J.-F. Chamberland conducts research in probability theory, statistical methods, and in their applications to control and communication systems. His current research focuses on statistical problems in the context of wireless communications, sensor networks, and genomic signal processing. He is also interested in the topics of control with communication constraints and optimization with possible applications to networks, biological systems, and economics. Furthermore, he seeks to develop techniques and paradigms that permit the analysis and the design of complex systems. Some of the areas that he and his collaborators work on are detailed below.

Massive Uncoordinated and Sporadic Multiple Access – Strengthening Connections between Coding and Random Access

The wireless landscape is poised to change, once again, within the next few years due to the emergence of machine-driven communications. This creates new challenges for wireless traffic, with packets originating from sporadic transmissions rather than sustained connections. Currently deployed scheduling policies are ill-equipped to deal with such traffic because they rely on gathering information about channel quality and queue length for every active device. The goal of this research initiative is to address this deficiency and devise novel access schemes tailored to massive uncoordinated and sporadic multiple access, thereby readying wireless infrastructures for the traffic of tomorrow.

The intellectual merit of this research initiative lies in exploiting the close connections between message-passing decoding and serial interference cancellation to create new access strategies. Linking advances in iterative methods to uncoordinated random access embodies the type of crosscutting research that can lead to disruptive technologies and paradigm shifts. This project embraces the evolving perspective of harnessing interference in wireless networks rather than fighting it or avoiding it. This viewpoint underlies many recent successes in network coding and distributed storage. This project brings forth such a perspective in the design of large-scale wireless networks. The broader impacts of this research program include providing pragmatic solutions to some of the challenges posed by an evolving wireless landscape, strengthening wireless infrastructures, and contributing to the training of a globally competitive Science, Technology, Engineering and Math workforce. The research tasks are attuned to societal needs in information technologies, an important economic driver for our nation. The wide dissemination of our findings will enhance the scientific understanding of wireless systems, access strategies, and iterative methods.

Adapting to a Changing Digital Landscape with Reconfigurable Antennas

This research initiative investigates new and realistic approaches to the creation and management of spatiotemporal information channels linking users and devices in wireless communication networks. Fast reconfigurable antennas can be employed to establish ancillary virtual links between nearby devices and hence augment the dimensionality of the solution space for several communication scenarios. This enables new ways to harness fading and manage interference in wireless environments. Understanding reconfigurable antennas, fading, interference and their interplay in the context of wireless communication networks forms the essence of this initiative. A central goal of the envisioned research is to circumvent the current bottlenecks that restrict Internet connectivity over access points and multi-cell networks. Prevalent obstructions that impede the development of superior systems include the complexity associated with interference management, limitations on feedback imposed by bandwidth and delay, and the fading characteristics of wireless environments. This research program introduces innovative technologies and algorithmic paradigms to address these fundamental issues. The proposed thrusts rely on a hierarchy of interlocking concepts; the three major tasks and their sub-tasks are summarized below.

  1. Design reconfigurable antennas and integrate them into mobile platforms.
  2. Provide a rigorous and thorough analysis on the repercussions of reconfigurable antennas on the foundations of wireless communication.
  3. Coordinate an integrative effort that blends the findings from tasks into a working prototype system.

Wireless Communications for Delay-Sensitive Traffic

This research initiative addresses current issues in wireless and hybrid data networks. Wireless technology offers a unique mixture of connectivity, flexibility, and freedom. It plays an instrumental role in bridging the gap between mobile devices and established communication infrastructures. Today, wireless technology is being embraced with increasing vigor. This trend is reflected in the growing interest for multihop wireless networks. Wireless systems have the potential to fulfill the long-standing promise of pervasive computing and ubiquitous network access. Recent breakthroughs in multi-antenna systems, user cooperation, active relaying and network coding provide a foundation to realize the next radical advance in information technology: building reliable wireless multihop networks that can support delay-sensitive applications (e.g., VoIP, video conferencing, remote control, monitoring, gaming). Stringent delay constraints typical of real-time traffic suggest that a classical capacity/throughput analysis of the communication infrastructure associated with a multihop wireless network may not offer an accurate assessment of overall performance. In particular, existing models for wireless networks are limited in their ability to characterize time-variation issues in both single-hop and multihop channels. This research project seeks to improve the robustness and reliability of wireless multihop systems and to enable them to support delay-sensitive applications. To achieve this goal, we envisage a number of broad objectives for which both models and methodology must be advanced.

  1. Develop an integrated methodology for the analysis of wireless systems that support real-time traffic and delay-sensitive applications such as voice, video conferencing, inference and control.
  2. Use this methodology to identify fundamental performance limits and to design algorithms which allocate system resources efficiently when confronted by stringent service requirements.
  3. Create communication schemes for delay-sensitive traffic that take advantage of novel paradigms in wireless communications such as network coding, active relaying, user cooperation, multi-antenna systems, and multipath routing.
  4. Develop a comprehensive evaluation and validation platform for system design and algorithm development in the context of small wireless multihop networks.

Detection and Estimation Theory with Applications to Sensor Networks

The emergence of miniature sensors with low-power wireless transceivers holds the promise of a new phase in the wireless revolution. Wireless sensor networks possess the ability to collect and transmit environmental data through the deployment of inexpensive devices. The amount of information generated by systems composed of hundreds or thousands of wireless sensors is vast. This creates many new challenges for the processing and transmission of the gathered data. New analysis tools are required to provide insight into the efficient design of sensor networks, especially in the context of inference problems and other delay-sensitive applications. The goal of this research initiative is to capture the preponderant features of sensor networks, and to use these features to derive guidelines and heuristics for the design of such systems.

Detection is a prime application of wireless sensor networks, and often serves as the initial goal of the system. For instance, the presence of an object has to be ascertained before a sensor network can estimate its attributes. The ability of a sensor network to make accurate decisions is governed by the amount of information available at the decision center. As such, we have been studying resource allocation and its impact on system performance for decentralized detection in wireless sensor networks. We have shown that under certain conditions having identical sensor nodes, each node sending one bit of information, is optimal. Furthermore, we have proposed a collection of performance metrics that can be employed to compare prospective sensor nodes in large networks. There are many directions in which future work can proceed.

  1. Modulation and error control coding have been employed successfully at the physical layer to shield information from interference and ambient noise. This is very encouraging for sensor networks as error control codes could permit the reliable transmission of information from the nodes. The relation between coding, performance, and power consumption in wireless sensor networks has not been explored in the literature. In general, a string of observations is required to be able to apply standard error correcting techniques. Yet successive observations corresponding to a same phenomenon can be fused together locally to reduce the data rate at the output of a sensor node. Comparing the performance of these two alternatives for the transmission of data over sensor networks is of interest.
  2. While the assumption that observations at the sensors are conditionally independent is common and convenient for analysis, it does not necessarily hold for arbitrary sensing systems. For instance, whenever sensor nodes lie in close proximity of one another, their observations are likely to be conditionally dependent. An interesting problem then is to derive rules for the optimal placement of sensors in correlated stochastic fields and to assess overall system performance as a function of node density and quantizer accuracy. Wireless nodes typically operate under stringent resource constraints. Energy efficient designs translate into more information for inference and, potentially, prolonged lifetimes for the corresponding systems. To minimize energy consumption in a wireless sensor network, sensor nodes are envisioned to have the ability to transmit information, process data, or sleep depending on the level of interest in the next few observations. We have obtained preliminary results that suggest that sensor nodes can use a priori knowledge about the process they are monitoring together with their current and past observations to save energy. This leads to several avenues of future research.
  3. The sleeping strategy whereby sensor nodes go to sleep when the event of interest becomes improbable, but stay awake otherwise, has shown potential for substantial energy savings. However, the optimal design of multi-sensor sleeping policy is difficult, due to a vast search space. A more complete study of sleeping policies using approximation tools such as neuro-dynamic programming is needed.
  4. For situations where sensor nodes do not have a priori knowledge about the stochastic process they wish to observe, adaptive sleeping policies can be considered. In an adaptive policy, sensor nodes use empirical measurements to estimate the statistics of the observed process. Once these statistics are known with sufficient accuracy, sensor nodes can enter a sleeping pattern where nodes are powered off whenever the next few observations are unlikely to contain much information. The design and performance analysis of adaptive sleeping policies are of interest.
  5. One of the challenges in the creation of energy-aware self-configuring wireless sensor networks is to develop efficient communication protocols. Wireless channels are characterized by a rapid decay in signal strength. The sublinear relationship between received power and distance implies that using a series of short hops in a wireless network typically requires less energy than using one large hop between the source of a data set and its destination. It is then important to characterize the interplay between in-network signal processing and traffic burstiness in event-driven applications. Sensor nodes that monitor rare events are likely to collect and transmit observations concurrently, thereby creating waves of traffic in the network. This bursty behavior can be alleviated by merging and processing information at critical points in the network.

Genomic Signal Processing and Efficient Intervention Strategies

The mechanisms governing the evolution of biological cells are extremely complicated. The study of these subtle interactions at the biochemical level has been the subject of ongoing scientific efforts for several decades. The great complexity of most biological systems suggests that many more decades of work may be required before we gain a precise understanding of the chemical interactions present in these systems. While fundamental research that strives to better our understanding of the cell and its operation is very commendable, an equally important endeavor is to develop models and intervention strategies to help cure diseases caused by malfunctioning cell-cycles.

Cancer and related degenerative diseases are system impairments where the normal operation of a cell, with its multiple feedback and regulation mechanisms, fails. A key aspect to cancer treatment is the realization that we need not catalog every possible biochemical interactions in a system to successfully control its behavior. Engineering has had important successes where system operation is efficiently regulated despite the fact that only limited information is available about the subcomponents of the controlled entity. The capable approach of stochastic modeling that underlies many such successes in system engineering promises to provide a framework for the design and implementation of effective gene therapies. To this end, novel technology is currently being developed to track the time-evolution of individual cells. The corresponding experiments produce time-course data on the progression of individual cells by sampling sequentially the phenotype of these organisms. In particular, the recent development of cDNA microarray technology that tracks gene-expression in a single cell through time will facilitate the application of control theory to cancer treatment. Along with this emerging technology, new analysis tools are required to give insight into the design of efficient intervention strategies.

The perspective adopted in this project is to view the cell as a biological system that can be monitored and regulated over time. To fully take advantage of a system theoretic approach in the design of efficient therapeutic strategies, two aspects of this interdisciplinary problem need to be addressed: (1) the development and validation of accurate stochastic models for the evolution of the cell, and (2) the design and evaluation of efficient control strategies applied to living organisms. It is these two issues, system identification and the design of efficient intervention strategies, that we seek to address in this project. A straightforward use of existing computation concepts, models, and methods may not work for the problems at hand. For cancer treatment, the a priori identification of good gene candidates, relevant pathways, and prospective drugs are examples of where the integration of biological knowledge with engineering insight can lead to innovations in computational thinking. Without a genuine collaboration between biochemists and engineers, this project would not be possible. This research initiative is joint work with the Genomic Signal Processing Laboratory at Texas A&M University and the Translational Genomics Research Institute (TGen).

Education and Technology

Teaching provides an exceptional opportunity to share knowledge and contribute to the scholarship of students. As a teacher, J.-F. Chamberland seeks to achieve three main interrelated pedagogical objectives: provide the students with a base of concepts and engineering skills, foster their interest in applied and theoretical research, and promote innovative and critical thinking. In college, a large portion of student learning occurs outside classrooms. One of the roles of an instructor is to facilitate and support this learning process. Understanding how emerging technologies such as content management systems, wikis, discussion boards, streaming contents and licensing influence learning and cooperation among students is an important research topic that will shape the future of education. This research area is an integrant part of our education program.