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Massive MIMO is the currently most compelling sub-6 GHz wireless access technology for 5G. Since its inception about a decade ago, it has evolved from a wild “academic” idea to one of the most vibrant research topics in the wireless communications community, as well as a main work item in 5G standardization. The concept is to equip base stations with arrays of many antennas that serve many terminals...
We consider the setting in which generators compete in scalar-parameterized supply functions to serve an inelastic demand spread throughout a transmission constrained power network. The market clears according to a locational marginal pricing mechanism, in which the independent system operator (ISO) determines the generators' production quantities so as to minimize the revealed cost of meeting demand,...
We investigate the ability of a homogeneous collection of deferrable energy consumers to behave as a battery; that is, to absorb and release energy in a controllable fashion up to fixed and predetermined limits on volume, charge rate and discharge rate. We derive bounds on the batteries that can be emulated and show that there is a fundamental conflict between the ability to absorb and release energy...
In the last fifty years, signal processing and machine learning experts have developed a wide range of algorithms to address a diverse set of inference and information processing tasks. New algorithms are often developed based on new structures that experts discover in data. For instance, the JPEG compression uses the sparse representation of images in the discrete cosine domain. In this talk, all...
The problem linear estimation problem has applications in linear regression, communications, compressed sensing, and machine learning. In recent work, we have provided a rigorous characterization of mutual information (MI) and minimum mean square error (MMSE) in the large system limit. In this talk, we address a phenomenon known as the decoupling principle, which says that the posterior distribution...
With the number of Phasor Measurement Units (PMUs) in the North American power grid scaling up into the thousands, system operators are gradually inclining towards distributed cyber-physical architectures for executing wide-area monitoring and control operations using Synchrophasors. Traditional centralized approaches, in fact, are anticipated to become untenable soon due to various factors such as...
Most of the research on dictionary learning has focused on developing algorithms under the assumption that data is available at a centralized location. But often the data is not available at a centralized location due to practical constraints like data aggregation costs, privacy concerns, etc. Using centralized dictionary learning algorithms may not be the optimal choice in such settings. This motivates...
Random number generators have become an essential part of our modern life, in ways that average consumers seldom appreciate. Much of modern communication and digital storage relies on cryptography to ensure privacy, security, and authentication. Random number generation is a critical process that underlies all of these methods. The rapidly increasing demand for bandwidth, storage, and computation,...
We address sparse signal, i.e. image recovery in a Bayesian estimation framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the popular spike and slab prior which is considered the gold standard in the statistics literature. The optimization problem resulting from this model has broad applicability in recovery, regression and classification...
I describe efforts at LANL in modeling and controlling aggregated ensembles of stochastic loads. The modeling part of the talk will focus on accurate analysis of the steady state and transient behavior of the probability distribution of an ensemble of loads (thermal, pools, electric cars or alike) as observed by an aggregator. Then in the control part of the talk I describe novel algorithmically tractable...
We propose a new encoding scheme for the wiretap channels with a random state known non-causally to the encoder but not necessarily to the decoder (the Gelfand-Pinsker setting). This setting combines two scenarios with celebrated results in information theory-the wiretap channel and the Gelfand-Pinsker channel-and it so happens that essentially the same encoding scheme is optimal for both scenarios...
Demand response is a cheap source of flexibility for power systems with renewables. Loads are uncertain due to unavailable physical models, random factors such as weather and human behavior, and privacy. Load aggregators can use online learning to characterize loads as they use them, reducing uncertainty without relying on costly pilot studies. In this talk we discuss multi-armed bandit and online...
We provide formal definitions and efficient secure techniques for — turning biometric information into keys usable for any cryptographic application, and — reliably and securely authenticating biometric data.
It has been recently shown that judicious use of correlation priors can lead to significant improvement in the performance of sparse estimation algorithms. This happens primarily due to two reasons: (i) second order statistics or covariance matrix of signals can possess unique structures that are not captured in the raw measurements (ii) these structures involve non linear functions of the underlying...
This talk describes a new adaptive variant of K-SVD dictionary learning that is suitable for the highly varied features present in MRI images. This variant is used to regularize SPIRiT parallel MRI reconstruction to produce higher quality images from incomplete measurements. The approach described involves modeling the dictionary approximation error as nearly sparse across the patches of the reconstructed...
Optimal power flow is a central problem in the operation of power systems. So far the majority of the literature deals with offline algorithms for traditional applications, but the proliferation of distributed energy resources and smart appliances in power networks motivates real-time and scalable algorithms. We introduce a real-time OPF algorithm based on quasi-Newton methods that can track the optimal...
We develop a covert communication scheme for binary-input asynchronous Discrete Memoryless Channels based on binary polar codes, in which legitimate parties exploit uncertainty created by both the channel noise and the time of transmission. The proposed code jointly ensures reliable communication for a legitimate receiver and low probability of detection with respect to an adversary, both observing...
The vocal tract is the universal human instrument played with great dexterity and skill in the production of speech to convey rich linguistic and paralinguistic information. The understanding of how individuals differ in their speech articulation due to differences in shape and size of their physical vocal instrument, and its acoustic consequences are not well understood. Knowledge of how people differ...
Quantum Zeno blockade offers a distinct approach to signal manipulation and logic operations in a counterintuitive “interaction-free” implementation. Previous experimental studies have used nonlinear waveguides and optical cavities, both in bulk-optics settings. We report the observation of quantum Zeno blockade in a chip-integrable platform, whose results point to all-optical information processing...
Frequency modulation of an electric field inside of an active medium with a very short gain recovery lifetime has become of recent interest due to the observed and calculated behavior of Quantum Cascade lasers (QCLs) exploiting optical nonlinearities to generate frequency combs in the spectral domain. Thus, in support of better understanding this behavior, we have investigated the effects of stochastic,...
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