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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...
With a growing system complexity in the IoT framework, many networked cyber-physical systems work in a hierarchical fashion. Layers of information outputs and command inputs are available. An active area of research is in optimizing the design of policies and control command that influence information flow for such multi-layered systems. Our focus in current research is to first formulate the control...
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,...
A two-stage-type algorithm is presented for blind source separation in the overdetermined instantaneous mixture case. The algorithm accomplishes two tasks: blind identification for estimating the mixing matrix and source estimation for recovering the original source signals with the identified mixing matrix. In this paper, we focus on the former task. A new mixing matrix identification method, which...
We have developed an efficient information-maximization method for computing the optimal shapes of tuning curves of sensory neurons by optimizing the parameters of the underlying feedforward network model. When applied to the problem of population coding of visual motion with multiple directions, our method yields several types of tuning curves with both symmetric and asymmetric shapes that resemble...
Consensus building in wireless sensor networks (WSN's) has been studied extensively over a number of years. However, the role of mobile nodes in a network has not been fully explored. This paper shows that introducing mobility increases the rate of convergence in partially connected and disconnected WSN. It also shows that selection of mobile nodes influences the rate of convergence. The mobile node...
We present a simple theoretical model and supporting experimental evidence for a new approach to maximizing the efficiency of wireless power transfer (WPT) to a receiver from multiple transmitters. Specifically, we consider a multiple-input single-output (MISO) WPT system using near-field inductive coupling to transfer power from multiple transmitting coils to a single receiver; the use of multiple...
Advanced Metering Infrastructure (AMI) plays a crucial role in Demand Side Management (DSM) in Smart Grid systems. It provides real-time, two-way communication capabilities between a utility/load aggregator and consumers. The communication infrastructure, by virtue of topological weaknesses, is vulnerable to cyber attacks that are undetectable or stealthy. This work investigates the topological vulnerabilities...
C-RAN! (C-RAN!) is a new paradigm for wireless networks that centralizes the signal processing in a computing cloud, allowing commodity computational resources to be pooled. While C-RAN improves utilization and efficiency, the computational load occasionally exceeds the available resources, creating a computational outage. This paper provides a mathematical characterization of the computational outage...
We address secure communication for energy-harvesting (EH) based cooperative cognitive radio networks (CRNs), in which several EH secondary users (SUs) cooperate with one primary user (PU) for both data transmission and energy harvesting. To increase the amount of energy harvested by SUs while improving spectrum utilization, we suppose that SUs are equipped with orthogonally dual-polarized antennas...
With a customized characterization of types, every universal one-to-one coding algorithm can be described as follows: assign sequences to binary strings based on their type class sizes from smallest to largest. With this view, the universal coding problem is to optimally characterize types. In this paper, this Type Size approach is studied for universal source coding of an exponential family of distributions,...
New device technologies such as spintronics, carbon nanotubes, and nanoscale CMOS incur random transient failures, where the failure probability is governed by the energy consumption through energy-failure functions. At the same time, there is growing use of deep neural networks for many inference applications, and specialized hardware is being developed with these nanotechnologies as physical substrates...
This paper considers sparse signal recovery under sensing constraints originating from the limitations of practical data acquisition systems. Such limitations introduce non-linearities in the underlying measurement model. We first develop a more accurate measurement model with structured noise representing a known non-linear function of the sparse signal obtained by leveraging side information about...
We consider a wireless network of M nodes connected together in a decentralized way (for example as an ad hoc network), and according to pre-specified rules. There are other malicious node(s) which can be either inserted or infected which are trying to disturb the operation of the network. The nodes are cooperating to defend the network (and eventually themselves) by isolating the misbehaved node(s)...
In this work, we consider the economics of the interaction between Mobile Virtual Network Operators (MVNOs) and Mobile Network Operators (MNOs). We investigate the incentives of an MNO for offering some of her resources to an MVNO instead of using the resources for her own. We formulate the problem as a sequential game. We consider a market with one MNO and one MVNO, and a continuum of undecided endusers...
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...
Stacked autoencoders have shown success in generating robust features for images and speech classifications, but there has been limited work in applying stacked autoencoders in signal recognition. In this paper, we study the feasibility of stacked autoencoder based order recognition of continuous phase FSK. The features used for recognition are the approximate entropy (ApEn) of the received signal,...
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