Kyoung-Jae Lee

Simultaneous Wireless Information and Power Transfer for Cooperative Relay Networks With Battery

In this paper, we investigate simultaneous wireless information and power transfer in a cooperative communication network consisting of one source, one battery-enabled relay, and one destination node. An amplify-and-forward relaying method is considered, where the relay node harvests energy from the received signal power to charge its battery, which is used to forward the received signal to the destination. We also consider a direct link between the source and the destination. The direct link signal can be combined with the relaying signal at the destination node using maximum ratio combining. Under the delay-limited transmission mode, closed form expressions for outage probability are derived for a battery-enabled relay. Our analytical results reveal the advantage of cooperative relay networks with a direct link. Also, we extend our design to a multiple-relay scenario, where the best relay is selected from the available number of relays, based on the information of relay locations. Finally, we demonstrate from simulation results based on outage probability that our proposed methods are efficient in comparison with Monte Carlo simulations.

Laplace based approximate posterior inference for differential equation models

Ordinary differential equations are arguably the most popular and useful mathematical tool for describing physical and biological processes in the real world. Often, these physical and biological processes are observed with errors, in which case the most natural way to model such data is via regression where the mean function is defined by an ordinary differential equation believed to provide an understanding of the underlying process. These regression based dynamical models are called differential equation models. Parameter inference from differential equation models poses computational challenges mainly due to the fact that analytic solutions to most differential equations are not available. In this paper, we propose an approximation method for obtaining the posterior distribution of parameters in differential equation models. The approximation is done in two steps. In the first step, the solution of a differential equation is approximated by the general one-step method which is a class of numerical numerical methods for ordinary differential equations including the Euler and the Runge-Kutta procedures; in the second step, nuisance parameters are marginalized using Laplace approximation. The proposed Laplace approximated posterior gives a computationally fast alternative to the full Bayesian computational scheme (such as Makov Chain Monte Carlo) and produces more accurate and stable estimators than the popular smoothing methods (called collocation methods) based on frequentist procedures. For a theoretical support of the proposed method, we prove that the Laplace approximated posterior converges to the actual posterior under certain conditions and analyze the relation between the order of numerical error and its Laplace approximation. The proposed method is tested on simulated data sets and compared with the other existing methods.

Inter-Cluster Design of Wireless Fronthaul and Access Links for the Downlink of C-RAN

This letter studies the joint design of wireless fronthaul and radio access links for the downlink of a cloud radio access network (C-RAN). In a C-RAN, high spectral efficiency can be achieved by migrating the baseband processing functionalities to a baseband processing unit (BBU). However, due to the increased system cost, the size of cluster should be kept relatively small, and hence the spectral efficiency performance tends to be limited by the impact of inter-cluster interference signals. This letter tackles the problem of jointly optimizing the fronthaul and access links across multiple clusters with the goal of maximizing the sum-rate of all the user equipments belonging to the clusters while satisfying the power constraints at the BBUs and remote radio heads. Via numerical results, the effectiveness of the proposed algorithm is confirmed.

Cooperative and Cognitive Hybrid Satellite-Terrestrial Networks

The development of next-generation wireless networks envisages the seamless integration between satellite systems and terrestrial cellular networks. The spectral resources allocated to satellite are not fully utilized, whereas terrestrial spectral resources are becoming overutilized day by day. Therefore, it is important to ascertain an efficient way to share the resources of space-based networks with the terrestrial networks. In view of different capabilities and services, the geostationary earth orbit (GEO) satellite could be the component of the space segment, while the terrestrial segment may be a 3G/4G heterogeneous network. With growing requirements of broadband services and limited availability of spectral resources, higher frequency bands (above 10 GHz), viz., Ku and Ka, may also need to be assigned for mobile satellite services. As such, it is quite challenging to transmit over such higher frequencies due to severe effects of atmospheric turbulence and scattering. Hence, it is imperative to explore cognitive spectrum sharing techniques to enhance spectrum utilization efficiency through the development of hybrid satellite-terrestrial communication systems. In this regard, this chapter studies the satellite-terrestrial communications with the emerging cooperative and cognitive radio techniques to promote the information and communication technology (ICT) sector in a more efficient and reliable manner. More specifically, the chapter addresses the hybrid satellite-terrestrial system design, planning, and resource allocation problems while exploiting cooperation among available network resources with hierarchical spectrum sharing to cope with the demands of futuristic wireless network.

Transceiver Designs for MIMO Relaying Systems with Imperfect Channel State Information

We focus on imperfect channel state information (ICSI) in closed-loop multiple-input multiple-output (MIMO) amplify-and-forward (AF) relaying systems. First, near-optimal closed-form solutions for transceiver designs are provided for the source-to-relay-to-destination link with ICSI at all nodes. Next, considering a nonnegligible direct link with ICSI available only at the relay and destination and no CSI or ICSI at the source, near-optimal designs are proposed. The Wiener or minimum mean square error (MMSE) filter is employed at the destination to estimate the transmitted signal. The effect of CSI mismatch on the performance of the proposed scheme is then shown through simulations.

Bayesian inference using two-stage Laplace approximation for differential equation models

We consider the problem of Bayesian inference for parameters in non-linear regression models whereby the underlying unknown response functions are formed by a set of differential equations. Bayesian methods of inference for unknown parameters rely primarily on the posterior obtained by Bayes rule. For differential equation models, analytic and closed forms for the posterior are not available and one has to resort to approximations. We propose a two-stage Laplace expansion to approximate the marginal likelihood, and hence, the posterior, to obtain an approximate closed form solution. For large sample sizes, the method of inference borrows from non-linear regression theory for maximum likelihood estimates, and is therefore, consistent. Our approach is exact in the limit and does not need the specification of an additional penalty parameter. Examples in this paper include the exponential model and SIR (Susceptible-Infected-Recovered) disease spread model.

Introduction to the Indian Buffet Process: Theory and Applications

The Indian Buffet Process is a stochastic process on equivalence classes of binary matrices having finite rows and infinite columns. The Indian Buffet Process can be imposed as the prior distribution on the binary matrix in an infinite feature model. We describe the derivation of the Indian buffet process from a finite feature model, and briefly explain the relation between the Indian buffet process and the beta process. Using a Gaussian linear model, we describe three algorithms: Gibbs sampling algorithm, Stick-breaking algorithm and variational method, with application for finding features in image data. We also illustrate the use of the Indian Buffet Process in various type of analysis such as dyadic data analysis, network data analysis and independent component analysis.