Browsing by Author "Fapojuwo, Abraham Olatunji"
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- ItemOpen AccessBlind Compensation of Impairments in Wireless Transceivers(2018-07-24) Aziz, Mohsin; Ghannouchi, Fadhel M.; Helaoui, Mohamed; Fapojuwo, Abraham Olatunji; Potter, Michael E.; Sawan, MohamadModern wireless communication systems suffer from hardware imperfections that degrade the quality of transmission signals and make the detection of signal quite difficult at the receiver. This thesis focuses on the gain and phase imbalances caused by the modulators and demodulators and nonlinearity stemming from the transmitter power amplifier. Broadly, the contribution of this thesis is two folds: Blind solutions to mitigate the above mentioned hardware impairments of the wireless link through the proposal of a methodology based on the derivation of closed form expressions for the probability density functions (PDFs) of the signals in the presence of these impairments. In this regards, firstly, a PDF in the presence of modulator’s and demodulator’s in-phase and quadrature phase imbalances has been derived and validated. A maximum likelihood estimation of the imbalance parameters has been proposed to mitigate these imperfections. The proposed methodology has been evaluated using extensive simulations and measurements. To evaluate the static performance of the proposed methodology, 10 KHz modulated signal has been used. Measurement results show that an image rejection of greater than 30 dB can be achieved. For a larger bandwidth signal of 1 MHz, around 19 dB improvement in NMSE can be achieved using the proposed methodology, as compared to the uncompensated case. Secondly, a closed form PDF in the presence of gain and phase imbalances and the transmitter’s power amplifier nonlinearity has been derived and validated. A cumulative distribution function-based methodology has been adopted to mitigate the effects of power amplifier’s amplitude distortions. For the modulator’s impairments, a maximum likelihood estimation of the imbalance parameters has been used to estimate and compensate for the modulator’s imperfections. Using measurements, for a 3 MHz LTE signal, a normalized mean squared error and an error vector magnitude of -35 dB and 1.5% can be achieved, respectively.
- ItemOpen AccessDesign and Analysis of Wireless Networks for Petroleum Refineries(2017) Herrmann, Michael James; Messier, Geoffrey; Fapojuwo, Abraham Olatunji; Ghaderi, MajidThis thesis investigates wireless sensor network design for industrial petroleum refineries. A wireless channel measurement campaign is conducted in a Shell Canada gas refinery west of Calgary. The propagation measurements are the first to characterize the large-scale channel statistics for peer-to-peer transmission in an outdoor refinery environment. It is shown that the propagation is well characterized by the familiar standard pathloss and log-normal shadowing model. This model is then used in building a machine-to-machine network performance simulation for process control in a petroleum refinery. The simulation requires the use of cross-layer optimizations to determine the routes which maximize the network lifetime. Linear Programming and Mixed Integer Linear Programming problems are formulated and evaluated to solve the cross-layer design problem. Carrier Sense Multiple Access (CSMA) and Time Division Multiple Access (TDMA) schemes are both used, but it is shown that TDMA has superior performance.
- ItemOpen AccessDynamic Resource Allocation and Pricing: A Randomized Auction Perspective(2017) Zhang, Linquan; Li, Zongpeng; Wu, Kui; Wang, Yingxu; Woelfel, Philipp; Fapojuwo, Abraham OlatunjiAuctions are widely employed to allocate scarce resources among strategic users. Truthfulness is a desired property of auctions, for eliminating falsified bids. The celebrated VCG auction is truthful, yet it becomes computationally infeasible when the underlying winner determination problem is NP-hard. Simply substituting the optimal solutions with approximate solutions makes a VCG auction lose its truthfulness property. In this thesis, we aim to address this challenge by employing a randomized auction framework, which translates a cooperative approximation algorithm into a truthful auction. Four resource allocation problems are carefully studied. We first discuss dynamic resource provisioning in clouds through the auction of virtual machines (VMs). It generalizes the existing literature by introducing combinatorial auctions of heterogeneous VMs, and models dynamic VM provisioning. We then study electricity markets between power grids and microgrids, an emerging paradigm of electric power generation and supply. We address the economic challenges arising from such grid integration, and design a power auction that explicitly handles the Unit Commitment Problem, a key challenge in power grids. Both power markets with grid-to-microgrid and microgrid-to-grid energy sales are studied, with an auction designed for each, under the same randomized auction framework. We next study emergency demand response (EDR) in multi-tenant colocation data centers. EDR in colocation data centers is challenging, due to lack of incentives to reduce energy consumption by tenants who control their servers and are typically on fixed power contracts with the colocation. We propose a new auction mechanism using the framework to enable colocation EDR, which leverages a reverse auction to provide monetary remuneration to tenants according to their energy reduction. We further study the online electricity cost minimization problem at a colocation data center. Electricity billing faced by a data center is nowadays based on both the total volume consumed, and the peak consumption rate. This leads to an interesting new combinatorial optimization structure on the electricity cost optimization problem. Applying the randomized framework, we model and solve the problem through two approaches: the pricing approach and the auction approach.
- ItemOpen AccessEnergy-Efficient Cooperative Routing in Wireless Networks(2010) Dehghan Shirehpaz, Mostafa; Ghaderi, Majid; Wang, Mea; Fapojuwo, Abraham OlatunjiIn this thesis, we explore physical layer cooperative communication in order to design network layer routing algorithms that are energy efficient. We assume each node in the network is equipped with a single omnidirectional antenna and that multiple nodes are able to coordinate their transmissions in order to take advantage of spatial diversity to save energy. Specifically, we consider cooperative diversity at physical layer and multi-hop routing at network layer, and formulate minimum energy routing as a joint optimization of the transmission power at the physical layer and the link selection at the network layer. We study wireless networks with both static and time-varying channels. Based on the optimal algorithms described throughout the thesis, we develop heuristic cooperative routing algorithms that find suboptimal routes, which are computationally simpler. Simulation results are also presented, which investigate the performance of optimal and heuristic routing algorithms in terms of energy efficiency and throughput.
- ItemOpen AccessImproving the Accuracy of GNSS Receivers in Urban Canyons using an Upward-Facing Camera(2018-07-03) Gakne, Paul Verlaine; O'Keefe, Kyle P. G.; Gao, Yang; Wang, Ruisheng; Fapojuwo, Abraham Olatunji; Ruotsalainen, LauraGlobal Navigation Satellite Systems are widely used as localization systems for various applications in indoor and outdoor environments. Autonomous vehicles for example rely on navigation sensors such as GNSS receivers, INS, odometers, LiDAR, radar, etc. However, none of these sensors alone is able to provide satisfactory position solutions in terms of accuracy, availability and reliability all the time and in all environments. This thesis presents a new tightly coupling method fusing the egomotion of a land vehicle estimated from a sky-pointing camera with GNSS signals and a digital map for navigation purposes in harsh urban canyon environments. The advantages of this configuration are three-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (known as segmentation). A satellite falling into the non-sky areas (e.g., buildings) will be rejected and not considered for the final position solution computation. Secondly, the narrow field of view sky-pointing camera is helpful for urban area egomotion estimation in the sense that it does not see most of the moving objects (e.g., cars) and thus is able to estimate the egomotion with fewer outliers than is typical with a forward-facing camera. Thirdly, the skyline can be extracted and serves as a finger print of the vehicle location in the city. This information can then be correlated with a 3D city model to obtain the vehicle location. In order to obtain an accurate solution from the proposed method, a few intermediate steps had to be taken into account. An improved image segmentation algorithm is presented. The output of this algorithm served for the skyline positioning and the camera-based multipath mitigation. Also, an accurate visual odometry was implemented. Moreover, the monocular-based visual odometry is able to determine the vehicle translation accurately but up to a scale only. An integrated system that tackles the scale factor issue is designed. From five datasets evaluated in this research, the proposed method has shown to be robust and provide more accurate position, velocity and attitude solution at least 83% of the time than the GNSS-only and loosely coupled GNSS/vision solutions.
- ItemOpen AccessLow-Overhead Packet Loss Diagnosis for Virtual Private Clouds using P4-Programmable NICs(2023-04-28) Aali Bagi, Soroush; Ghaderi, Majid; Maleki, Farhad; Fapojuwo, Abraham OlatunjiThe virtual private cloud, a logical division into tenants of a service provider's multi-tenant multi-cloud architecture, has become a major trend in the cloud computing industry because of its cost-effectiveness. Effective monitoring systems are critical for cloud tenants to identify and troubleshoot problems that cause negative impact on the reliability and availability of their services in the virtualized cloud infrastructure. However, the virtualized and complex nature of cloud infrastructure limits tenants' ability to pinpoint problems that result in performance degradation, such as packet loss, within the cloud environment. Current monitoring systems are either designed for the physical network or are not adequate to be able to detect the specific cause of packet losses in the physical network. Besides, there are few virtual network monitoring systems suitable for tenants that impose an insignificant overhead. This thesis presents a Packet Loss Diagnosis (PLD) system, which is a specific monitoring service designed to detect packet losses on virtual networks. It notifies virtual private cloud tenants with diagnostic information for troubleshooting. This comprehensive mechanism is based on the modern capabilities of P4 data plane programmable NICs and has a limited footprint in the network. Virtual networks at cloud-scale pose considerable challenges in achieving this goal. We differentiate between packet losses in virtual and physical networks to address these challenges. As for the former, we support tenants with comprehensive information that allows them to locate and resolve their problems on their own. In relation to the latter, we provide high-level information respecting service isolation and abstraction. Furthermore, PLD is designed to meet the requirements of a monitoring system designed for multi-tenant clouds at large scales. To prove the viability of the proposed scheme, we implemented it in P4 as well as investigated its performance through extensive experiments and Mininet simulation. The results of our study demonstrate that the proposed scheme can detect all packet losses reliably and can notify tenants in real-time of the occurrence of packet losses while imposing a minimal overhead that is proportional to the number of packet losses.
- ItemOpen AccessNatural Hedging of Longevity Risk with Mortality Key Rate Durations(2016) Sam, Charles; Ambagaspitiya, Rohana Shantha; Scollnik, David; Qiu, Chao; Fapojuwo, Abraham OlatunjiUnanticipated increase in life expectancy (longevity risk) of policy holders expose annuity providers to financial risk over a period of time. In order to measure the sensitivity of the actuarial present value to shifts in mortality rates for two portfolios for USA male: the Lee-Carter model is used to forecast future mortality rates with mortality data from mortality.org; and the term structure of interest rates are estimated using the Nelson-Siegel-Svensson model. Mortality key rate durations are proposed as a measure of the sensitivity of the actuarial present value due to the nonparallel shifts in mortality rates. The objectives for this thesis are to determine the best weight of surplus of life insurance to use for hedging against longevity risk, and ascertain how the mortality key rates periods should be selected for the two portfolios in order to have weighted surplus greater than zero using the natural hedging approach.
- ItemOpen AccessPerformance evaluation of passive optical fiber local area networks(1985) Fapojuwo, Abraham Olatunji; Irvine-Halliday, David
- ItemOpen AccessResource Allocation for Energy Harvesting D2D Communications Underlaying NOMA Cellular Networks(2021-11) ., Vatsala; Fapojuwo, Abraham Olatunji; Sesay, Abu-Bakarr B; Far, Behrouz HThe fifth generation (5G) cellular networks promise higher data rates, lower latency, higher energy efficiency, and increased bandwidth as compared to the fourth generation (4G) networks. To fulfill requirements raised by 5G networks, notable technologies such as Simultaneous Wireless and Information Power Transfer (SWIPT), device to device (D2D) communications and non-orthogonal multiple access (NOMA) are being extensively researched by the academia and industry. This thesis attempts to fulfill the requirements raised by current users and thus studies these technologies in the form of resource allocation problems for two SWIPT receiver architectures, namely, time switching (TS) and power splitting (PS) enabled D2D communications underlaying a NOMA based network with the objective of maximizing the D2D throughput while the rate requirements of the cellular users are guaranteed. The performance is compared with orthogonal multiple access (OMA) scheme. The problems are solved using two approaches: conventional optimization and deep learning. The conventional optimization entails a large number of iterations and involves significant time to solve the problem. Thus, deep learning is used where neural networks can learn from a dataset provided and used to predict an output. The neural networks involve less computation time and are more efficient. Therefore, a feed forward neural network (FFNN) - a kind of Deep Neural Network (DNN) is used to predict the D2D throughput. It was found that the efficient integration of D2D with the conventional cellular networks depends upon several factors such as environment, density of the network, geographical position of the devices and the rate requirement of the cellular users. Also, deep learning gives almost same results as that of the conventional optimization algorithm but is much more time efficient. In all the scenarios, the NOMA based networks give much better performance than the OMA based networks. The significance of the project lies in adopting D2D communications equipped with TS and PS SWIPT architectures in practical scenarios efficiently by studying the various factors that impact the adoption of D2D communications.
- ItemOpen AccessSensor Fusion-based Framework for Floor Localization(2018-07-03) Haque, Fahimul; Dehghanian, Vahid; Fapojuwo, Abraham Olatunji; Nielsen, Jørgen S.; Messier, Geoffrey G.Floor localization is at the heart of indoor positioning systems (IPSs) in multi-storey buildings with a variety of commercial, industrial, and health and safety applications. The prevalence of wireless technologies along with the integration of micro electro-mechanical sensors (e.g. barometers) in handheld devices and wearable gadgets of current vintage have prompted a surge in research and development efforts in the IPS area. Received signal strength (RSS), barometric altimetry (BA), and differential barometric altimetry (DBA) are three well-known methods of floor localization. However, the RSS-based methods lack the required accuracy, BA-based methods are prone to random errors due to local changes in the air pressure, e.g. from approaching weather systems, and DBA-based methods require installation of additional infrastructure (e.g. reference nodes and ad-hoc network for real-time information exchange). Fusion of BA and RSS is a viable solution for floor localization; nevertheless, available fusion algorithms are rather heuristic. In this dissertation, a theoretical framework is developed for fusing BA and Wi-Fi RSS measurements. The proposed framework involves a novel Monte Carlo Bayesian inference algorithm, for processing RSS measurements, and then fusion with BA using a Kalman Filter scheme. As demonstrated by our experimental results, the proposed sensor fusion algorithm achieves floor localization accuracy of 97% on average. The algorithm does not require new infrastructure, and has low computational complexity, hence, can be readily integrated into various state-of-the-art mobile devices.
- ItemOpen AccessStochastic modeling and analysis of buffered random access local area networks(1989) Fapojuwo, Abraham Olatunji; Irvine-Halliday, David
- ItemOpen AccessTheories and Experiments of Cognitive Knowledge Bases for Machine Learning(2018-06-26) Zatarain Duran, Omar Ali; Wang, Yingxu; Gavrilova, Marina L.; Fapojuwo, Abraham Olatunji; Chen, Zhangxing; Budin, GerhardThis thesis presents a framework of studies on theories, methodologies, algorithms, and experiments on cognitive knowledge bases (CKBs) for machine knowledge learning in cognitive computing and computational linguistics. CKB is both the results and the means of machine learning methodologies mimicking human learning and semantic comprehensions. Technologies for machine learning can be classified into six categories according to Dr. Y. Wang known as object identification, cluster classification, pattern recognition, functional regression, behavior generation, and knowledge acquisition. Most current machine learning techniques fall into the first five categories. However, the sixth category of knowledge learning as humans do has remained as a fundamental problem and challenge in machine learning, AI, and computational intelligence. A set of algorithms, tools, and experiments on machine knowledge learning is designed in order to demonstrate that cognitive machines may create their own concepts and CKBs through knowledge learning. The accuracy and cohesiveness of machine learnt results may outperform humans. This leads to the implementation of formal knowledge comprehension and quantitative semantic analyses by cognitive systems based on CKBs and machine semantic comprehensions. The theoretical framework and case studies derived from this research will impact the field of machine knowledge learning technologies and the development of novel cognitive systems. This research will enable industrial applications such as personal leaning assistants, cognitive search engines, and cognitive translators.