Mohammed M. Olama

Stochastic power control for time-varying long-term fading wireless networks

A new time-varying (TV) long-term fading (LTF) channel model which captures both the space and time variations of wireless systems is developed. The proposed TV LTF model is based on a stochastic differential equation driven by Brownian motion. This model is more realistic than the static models usually encountered in the literature. It allows viewing the wireless channel as a dynamical system, thus enabling well-developed tools of adaptive and nonadaptive estimation and identification techniques to be applied to this class of problems. In contrast with the traditional models, the statistics of the proposed model are shown to be TV, but converge in steady state to their static counterparts. Moreover, optimal power control algorithms (PCAs) based on the new model are proposed. A centralized PCA is shown to reduce to a simple linear programming problem if predictable power control strategies (PPCS) are used. In addition, an iterative distributed stochastic PCA is used to solve for the optimization problem using stochastic approximations. The latter solely requires each mobile to know its received signal-to-interference ratio. Generalizations of the power control problem based on convex optimization techniques are provided if PPCS are not assumed. Numerical results show that there are potentially large gains to be achieved by using TV stochastic models, and the distributed stochastic PCA provides better power stability and consumption than the distributed deterministic PCA.

Position and Velocity Tracking in Mobile Networks Using Particle and Kalman Filtering With Comparison

This paper presents several methods based on signal strength and wave scattering models for tracking a user. The received-signal level method is first used in combination with maximum likelihood (ML) estimation and triangulation to obtain an estimate of the location of the mobile. Due to nonline-of-sight conditions and multipath propagation environments, this estimate lacks acceptable accuracy for demanding services, as the numerical results reveal. The 3-D wave scattering multipath channel model of Aulin is employed, together with the recursive nonlinear Bayesian estimation algorithms to obtain improved location estimates with high accuracy. Several Bayesian estimation algorithms are considered, such as the extended Kalman filter (EKF), the particle filter (PF), and the unscented PF (UPF). These algorithms cope with nonlinearities in order to estimate mobile location and velocity. Since the EKF is very sensitive to the initial state, we propose the use of the ML estimate as the initial state of the EKF. In contrast to the EKF tracking approach, the PF and UPF approaches do not rely on linearized motion models, measurement relations, and Gaussian assumptions. Numerical results are presented to evaluate the performance of the proposed algorithms when the measurement data do not correspond to the ones generated by the model. This shows the robustness of the algorithm based on modeling inaccuracies.

Stochastic differential equations for modeling, estimation and identification of mobile-to-mobile communication channels

Mobile-to-mobile networks are characterized by node mobility that makes the propagation environment time varying and subject to fading. As a consequence, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) which varies from one observation instant to the next. The current models do not capture and track the time varying characteristics. This paper is concerned with dynamical modeling of time varying mobile-to-mobile channels, parameter estimation and identification from received signal measurements. The evolution of the propagation environment is described by stochastic differential equations, whose parameters can be determined by approximating the band-limited DPSD using the Gauss-Newton method. However, since the DPSD is not available online, we propose to use a filter-based expectation maximization algorithm and Kalman filter to estimate the channel parameters and states, respectively. The scheme results in a finite dimensional filter which only uses the first and second order statistics. The algorithm is recursive allowing the inphase and quadrature components and parameters to be estimated online from received signal measurements. The algorithms are tested using experimental data collected from moving sensor nodes in indoor and outdoor environments demonstrating the methods viability.

Cybersecurity through Real-Time Distributed Control Systems

Critical infrastructure sites and facilities are becoming increasingly dependent on interconnected physical and cyber-based real-time distributed control systems (RTDCSs). A mounting cybersecurity threat results from the nature of these ubiquitous and sometimes unrestrained communications interconnections. Much work is under way in numerous organizations to characterize the cyber threat, determine means to minimize risk, and develop mitigation strategies to address potential consequences. While it seems natural that a simple application of cyber-protection methods derived from corporate business information technology (IT) domain would lead to an acceptable solution, the reality is that the characteristics of RTDCSs make many of those methods inadequate and unsatisfactory or even harmful. A solution lies in developing a defense-in-depth approach that ranges from protection at communications interconnect levels ultimately to the control system s functional characteristics that are designed to maintain control in the face of malicious intrusion. This paper summarizes the nature of RTDCSs from a cybersecurity perspec tive and discusses issues, vulnerabilities, candidate mitigation approaches, and metrics.

Recursive estimation and identification of time-varying long-term fading channel

This paper is concerned with modeling of time-varying wireless long-term fading channels, parameter estimation, and identification from received signal strength data. Wireless channels are represented by stochastic differential equations, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal strength data. These algorithms estimate the channel path loss and identify the channel parameters recursively. Numerical results showing the viability of the proposed channel estimation and identification algorithms are presented.

Position and velocity tracking in mobile cellular networks using the particle filter

This paper presents a new method for tracking a mobile based on Aulins wave scattering model This model takes into account non line of sight and multipath propagation environments, which are usually encountered in wireless fading channels. According to Aulins model, the received instantaneous electric field at the base station is a nonlinear function of the mobile location and velocity. A method based on particle filtering (sequential Monte Carlo methods) that copes with nonlinearities in order to estimate the mobile location and velocity is proposed. In contrast to standard target tracking literature we do not rely on linearized motion models, measurement relations, and Gaussian assumptions. Numerical results are presented to evaluate the accuracy of the proposed method. They demonstrate significant accuracy improvement over known algorithms

Position and Velocity Tracking in Cellular Networks Using Particle and Kalman Filtering with Comparison

This paper presents two methods for tracking a user based on Aulins wave scattering channel model. The first method is based on the extended Kalman filter approach, while the second method is based on the particle filter approach. Aulins model takes into account non line of sight and multipath propagation environments, which are usually encountered in wireless fading channels. The received instantaneous electric field at the base station is a nonlinear function of the mobile location and velocity as Aulins model indicate. The proposed methods cope with nonlinearities in order to estimate the mobile location and velocity. The assumptions are knowledge of the channel and access to the instantaneous received field, which are obtained through channel sounding samples from the receiver circuitry. In contrast to the extended Kalman filter tracking approach, the particle filter approach does not rely on linearized motion models, measurement relations, and Gaussian assumptions. Numerical results are presented to evaluate the accuracy of the proposed methods

Stochastic channel modeling for ad hoc wireless networks

Due to nodes mobility and environmental changes in mobile ad hoc networks, the ad hoc channel is time varying and subject to fading. As a consequence of these variations, the statistical characteristics of the received signal vary continuously, giving rise to a Doppler power spectral density (DPSD) which varies from one observation instant to the next. Therefore, the traditional models can no longer capture and track complex time variations in the propagation environment. These time variations compel us to introduce more advanced dynamical models in order to capture higher order dynamics of the ad hoc channel. Stochastic ad hoc channel models, in which the evolution of the dynamical channels is described by a stochastic state space representation, are derived. The parameters of the stochastic state space models are determined by approximating the band limited DPSD. Two approximating methods are considered. The first one is simple and the second one is the complex cepstrum method. Inphase and quadrature components of the proposed stochastic ad hoc channel models are derived. Numerical results show that link performance for ad hoc case is worse than cellular case, but the performance gap shrinks with increased mobility

A Qualitative Readiness-Requirements Assessment Model for Enterprise Big-Data Infrastructure Investment

In the last three decades, there has been an exponential growth in the area of information technology providing the information processing needs of data-driven businesses in government, science, and private industry in the form of capturing, staging, integrating, conveying, analyzing, and transferring data that will help knowledge workers and decision makers make sound business decisions. Data integration across enterprise warehouses is one of the most challenging steps in the big data analytics strategy. Several levels of data integration have been identified across enterprise warehouses: data accessibility, common data platform, and consolidated data model. Each level of integration has its own set of complexities that requires a certain amount of time, budget, and resources to implement. Such levels of integration are designed to address the technical challenges inherent in consolidating the disparate data sources. In this paper, we present a methodology based on industry best practices to measure the readiness of an organization and its data sets against the different levels of data integration. We introduce a new Integration Level Model (ILM) tool, which is used for quantifying an organization and data system’s readiness to share data at a certain level of data integration. It is based largely on the established and accepted framework provided in the Data Management Association (DAMADMBOK). It comprises several key data management functions and supporting activities, together with several environmental elements that describe and apply to each function. The proposed model scores the maturity of a system’s data governance processes and provides a pragmatic methodology for evaluating integration risks. The higher the computed scores, the better managed the source data system and the greater the likelihood that the data system can be brought in at a higher level of integration.

Modeling resource, infrastructure, and policy cost layers for optimizing renewable energy investment and deployment

This paper presents a framework for creating a common spatial canvass that can bring together considerations of resource availability, infrastructure reliability, and development costs while strategizing renewable energy investment. We describe the underlying models and methodologies that annotate an investment plan for potential sites over a time-period with costs and constraints which may be imposed on distance from infrastructure, system impact on infrastructure, and policy incentives. The framework is intended as an enabler for visualization, optimization and decision making across diverse dimensions while searching for lucrative investment-plans.