Capacity of wireless channels and networks
One of the fundamental issues in the design of a wireless communication system is how fast data can be transmitted reliably. The theoretical upper bound for speed of data transmission over a channel is called the channel capacity. The capacity of time-varying channels depends on what is known about the channel at the transmitter and/or receiver. It also depends on whether rate can be adaptive or constant, as well as whether or not service outage can be tolerated. We have investigated capacity and capacity regions of time-varying multiuser channels, cellular systems, and ad-hoc networks with one or more antennas at the transmitter(s) and receiver(s).
Information limits of analog to digital schemes
Although most of the information stored in today's machines is digital, real world physical systems assume a continuum of states. This implies that analog to digital conversion and possible information loss is an inherent part of any practical wireless communication system. This information loss can be associated with both sampling and quantization errors. Although the sampling error can be eliminated by sampling at the Nyquist rate, technology limitations may preclude us from doing so and sub-Nyquist sampling techniques have to be employed. We study the performance loss due to sub-Nyquist sampling from an information theoretic framework. Specific metrics include capacity loss in channels with sampling at the receiver end and the minimal distortion in rate-limited descriptions of sampled processes.
Rethinking cells in cellular system design
Next-generation cellular system will consist of an all-IP backbone network core with hierarchical macrocells, small cells, and indoor femtocells all sharing the same limited bandwidth. Optimal resource allocation, handoff, and WiFi offload is needed to achieve the Gbps data rates and near-ubiquitous coverage demanded by users. We are investigating self-organizing network techniques, timefrequency allocation, and handoffoffload mechanisms to achieve these ambitious performance goals.
We consider massive MIMO systems, i.e. systems with a large number of transmitter and/or receiver antennas, and look into the benefits achievable in the asymptotic regime of large antennas. Our work focuses on low-complexity transmission and detection of uncoded symbols reliably through the said systems. One direction we are exploring exploits the spatial diversity inherent in large antenna systems to provide noncoherent communication schemes, i.e. schemes where the transmitter and the receiver do not know the instantaneous communication channel, but know the statistics. Based on a characterization of the large antenna behaviour of such systems, we propose encoding and decoding schemes and investigate the competitiveness of these schemes with schemes exploiting instantaneous channel knowledge. Our investigations use techniques from optimization, error exponent analysis, space-time coding, and robust design. We also have developed novel cryptographic schemes based on large dimensional random matrices. In these schemes the complexity involved in decoding massive MIMO systems is used to provide low-complexity physical-layer encryption commensurate with the most sophisticated forms of application-layer encryption.
Software-defined wireless networks
Software-defined wireless networks provide a unified control plane for wireless hardware to dynamically optimize heterogeneous hardware across multiple spectral bands. The control plane allows for dynamic configuration of radio parameters such as channel, power control, antenna parameters to minimize interference and provide the required performance to heterogenous applications. The control plane can also provide seamless handoff between different wireless networks as well as utilize multiple networks simultaneously to provide robustness as required for demanding applications such as video and real-time control.
Green wireless system design
A large chunk of the wireless system infrastructure in place today was not built with energy efficiency in mind. However, with ever increasing demands for wireless services, this is a significant bottleneck to future expansion. We take a cross-layer approach to studying energy efficiency in the entire communication chain. We investigate the fundamental limits of energy involved in transporting bits from one point to another. We develop models of bit transport interactions across the entire communication system chain, from the circuits of the baseband processing blocks to the networks of communicating agents that they support. This allows us to investigate the tradeoffs among QoS, power consumption and cooperation requirements on wireless networks at different layers of the communication chain.
Cognitive radio networks
Spectrum is a very scarce resource in commercial deployments of wireless networks. This has led to interest in intelligent or cognitive radios which would be able to make opportunistic use of spectrum not licensed to it, thereby coexisting with legacy systems. Our research focuses on developing enabling schemes for this technology, right from coding techniques optimal for different regimes of operation to techniques which allow smarter interference management under very general conditions.
Compressed sensing in wireless systems
While the Shannon-Nyquist sampling theorem has made possible the representation of real world signals in digital domain, and is one of the most prominent theoretical foundations of the digital age, the frequency domain restriction on signals is often too restrictive. We investigate the limits of communication system design outside this paradigm and look at exploiting other prior knowledge of real world systems and channels to come up with design insights for the practicing engineer.
Heterogeneous parallel wireless networks
Wireless networks today are becoming heterogeneous by integrating multiple radios with different capabilities, protocol stacks, and spectrum allocations. The flexibility of these different radios allows for more general dynamic resource allocation, better coverage, and higher capacity. In heterogeneous networks, users can be connected to transmitters via parallel channels operating over different networks, such as a cellular network and a WiFi network. How to optimally communicate over such parallel channels is a fundamental question that we seek to address. Building on the information theory and coding techniques, our research aims to design the optimal communication scheme to meet the fundamental limit of such parallel channel, i.e., to achieve the maximum communication rate transmitted reliably (called capacity). Another fundamental question we seek to address is how to optimally utilize channel state information (CSI) feedback in such heterogeneous parallel channels. CSI feedback is very important in the multiuser communication, and the capacity gap between the case with instantaneous CSI feedback and the case with delayed CSI feedback is often very significant. A new insight on such heterogeneous parallel channels is that, delayed CSI feedback can achieve the same performance as instantaneous CSI feedback, in terms of sum degrees of freedom (prelog factor of capacity). Such insight can allow us to design a communication system to achieve the maximum communication rate with less feedback overhead.
Distributed sensing, communications, and control
This area of research focuses on the interplay between control and communications, optimal sparse sensing and state estimation for high-dimensional control systems, as well as sensor placement, outage detection, and cyber-physical security. Questions we are trying to address here include how to optimally track a process or infer the dynamics of a system from a good choice of limited samples of large dimensional measurements.
Reliability and security of modern power grid
Economic and social loss due to power grid outages and blackouts have been huge due to the lack of effective sensing, communications, data processing and control over the grid. As the grid becomes increasingly complex and dependent on modern information technology, its security against cyber-physical attacks is another critical issue. We provide solutions to these critical issues by leveraging the advancement in sensing devices and developing efficient and real-time power network monitoring, analysis and control systems.
Reliable and efficient integration of renewable energies
Renewable energies, notably wind and solar, play crucial roles in achieving sustainable energy supplies for our society. While renewable energies are clean and do not rely on fossil fuels, they are also variable and uncertain in nature. Such variability and uncertainty pose great challenges on energy system operation with deep renewable energy penetration. We provide a variety of solutions for reliable and efficient integration of renewable energies. Exploiting statistical diversity among difference sources of renewables, efficient market mechanisms are developed for aggregation of renewable energies, and significant reduction of their variability and uncertainty is achieved. We also evaluate the benefit from energy storage in risk reduction of renewable energies.
Communications and signal processing in bioengineering and neuroscience
One of our major focus areas is the use of the information theoretic notion of directed information to improve detection of neural connectivity. Current methods of inferring the connectivity of neurons generalize notions of Granger causality and find the directed information of a pair of neurons’ spike trains. These methods can lead to a few major false positive scenarios. These false positives have been shown to be avoidable if all vital neurons in the network are recorded and included in the directed information measure. In practice, however, it is restrictive and expensive to capture the signal from all such vital neurons. To address this issue, we propose a modified method that uses the spike trains of only the two neurons as well as side information readily available to the experimenter to detect neural connectivity with fewer false positives.
We have also applied signal processing methods to separate gene expression microarray mixed cell tissue samples. For example, tumor biopsy tissues that are profiled using microarrays contain a mixture of tumor cells, infiltrating immune cells and additional microenvironmental cells such as fibroblasts. Previous studies have shown that microarray measurements are well modeled as a linear mixture of multiple signatures from different cell types. This problem is almost identical to signal processing of hyper-spectral imaging. Thus, by incorporating state of the art signal processing tools we successfully identify the cell types in the mixture, extract their individual signatures and relative proportions per sample (Zuckerman et al. PLoS Comput Biol. 2013;9(8):e1003189). Application of this method to the abundance of gene expression microarray samples that exist in publicly available repositories can be a powerful tool for the discovery of phenomena otherwise not detected in the mixed tissue samples of individual experiments.