analyzing how the adoption of artificial intelligence (AI)-based technologies impacts manufacturing SMEs’ labor force and productivity. AI-based technologies pose a number of concerns around retraining and replacement of workers, but also potential benefits around productivity and the creation of new jobs, including ones that are less physically demanding and may support workers currently excluded from manufacturing jobs. The project focuses on SMEs rather than larger firms both because SMEs employ a large majority of U.S. manufacturing workers and because SMEs may pose unique challenges for the adoption of AI-based technologies in terms of the resources and skills these firms have available to make these technologies work for them. The goals of the project are to: (1) understand the challenges that manufacturing SMEs and their workers currently face in adopting and implementing new AI technologies, restructuring work and tasks and learning new skills; (2) design a controlled manufacturing environment to support studies of workers collaborating with AI-based technologies; (3) develop a framework that SMEs can use when adopting these technologies; and, (4) develop connections with industry partners and community colleges to identify ways to lower the barriers to the adoption of new AI technologies on the factory floor and to develop a robust workforce training program. <br/><br/>To accomplish these goals, the project team will build a collaboration with manufacturing firms, both SMEs and large corporations holding multiple SMEs, in Indiana’s South Bend-Elkhart region. A team of experts in the areas of economics, engineering, AI, information technology, analysis and operations, and sociology will work with these local SMEs and conduct on-site observations and in-depth interviews to understand companies’ current technology use and needs, as well as opportunities for AI-based technologies to meet those needs. These findings will inform a survey to collect data on a wider range of companies’ financial situations and production capacity, technological sophistication, management and hiring practices, workforce composition and turnover, and work conditions. Together this information will be used to identify promising candidate AI-based technologies to explore further, then design a manufacturing cell that facilitates controlled studies of workers collaborating with these technologies. Further, the project team will develop novel approaches to training manufacturing SME employees to allow them to be more directly involved in the adoption and use of these AI-based technologies. This curriculum development work will be done in collaboration with Ivy Tech, which has 40 community college locations in Indiana and is developing a School of Advanced Manufacturing, Engineering and Applied Science in response to the needs of the state’s manufacturing industry. Based on these activities, the project will develop a framework called “Participatory AI Adoption and Implementation” with guidelines for how workplaces, workers, and AI-based technologies can productively interact in manufacturing SMEs, focusing on ways to involve workers in the firm’s decision process when adopting new technologies from design to deployment.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.