Andrea Goldsmith - US grants

NCR-9501452 Goldsmith, Andrea The broad goal of this research is to address fundamental issues in the design, analysis, and implementation of wireless networks. Issues treated will cross traditional boundaries between signal processing, communications, and networking. The work will first develop a networking, architecture and protocol suite to support the internetworking of wireless subnetworks. A hierarchical network infrastructure will be considered that exploits the broadcast capability and power control of radio channels. Optimum location of multimode gateways and hierarchical databases to aid in mobility management and routing within this infrastructure will be determined. Optimization techniques for efficient allocation of spectrum will be studied using techniques from dynamic programming. Formal methods for obtaining and analyzing channel policies will be developed and applied to analysis and design issues. Joint source/channel coding techniques to minimize end to end distortion will be examined. Emerging wireless applications will be considered and a global networking infrastructure to support these applications will be sought. The education component will develop innovative and dynamic course programs at the undergraduate and graduate level in wireless communications, networks, and dynamic programming. These activities will encourage industrial collaborations.

This is a collaborative research project between UC Berkeley and Stanford University to develop a validated analytical methodology for designing control systems where some of the feedback loops are closed over wireless communication links. The proposed work is an interdisciplinary effort between two very disparate research areas: wireless communications and control theory. This work will have a great impact on many fields where wireless communication is beginning to see an increasing presence such as transportation, manufacturing and remote sensing applications.

Most current controller designs assume that communication between the sensors and actuators and the central logic system is done over hard-wired lines providing essentially perfect transmission. Thus, the controller design typically assumes no rate constraints, delay, or loss of information. However, there are many applications where this communication must take place over imperfect wireless links where these imperfections can lead to loss of performance and even instability. There currently exists no unified theory for the design of closed loop control systems using wireless links. This proposal is aimed at developing such a theory.

The first stage of the proposed work will focus on characterizing the communications network carrying the sensor, actuator, and control information in terms of its rate constraints, and delay and packet loss statistics. The next stage will investigate joint optimization of the controller and communication system.

The research results will be presented at both communications and controls conferences. The final results will be incorporated into advanced graduate courses. A very important aspect of this project is the involvement of undergraduate students in the research. Both UC Berkeley and Stanford have active UROP (Undergraduate Research Opportunities Programs). The area of communication/control is very attractive to undergraduates and it is anticipated that undergraduates will participate in the project due to its interdisciplinary nature and its potential to impact real world problems.

ABSTRACT
0325639
Goldsmith, Andrea
Stanford University

An ad-hoc wireless network is a collection of possibly mobile wireless nodes that self-configure to form a
network without the aid of any established infrastructure. The lack of infrastructure is extremely compelling
for applications where a communications infrastructure is too expensive to deploy, cannot be deployed
quickly, or is simply not feasible. Such applications range from multi-hop wireless broadband Internet
access to sensor networks to building or highway automation to voice, image, and video communication for
emergency response, a life-saving capability urgently needed by our society. The nature of ad-hoc wireless
networks makes support of delay-critical applications such as voice and video quite challenging. In this
project the investigators study a new framework for ad-hoc wireless network design to support such delay-
critical applications. The framework includes dynamic allocation of network resources to support media
requirements, protocols that are robust to variations and uncertainties in the network, and adaptive media
compression techniques that preserve end-to-end network connectivity even when network conditions are
poor. The results of the project are expected to have a significant impact on the development of future
wireless networks and the enabling of critical applications.
The framework being developed to support delay-critical applications in ad-hoc wireless networks is based
on cross-layer design across the network protocol stack. The design incorporates adaptation across multiple
protocol layers, including the application, transport, network, and link layers. Within this framework, the
investigators develop a suite of new techniques to balance network congestion and distortion of real-time
media streams by jointly optimizing error-resilient source coding, packet scheduling, stream-based routing, link capacity assignment, and adaptive link layer techniques. The optimization is carried out dynamically by all nodes, based on link state communication, to continuously adapt to changing link and trac conditions.
The research project systematically investigates these new ideas and ultimately demonstrates them in a small
testbed.

Center Name: Center for Science of Information
Center Director: W. Szpankowski,
Lead Institution: Purdue University

The foundations of modern communications and ancillary trillion plus dollar economic windfall were laid in 1948 by Claude Shannon who introduced a general mathematical theory of the inherent information content in data and its reliable communication in the presence of noise. While Shannon?s Theory has had a profound impact, its application beyond storage and point-to-point communication, e.g., to the Internet, poses fundamental challenges, among the most vexing facing today?s scientists and engineers. The overarching vision of the proposed Center for Science of Information is to develop a new science of information that incorporates common features generally associated with data/information, such as space, time, structure, semantics and context that are not addressed by Shannon?s Theory. The realization of this vision requires a center-level environment that can focus the efforts of a sizeable (and diverse) group of researchers, for a protracted period of time, on these critically important challenges, which could have far reaching societal impact and enormous economic ramifications. Under the umbrella of this overarching vision, the proposed center will explore the following fundamental issues: (i) modeling complex systems and development of analytical techniques for information flow (e.g., understanding Darwinian selection); (ii) quantification and extraction of informative substructures in complex systems (e.g., discovering functionally relevant structures in gene regulatory networks or modular entities in social networks); (iii) understanding of spatio-temporal coding used to exchange information through timing and localization in complex systems (e.g., building more efficient ad hoc networks and understanding neuronal activity); (iv) data-driven knowledge discovery based on formal information-theoretic measures (e.g., finding semantically relevant information in unstructured repositories); (v) steganography, data obfuscation and hiding as mechanisms for robustness (e.g., developing secure systems for monitoring and surveillance); and (vi) discovering principles of redundancy and fault tolerance in diverse natural systems (e.g., understanding the interplay between erasure coding and distributed system design).

The intellectual merits of the proposed center include the community of students and academic and industrial scholars it seeks to sustain, the theoretical advances it hopes to achieve, and the novel insights and tools it hopes to provide to explicate a myriad of diverse systems, ranging from the life sciences through business applications. The broader impacts of this Center extend beyond the potential scientific, societal and economic ramifications and include the creation of an ?active and thriving community of students and scholars? who will train the next generation of scientists and engineers, enlighten the public, and ultimately pave the way for the next information revolution. The Center team is composed of over 40 investigators, many having already made significant accomplishments in multiple research areas relevant to the Science of Information. The Center team is a very diverse group: it has a mix of junior and senior researchers, including several members of underrepresented groups. They bring expertise in all essential areas of research, including Computer Science, Chemistry, Economics, Statistics, Environmental Science, Information Theory, Life Sciences, and Physics. The institutional partners include nine premier institutions (Purdue, Bryn Mawr, Howard, MIT, Princeton, Stanford, UC Berkeley, UCSD, and UIUC), two of which have significant underrepresented student populations. The academic institutions are complemented by the Center?s industrial partners (Amgen, Bell Labs, Configuersoft, Google, HP, Lilly, NEC, Qualcomm, and Yahoo) and by world-renowned researchers at international institutions.

Current radio receiver designs are pushing the boundaries of Analog-to-Digital conversion and digital signal processing technology in terms of speed and energy efficiency. These technology limitations present a major bottleneck in transferring promising wideband and energy-efficient receiver design paradigms from theory to practice. This project investigates whether these bottlenecks can be circumvented by designing digital communication system receivers that are sampled at sub-Nyquist rates. By sampling below the Nyquist rate, current technology can be used for very wideband communication systems and energy consumption can be significantly reduced below that required for Nyquist-rate sampling. The proposed research brings together the areas of Shannon theory and sampling theory by exploring the fundamental capacity limits of single-user and multi-user subsampled channels as well as the optimal sampling mechanisms that achieve these limits. In addition, the project will extend the ideas of sub-sampled communication to determine the rate-distortion trade-off of sub-sampled sources along with joint source-channel coding when both the source and channel are undersampled. The proposed activity will develop a broader understanding of communication system design subject to hardware constraints by exploring an important and unanswered question at the intersection of two important fields within electrical engineering: signal processing and information theory. The results of the proposed work can enable low-complexity high-performance radio designs for 60 GHz wideband communications and for cognitive radios. Furthermore, these results impact other engineering systems such as radar, optical systems, medical imaging and more, since the mathematical machinery and hardware insights developed in the proposed research can provide important insights into related areas in which reduced rate sampling and processing is needed.

The broader impacts resulting from the proposed activity will include significant enhancement of the communications capabilities beyond the current state of the art in wideband, cognitive, and energy-efficient radio design. Wideband radios are of key importance to meet the significant demand for multimedia wireless communications, especially video. Cognitive radios have the ability to more efficiently utilize the limited available radio spectrum. In addition, there is a great need to design communication systems that consume minimal energy, especially sensor networks, which have application to enable smart buildings, enhance homeland security, and improve the reliability and robustness of our power grid.

This project focuses on the problem of information acquisition, state estimation and control in the context of cyber physical systems. In our underlying model, a (set of) decision maker(s), by controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about stochastically time-varying parameters of interest. These parameters are then used to control the physical system efficiently and robustly. Here the cyber system collects, processes, and acquires information about the underlying physical system of interest, which is used for its control. The proposed work will develop a new theoretical framework for stochastic learning, decision-making, and control in stochastically-varying cyber physical systems.

In order to obtain analytical insights into the structure of efficient design, we first consider the case where the actions of the cyber system only affect the estimate of the underlying physical system. This class of problems arises in the context of (distributed) sensing/tracking of a physical system in isolation from cyber system control of the physical system's state. Joint state estimation and control for cyber-physical systems will then be considered. Here the most natural first step is to obtain sufficient conditions and/or special classes of systems where a separated approach to the information acquisition and efficient control is (near) optimal. To demonstrate its utility in practice, our theoretical framework will be applied in the specific context of energy efficient control of data centers and robust control of the smart grid under limited sensing.

The intellectual merit of this work will be to develop a theoretical framework for the design of cyber-physical systems including information acquisition, state estimation, and control. In addition, separation theorems for the optimality of separate state estimation and control will be explored.

In terms of broader impacts, significant performance improvement of control systems closed over communication networks will impact a wide range of applications for societal benefit, including smart buildings, intelligent transportation systems, energy-efficient data centers, and the future smart-grid. The PIs plan to disseminate the research results widely through conferences and journals, as well as by organizing specialized workshops and conference sessions related to cyber physical systems. The proposed project will train Ph.D. students as well as enrich the curriculum taught by the PIs in communications, stochastic control, and networks. The PIs have a strong track record in diversity and outreach activities, which for this project will include exposure and involvement of high school and undergraduate students, including under-represented minorities and women.

This research studies the use of power combining in multi-antenna radios. Modern radio systems often use multiple transmit antennas and appropriate signal coding to improve throughput and reliability of wireless links. Ideally, each amplifier should be rated at the maximum total (or sum) power that can be transmitted from all antennas. Due to cost and efficiency considerations, the power rating is usually designed to be lower (typically the maximum total power divided by the number of antennas), and this can lead to significant performance loss. This research explores a solution using Power Combining Networks (PCN) that enable the output of an amplifier to be switched/combined into any antenna, thus moving power from a weaker to a stronger antenna, resulting in improved link performance.

The maximum output power of a MIMO (Multiple Input, Multiple Output) antenna transmitter is limited due to bio-safety considerations. MIMO transmission performs best when each power amplifier is rated at the full system transmit power. If the amplifiers are fractionally rated due to cost and efficiency considerations, the worst case loss with M antennas can reach log M dB in the presence of channel imbalances. A PCN can reduce this loss significantly, trading the loss of array gain for a better channel coupling efficiency. Use of a PCN alters the effective MIMO channel and therefore can also change the optimum transmit coding. The goal of this research is to study fast algorithms for PCN selection, appropriate signal coding schemes and the best realizable performance.

Many of the sophisticated electronic devices ubiquitous today have been enabled by digital signal processing (DSP) that converts the analog signals associated with the physical world to the digital domain. Moore's law allows such digitized signals to be processed and communicated with small, low-cost energy-efficient digital hardware. Yet fundamental questions regarding the performance limits and tradeoffs associated with the sampling, quantization, communication, and reconstruction of analog data with respect to both fidelity and overall energy consumption remain open. Better understanding of these performance limits and tradeoffs will significant enhance capabilities for collection, processing, and communication of analog sensor data beyond the current state of the art. These capabilities are particularly acute for emerging sensor network applications in health and wellness, security, energy-efficient infrastructures, and smart cities, where many low-cost low-energy analog sensors will be collecting large amounts of data and transmitting it to remote locations for processing.

The proposed research will investigate the interplay between sampling, quantization, communication and reconstruction of analog signals under memory constraints, communication constraints, and energy constraints. The goal of the proposed work is to develop a fundamental rate-distortion theory for the sampling, quantization, and reconstruction of analog data subject to these constraints. Specific Shannon-theoretical limits for the performance of combined sampling and source coding - the tradeoff between the digitizer's sampling rate and quantization precision in terms of distortion will be investigated. In addition, the optimal fidelity in the reconstruction of analog data from a sequence of samples will be derived. Fundamental limits on communication under energy constraints, where an analog sensor must digitize and communication its data under finite energy constraints, will be determined. Methods used will draw from prior work in Shannon capacity, rate-distortion theory, joint source-channel coding and separation, sampling, estimation, and statistics. In particular, after extending existing results on rate-distortion theory to incorporate sub-Nyquist sampling, joint source-channel coding techniques applied to the sampled analog data transmitted over a rate-limited channel will be developed. Energy constraints will be incorporated based on information-theoretic models for computation energy along with recent results on finite-block-length codes and minimum energy-per-bit capacity.