Lutz Lampe


Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters


Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Cable health monitoring using power line communications signal and machine learning technique (2023)

Automated cable health monitoring is an indispensable constituent of a smart grid (SG). We employ machine learning (ML) techniques to automatically analyze power line communications (PLC) signals to monitor the health condition of electric cables under changing environments and load conditions. PLC provides an attractive solution for both monitoring and control of an SG. With regards to the former, we reuse the wide-band signals transmitted by PLC modems to also diagnose the cable health conditions. This circumvents the requirement of conventional cable diagnostics solutions to install additional dedicated sensors. In this thesis, we first propose an ML framework using supervised learning to analyze a degradation profile automatically. This includes the type, the severity, the dimension and the location of the degradation. We carry out extensive studies with synthetic channels using a water-treeing degradation model for cross-linked polyethylene cables. We explore various ML algorithms, including neural networks and automated machine learning, for our developed supervised learning cable diagnostics schemes.Then, in the absence of a detailed characterization of a particular type of the cable degradation, we design cable anomaly detection schemes using unsupervised dimension reduction and time-series processing techniques. We incorporate laboratory measurement data and in-field collected experimental data into our design.We illustrate the effectiveness of our proposed schemes using both synthetic studies and measurement data.

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Enhancing low-cost cellular IoT networks on the sidelink and satellite links (2023)

Internet-of-Things (IoT) is one of the fast growing technologies in the current era which holds a large and rapidly increasing global market size. Device-to-device (D2D) communication is a key enabler for connecting devices together to form the IoT, especially when the cellular coverage is limited. Similarly, non-terrestrial networks (NTNs) involving satellites which complement the terrestrial cellular networks to provide global coverage also play a vital role in expanding IoT. Both D2D and satellite links are essential in providing seamless universal cellular IoT (C-IoT) coverage. In this thesis, we propose enhancements for C-IoT devices which address up-to-date problems in D2D communication and NTNs. Leveraging the unlicensed frequency bands for D2D communication reduces the costs, and offloads network traffic from the licensed spectral resources. To this end, we design a new low-cost radio access technology (RAT) protocol called Sidelink Communications on Unlicensed BAnds (SCUBA). SCUBA complements the primary RAT, and functions by reusing the existing hardware on a non-overlapping time-sharing basis. We prove the effectiveness of our protocol with analyses and simulation results of the medium access control layer of SCUBA. One of the most critical problems faced by NTN is the uplink (UL) synchronization failure due to high Doppler offset. While NTN new radio (NR) devices rely on global navigation satellite system (GNSS) to resolve this issue, it is not always feasible for power-critical IoT user equipments (UEs). Therefore, we design Synchronization signal-based Positioning in IoT Non-terrestrial networks (SPIN) which enables the IoT UEs to tackle the UL synchronization problem. Our evaluations show that SPIN positioning accuracy achieves the Cramer-Rao lower bound and meets the target accuracy required for UL synchronization, along with significant battery life savings over GNSS-based solution.Another pertinent problem faced by C-IoT devices in NTN is the extended round-trip time resulting in a degraded network throughput. To this end, we propose smarter hybrid automatic repeat request (HARQ) scheduling methods that can increase the efficiency of resource utilization. We conduct end-to-end link-level simulations of C-IoT traffic over NTNs. Our numerical results of data rate show the improvement in performance achieved using our proposed solutions against legacy scheduling methods.

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Stochastic geometry aided user mobility analysis in heterogeneous wireless networks (2022)

Ultra-dense networks (UDNs) envision the massive deployment of heterogeneous base stations (BSs) and the integration of emerging technologies with the existing wireless networks to meet the desired traffic demands. For instance, in indoor environments that hold around 80% of the overall mobile traffic, integration of visible light communication (VLC) with existing radio frequency (RF) networks has emerged as a new network architecture to meet the rapidly growing traffic demand. In outdoor environments,harnessing unmanned aerial vehicles (UAVs) as flying BSs has helped to achieve a cost-effective and on-the-go wireless network that may be used in several scenarios such as to support disaster response and in temporary hotspots. While network densification offers capacity-per-area improvements, the reduced BS coverage footprints and the heterogeneous BS types result in challenges for user mobility such as frequent handovers. User mobility and the resulting handovers in such highly dense networks may in fact nullify the capacity gains foreseen through BS densification. Thus, there exists a need to quantify the effect of user mobility in UDNs. To this end, we conduct a user mobility analysis in emerging network architectures in indoor and outdoor environments (i.e., RF/VLC hybrid networks and UAVs assisted wireless networks) by deriving the user-to-BS association probabilities, handover rates, and sojourn time. The mathematical analysis makes use of stochastic geometry and modeling BSs' locations via a Poisson point process (PPP). First, we validate our mathematical model via Monte-carlo simulations. Then, we utilize it to quantify and reduce the effect of handover rates on the user rate experience. In addition, we exploit machine learning to make cell dwell time aware handover decisions and thus skip unnecessary handovers.

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Application of Behind-the-Meter Energy Storage Systems for Household Load Hiding and Frequency Regulation Service (2020)

The electric utilities industry is undergoing a major paradigm shift, driven by an aging physical infrastructure as well as concerns for carbon emissions. The migration to the digital space with information and communications technology (ICT) as well as the need to integrate more sustainable energy sources have raised new challenges for the legacy power systems. Energy storage systems (ESSs) can help to address the aforementioned challenges and to facilitate the transition to the future smart grid infrastructure. Early deployment of front-of-the-meter utility-scale ESSs have proven to be valuable in providing alternative service options that can benefit the bulk power systems.With the fast declining capital and operating cost, there is a rapid growth in behind-the-meter ESSs adoption. In this thesis, we focus on the application of such ESSs in the low voltage networks and investigate their potential use cases for customers and electric utilities alike. It addresses several specific challenges that exist in the smart grid infrastructure and leverages the unique characteristics of the ESSs to provide solutions for end consumers, the system operator, and storage owners.For end consumers, we design a privacy protection solution at the customer premises based on data obfuscation approach. A household load hiding scheme is developed by exploiting the opportunistic use of the electric vehicles and household appliances to minimize customer’s privacy leakage. For the system operator, we design a frequency regulation scheme based on bi-level optimization that takes into account the ESSs’ operation economics. A decentralized control algorithm is developed to allow the system operator to align with the ESSs on the frequency regulation decisions. For storage owners, we design a market participation strategy to maximize the revenue from providing frequency regulation service. A decision-making framework is developed that allows the storage owners to optimize its operation decisions by anticipating the effect of such decisions on the market clearing outcomes. Our simulation results demonstrate that the applications designed in this thesis by leveraging the behind-the-meter ESSs in the low voltage networks can provide significant benefits for customers and electric utilities alike.

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Coded caching: convex optimization and graph theoretical perspectives (2020)

Data communications is an essential building block of everyday life. A fundamental challenge in communications networks has always been the limited capacity of the links that transfer data from sources to destinations. A core technique to alleviate the load on the network links is to cache popular content in intermediate nodes between the sources and destinations to avoid redundant transmission of the same data. Although the concept of caching has been well studied in the context of computer networks, new settings are emerging in communication networks for which the conventional caching techniques are significantly inefficient and deliver far less than the full benefits that caching can provide. One such setting is that of networks in which caches are connected to the backbone communications system through broadcast links. In the recent years, substantial amount of work has been devoted to characterizing the fundamental limits of the gain of caching in such networks, and coming up with techniques to achieve those limits. At the center of these attempts has been the introduction of coded caching, a technique rooted in information theory that takes advantage of network coding to minimize the amount of information that needs to be communicated in the network.This thesis is devoted to the development of coded caching techniques for three specific settings that are of significant practical interest. In particular, it adapts a convex optimization perspective to address the problem of caching in the presence of duplicate demands, and the problem of designing the optimal way to place the content in caches when different files are non-uniformly popular. The latter is a core problem in the caching literature, for which we derive the optimal placement scheme for certain settings of the problem. We further look into the problem of placement of files in caches without splitting them into sub-packets. We establish a graph theoretical model to study this problem and explore the efficiency of coded caching under this constraint. We believe that this thesis provides fundamental insights into these problems and solutions for them.

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Energy efficient schemes in a mobile sensor network with application in automated monitoring of water quality (2020)

The present thesis addresses the power management issues in a mobile sensor network, with application in automated water quality monitoring. A water quality monitoring platform typically involves a wireless sensor network (WSN), in which a number of mobile sensor nodes (SN) are deployed in the water body to constantly collect the water-related sensory data such as the dissolved oxygen, pH value, temperature, oxidation-reduction potential, and electrical conductivity. This data is used to compute water quality index values, transmit them via some routing schemes, and eventually make them accessible to the water quality professionals, governing agencies, or the public. Power management is nontrivial in the monitoring of a remote environment, especially when long-term monitoring is anticipated. However, constrained by the limited energy supply and internal characteristics of the devices, without proper power management, the devices may become nonfunctional within the networked monitoring system, and as a consequence, the data or events captured during the monitoring process will become inaccurate or non-transmittable. Research is proposed here to develop three distinct approaches for energy conservation in a sensor network, and apply them for automated monitoring of the quality of water in an extensive and remote aquatic body. This thesis analytically develops and applies several energy efficient schemes for power management in the automated spatiotemporal monitoring of the quality of water in an extensive and remote aquatic environment. In general, the schemes for power management of a sensor network can be investigated from a number of aspects and schemes. Those schemes typically range from physical layer optimization to network layer solutions. Meanwhile, depending on the specific applications, some energy efficient methodologies are custom-designed, and thus have limitations when used in other applications. Given this background, three energy efficient methods are proposed in this thesis for conserving energy within a WSN. Those proposed three methods, including DDASA, Hybrid DPS and GCVD, are studied on both the sensor node level and the system level, which energy-efficiently reduce the energy consumption and save extra energy thereby prolonging the life of the WSN. It is expected that the proposed methods will be applicable in other spatiotemporal monitoring applications.

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Faster-than-Nyquist system design for next generation fixed transmission networks (2020)

Monumental growth of traffic load in the communication networks has heavily strained the existing fixed transmission network infrastructure. Such enormous surge of traffic warrants enabling higher data rates in these networks, where predominantly optical fibers and microwave radio links are deployed. With bandwidth becoming an expensive resource, and owing to the practical constraints of the electronic components, employing high baud rates alone may be insufficient to accomplish such high throughputs in these optical fiber communication (OFC) and microwave communication (MWC) systems. Hence, increasing the spectral efficiency (SE) is a key requirement for these networks. For this pursuit, this thesis investigates the application of Faster-than-Nyquist (FTN) signaling in fixed transmission networks, with an objective to achieve high SE and data rates. FTN is an enabling technology that offers SE improvements by allowing controlled overlap of the transmitted symbols in time or frequency or both. OFC and MWC systems are suitable platforms for the introduction of FTN signaling, since FTN can moderate the need for higher order modulation formats, which are sensitive to phase noise and fiber nonlinearity. In this thesis, we combine the concept of FTN signaling with other conventional throughput increasing techniques, such as polarization multiplexing and multicarrier transmission, to further the data rate improvements.However, FTN introduces inter-symbol-interference and/or inter-carrier interference. Moreover, integrating FTN signaling with polarization multiplexing and multicarrier transmission complicates the realistic implementation. OFC and MWC systems also pose additional practical challenges stemming from the specific communication channel environments and the transceiver components. If not successfully mitigated, all of these impairments and non-idealities significantly deteriorate the performance of the communication links.In this thesis, we address each of these unique challenges through suitable mitigation algorithms, to facilitate an efficient FTN transmission. For this, we present sophisticated system designs equipped with powerful digital signal processing tools. We numerically evaluate the performance of our proposed methods by simulating realistic OFC and MWC systems. The simulation results indicate that our proposed spectrally efficient designs offer significant performance advantages over existing competitive schemes.

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In-band full-duplex power line communications (2019)

Power line communication (PLC) exploits the existing power-grid infrastructure for signal transmission at various avenues from large-scale transmission and distribution networks to smaller-scale in-home and in-vehicle electrical wiring frameworks. PLC provides gigabit-range throughput, which also makes it a viable solution for multimedia communication in indoor local area networks. However, the low-pass nature of power line channels and the strict electromagnetic compatibility regulations governing PLC hinder adequate data rate gains that can be further achieved by traditional means of increasing transmit power and/or bandwidth. One solution to improve data rate under such restrictions is to use in-band full-duplexing (IBFD), which doubles the spectral efficiency for a given channel quality by enabling simultaneous bidirectional data communication in the same frequency band. With the backdrop of existing IBFD implementation in various communication systems from analog telephones to more recent proposals for wireless communications, we investigate the requirements for an IBFD PLC system and propose solutions that counter the unique challenges encountered in the harsh power line environment. Simulation results show that our low-cost and low-complexity design achieves over 80% increase in median bidirectional data rates under typical in-home power line networking conditions without any additional power or bandwidth requirement.Aside from improving spectral efficiency, IBFD allows us to solve several electromagnetic compatibility issues observed at PLC deployments. By using IBFD, PLC transceivers can simultaneously transmit data packets while also sensing the operating spectrum. We use this spectrum aware transmission ability of IBFD-enabled PLC modems to propose cognitive coexistence techniques to eliminate or reduce the impact of the electromagnetic interference caused by unintentional PLC radiation on broadcast radio services, digital subscriber line communications, and neighboring PLC systems in a heterogeneous PLC environment. Along with obtaining physical layer advancements, we further apply IBFD to achieve medium access control (MAC) layer enhancements to improve its efficiency, which is known to deteriorate under heavily loaded network conditions. We propose an IBFD priority resolution procedure and a combined frequency domain contention resolution and preamble collision detection technique that improve the MAC efficiency by reducing the time spent in resolving priorities and contentions by up to 85%.

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Analysis and optimization of communication technologies for smart grid applications (2018)

Several communication technologies including IEEE 802.15.4g, world-wide interoperability for microwave access (WiMAX), and power line communication (PLC) have been suggested for smart grid implementation. As the successful arrival of smart grid traffic within their latency requirement is essential for the correct operation of the power grid, we focus on the optimization of different features of these communication technologies and also the development of aspects of an efficient network architecture such that the reliability requirement associated with smart grid traffic can best be assured.We first investigate an optimized configuration of WiMAX features, in particular, the choice of frame duration, type-of-service to traffic mapping and uplink and downlink allocations, under what we call the “profile configuration”. We also devise inter-class and intra-class scheduling solutions in order to prioritize time-critical traffic within both base station and customer premises equipments. We then evaluate the performance of the developed WiMAX profile configuration and scheduling scheme through our newly developed WiGrid (WiMAX for Smart Grid) module. From the performed simulations, we conclude that the proposed configurations for the WiMAX features can ensure the satisfaction of the reliability requirement.Next, we design advanced metering infrastructures (AMIs) based on the characteristics of the PLC and the IEEE 802.15.4g technologies. We use intermediary data collectors, known as data acquisition points (DAPs), in order to efficiently collect traffic from smart meters and forward them to the utility control center. We formulate an optimization platform for efficiently placing DAPs on top of the existing utility poles or transformers, in such a way that the required reliability for smart grid traffic is ensured and also the installation cost is minimized. In order to address the QoS requirements, we derive the latency based on the characteristics of the medium access control schemes of each of these technologies. Since finding the optimal DAP locations is an integer programming problem and NP-hard, we develop several heuristic algorithms for efficiently placing DAPs within large-scale scenarios. We observe that the DAP placement algorithms, proposed here for large-scale scenarios, return near-optimal results within a much shorter time, than that of the IBM CPLEX software for small scenarios.

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Interference mitigation and alignment for interference-limited communication systems (2018)

With limited availability of the communication spectrum and ever-increasing demandsfor high-data-rate services, it is natural to reuse the same time-frequency resourceto the greatest degree possible. Depending on the nature of transmission andreception of the users, this leads to different instances of interference, e.g., inter-userinterference in an interference network and self-interference in a Full-Duplex (FD)transmission. With a goal to mitigate such interference, in this thesis we investigateemerging interference-limited communication systems, such as FD, Device-to-Device(D2D), and Power Line Communication (PLC). To this end, we propose advancedsolutions, namely self-interference mitigation and Interference Alignment (IA).With an objective to reduce the power consumption, we study transceiver designfor FD multi-cell Multi-Input Multi-Output (MIMO) systems with guaranteed Qualityof Service (QoS). Considering realistic self-interference models and robustnessagainst Channel State Information (CSI) uncertainty, our numerical results revealtransmission scenarios and design parameters for which replacing half-duplex withFD systems is beneficial in terms of power minimization. If the system is not power constrained,however, a natural objective is to optimize the total throughput givena power budget. Nonetheless, throughput maximization underserves the users thatexperience poor channels, which leads to QoS unfairness. Therefore, we propose afair transceiver design for FD multi-cell MIMO systems, which can be implementedin a distributed manner. We further extend our design to enforce robustness against CSI uncertainty. As a second contribution within this design theme, the concept ofrobust fair transceiver design is also extended for D2D communications, where unlikethe self-interference in FD transmission, the users suffer from strong inter-userinterference. Recognizing that simultaneous multiple connections in PLC contribute to (interuser)interference-limited communication, we introduce IA techniques for PLC networks,for which the results confirm a significant sum-rate improvement. To overcomethe implementation burden of CSI availability for IA techniques, we then study BlindInterference Alignment (BIA) for PLC X-network, and show that the characteristicsof the PLC channel thwart simple implementation of this technique via impedancemodulation. We therefore resort to a transmission scheme with multiple receivingports, which can achieve the maximum multiplexing gain for this network.

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Coordinated transmission for visible light communication systems (2017)

Visible light communication (VLC) is an emerging optical wireless communication technology that employs the light-emitting diode (LED) as the data transmitter. It has great potential to alleviate the strain on the radio-frequency (RF) spectrum in the indoor environment. The integration of VLC into indoor communication networks establishes optical attocells, responsible for the downlink traffic from the network to user terminals. These attocells could be easily deployed wherever LEDs are adopted for general illumination, including in electromagnetic interference sensitive areas like hospitals and airplanes. Although opaque bounds effectively contain light signals, VLC attocells would generally not operate free of interference. Illumination designers aim to have a uniform illumination at a certain height in the indoor environment, which mandates a rich overlap between the emissions of luminaires and results in unavoidable inter-attocell interference (IAI) from a communications perspective. This reality encourages us to propose the coordination of multiple VLC attocells (i.e., VLC-enabled LED luminaires) to turn the problem of overlap and thus interference into an advantage. In this thesis, we study how the coordination of VLC attocells can be employed to improve the user performance. Two coordinated VLC architectures, both of which utilize single-carrier transmission but differ at the coordination level, are investigated first. The analysis primarily focuses on the beamforming design subjected to the limited dynamic range of LED transmitters. The design of robust beamformers is also considered to combat the uncertainty of channel information at the transmitter. Finally, we propose a multi-carrier coordinated VLC architecture that uses power lines as the backbone network for the VLC front-end. Several subcarrier allocation schemes with varying degrees of tradeoff among hardware, computational complexity, and performance for meaningful variations of this hybrid system are proposed. The system designs developed throughout the thesis enable the collaboration among multiple LED transmitters in VLC systems, and our results indicate that these collaborative designs can significantly improve the performance of indoor VLC systems.

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Enhancements to the long term evolution (LTE) standard for facilitating the Internet of things (IoT) (2017)

The world is evolving towards an Internet of Things (IoT) where a large number of devices interact to realize different applications that constitute smart electricity grids, intelligent transportation systems, ubiquitous healthcare solutions, etc. Machine Type Communications (MTC) provide the substrate for the connectivity and service mechanisms of these devices. Many services associated with the MTC applications such as smart metering and location tracking require the cellular network as the backbone for communication and are instrumental in driving the growth of the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) / LTE-Advanced (LTE-A) standards. A substantial number of MTC User Equipment (UE) hosting IoT applications are expected to be low cost, low data rate devices requiring prolonged battery life. In the downlink, the current LTE/LTE-A standards adopt Discontinuous Reception (DRX) mechanism for power reduction, which requires the UE to wake up periodically to check for a paging message from the base station. The LTE/LTEA standardization activities have identified that intricate paging decode procedures increase the energy consumption for low complexity MTC UEs, necessitating enhancements to the current mechanisms. This encourages us to investigate novel energy efficient mechanisms for LTE MTC systems. Specifically, we develop DRX with Quick Sleeping Indication (QSI), which enables the MTC UEs to go back to sleep quickly and save power, when there is no impending page from the base station. We also design the enhanced Primary Synchronization Signal (ePSS) for faster timing resynchronization, which can be used as QSI for additional improvements in the downlink energy efficiency of MTC UEs in low coverage. Further, LTE/LTE-A standardization activities in the uplink are examining different procedures to reduce UE data retransmissions for improved energy efficiency. To this end, we develop a Maximum Likelihood (ML) based uplink Carrier Frequency Offset (CFO) estimation technique for the LTE/LTE-A base station, which is robust and accurate in low coverage, enhancing the uplink energy efficiency of MTC UEs. The MTC mechanisms described in this thesis are not only simple to implement, but also require minimal changes to the present LTE/LTE-A standardization framework, promoting smooth integration into the current LTE/LTE-A networks.

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Physical-layer security for visible-light communication systems (2017)

Visible-light communication (VLC) is an enabling technology that exploits the lighting infrastructure to provide ubiquitous indoor broadband coverage via high-speed short-range wireless communication links. On the other hand, physical-layer security has the potential to supplement conventional encryption methods with an additional secrecy measure that is provably unbreakable regardless of the computational power of the eavesdropper.The lack of wave-guiding transmission media in VLC channels makes the communication link inherently susceptible to eavesdropping by unauthorized users existing in areas illuminated by the data transmitters. In this thesis, we study transmission techniques that enhance the secrecy of VLC links within the framework of physical-layer security.Due to linearity limitations of typical light-emitting diodes (LEDs), the VLC channel is more accurately modelled with amplitude constraints on the channel input, rather than the conventional average power constraint. Such amplitude constraints render the prevalent Gaussian input distribution infeasible for VLC channels, making it difficult to obtain closed-form secrecy capacity expressions. Thus, we begin with deriving lower bounds on the secrecy capacity of the Gaussian wiretap channel subject to amplitude constraints.We then consider the design of optimal beamformers for secrecy rate maximization in the multiple-input single-output (MISO) wiretap channel under amplitude constraints. We show that the design problem is nonconvex and difficult to solve, however it can be recast as a solvable quasiconvex line search problem. We also consider the design of robust beamformers for worst-case secrecy rate maximization when channel uncertainty is taken into account.Finally, we study the design of linear precoders for the two-user MISO broadcast channel with confidential messages. We consider not only amplitude constraints, but also total and per-antenna average power constraints. We formulate the design problem as a nonconvex weighted secrecy sum rate maximization problem, and provide an efficient search algorithm to obtain a solution for such a nonconvex problem. We extend our approach to handle uncertainty in channel information.The design techniques developed throughout the thesis provide valuable tools for tackling real-world problems in which channel uncertainty is almost always inevitable and amplitude constraints are often necessary to accurately model hardware limitations.

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Optimization in Wireless Sensor and Machine-Type Communication Networks (2016)

Wireless sensor networks (WSNs) are systems used for detecting events and gathering information from an area of interest in many different application domains, from home and industry automation, to healthcare and transportation, to environmental monitoring. With regard to the communication task involved in WSNs, they can also be seen as an instance of the new paradigm, known as machine-type communication (MTC). Similar to traditional wireless sensors, MTC-enabled devices can communicate together without direct human interference.Energy efficiency for the sake of longevity is perhaps the most challenging requirement for many WSNs and MTC networks. In this thesis, we consider ultra-wideband (UWB) transmission technology for energy-efficient communication in WSNs. UWB achieves frugal use of energy by transmitting with low spectral efficiency when compared to legacy wireless technologies. This also allows it to operate license-exempt in many jurisdictions around the world. More recently, however, wireless service operators consider the use of cellular technology also for low data-rate applications originally only served by WSN-type technology. In particular, long-term evolution (LTE) technology has moved into the focus for joint personal-communication and MTC networks. Recent releases of the LTE standard and ongoing work items in LTE standardization specifically accommodate low-cost and low-power MTC.This thesis presents contributions that improve the performance of UWB WSN and LTE MTC networks in several aspects, namely lifetime, localization accuracy, and coverage. A common theme of these different contributions are the use of optimization methods for obtaining scalable, robust, and/or low-complexity solutions.We first address the lifetime maximization problem in a UWB-based WSN designed for multiple event detection. The key contribution is the joint optimization of transmission and routing parameters of sensor nodes so that the energy consumption is distributed as evenly as possible among the entire WSN. We then investigate the challenges of localization in WSNs and provide a convex solution which is robust to measurement uncertainties. In the last part of this thesis we focus on providing coverage for low-cost LTE MTC networks, where the challenge is to develop efficient transmission strategies that maximize the coverage of MTC devices in an LTE cell.

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Spectrum sensing for cognitive radios: improving robustness to impulsive noise (2016)

Many different types of promising spectrum sensing algorithms for Cognitive Radio (CR) have already been developed. However, many of these algorithms lack robustness with respect to signal statistical parameters uncertainties, such as the noise variance or the shape of its distribution (often assumed to be simply Gaussian). In conjunction with the low Signal-to-Noise Ratio (SNR) requirements, this lack of robustness can often render interesting sensing algorithms impractical for real-life applications. In this thesis, we primarily focus on the impact of heavy-tail noise distributions on different CR detectors and the use of signal limiters (mostly the spatial sign function) to improve their robustness to such noise distributions. Introducing a non-linear transformation of the received signal prior to its processing by the detector fundamentally changes the signal distribution which in turn modifies the distribution of the detector statistic. In order to parametrize the detector and study its performance, it is then necessary to know the shape of the modified distribution. Three types of detectors are investigated: a generic second-order cyclic-feature detector, a Scaled-Largest Eigenvalue (SLE) detector studied in the context of stationary time-series and a new Sequential Likelihood Ratio Test (SLRT) detector. The analysis conducted for each detector revolves around the influence of its parameters, the distribution of the detector statistic and several comparisons with similar detectors for various detection scenarios. Our results indicate that at the cost of a moderate performance loss in a Gaussian noise environment, all the detectors fitted with a signal limiter become robust to impulsive noise and noise parameters uncertainties. We provide analytical approximations for the detectorsstatistical distribution that allow us to use the detectors in such configurations as well as to study their performance for different signal limiters and noise distributions.

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Novel Protocols for the Two-Hop Half-Duplex Relay Network (2015)

Wireless communication has enabled people to be connected from anywhere and at any time. This has had a profound impact on human society. Currently, wireless communication is performed using the communication protocols developed for cellular and wireless local area networks. Although these protocols support a broad range of mobile services, they do not fully exploit the capacity of the underlying networks and cannot satisfy the exponential growth in demand for higher data rates and more reliable connections. Therefore, new communication protocols have to be developed for general wireless networks in order to meet this demand. Ultimately, these protocols will have to be able to reach the fundamental limits of information flow in wireless networks, i.e., the network capacity. However, due to the complexity of the problem, it is currently not known how to design such protocols for general wireless networks. Therefore, in order to get insight into this problem, as a first step, communication protocols for very simple wireless networks have to be devised. Later, the gained knowledge can be exploited to design protocols for more complex networks. In this thesis, we propose new communication protocols for the simplest half-duplex relay network, which is also the most basic building block of any wireless network, the two-hop half-duplex relay network. This network is comprised of a source, a half-duplex relay, and a destination where a direct source-destination link is not available. For the considered relay network, we propose three novel communication protocols. The first proposed protocol achieves the capacity of the considered network when fading on the source-relay and relay-destination links is not present. The second and third protocols significantly improve the average data rate and the outage probability, respectively, of the considered network when both the source-relay and the relay-destination links are affected by fading.

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Power line communications in vehicles: Channel measurements and impedance matching networks (2015)

In today's Electric Vehicles (EVs) and conventional Combustion Engine Vehicles(CEVs), data communication between electronic control units is accomplished by sending communication signals over dedicated wires. The space requirement, weight, and installation costs for these wires can become significant, especially in electric vehicles of the future, which are highly sophisticated electronic systems. This has motivated research and development activities in the area of Vehicular Power Line Communication (VPLC). VPLC systems reuse power wires inside a vehicle for data communicationpurposes. Thus, they eliminate the need for extra wires dedicated to communication.However, there are several impediments to overcome in order to achieve a reliable and robust VPLC. Many of these challenges originate from inherentproperties of current wirings in vehicles, which are not designed with communication in mind. Therefore, to develop suitable data transmissionequipments, a good understanding of the communication channel characteristics is essential. Considering the importance of proper characterization as a first step towards the design and deployment of VPLC systems, in this work, we have tried to contribute to the available body of knowledge on channel characterization for VPLC in EVs and CEVs. As tangible contributions, we present methodology and results of two measurement campaigns in this thesis. The main outcomes of this part of our research are quantitative statements about Channel Transfer Functions and Access Impedance for two vehicles and discussions of our results in the context of VPLC system design.Building on the results of these measurements, an adaptive impedance matching system is designed to improve the power transmission between VPLC devices and the vehicular power line network, and consequently improve the Signal-to-Noise Ratio (SNR) of the communication system. The adaptive impedance matching system is first behaviorally described in VHDL-AMS and simulated using Cadence™ and then for each unit a circuit design compatible for implementation on an Integrated Circuit (IC) platform is suggested.Tested against the challenges of VPLC observed in our measurement campaigns, the proposed system proved to be capable of significantly improving the reliability of communication over power wires in vehicle.

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Multiuser pre-filtered ultra-wideband systems (2014)

Ultra-wideband (UWB) communication enables license-exempt transmission with very low power over large bandwidths. The technology can provide very high data rates over short transmission ranges to support applications such as real-time data streaming, interchip communication and wireless memory. The work in this thesis considers a particular type of high data-rate multiuser direct-sequence UWB (DS-UWB) with popular and commonly used binary UWB signalling. The system consists of multiple low-complexity DS-UWB transceivers (nodes) and a central unit that is more powerful in terms of signal processing capabilities. We mostly focus on the transmission from the central unit to the nodes.We address the following main questions: (1) What signal processing should be applied at the central unit to enable simple yet reliable detection at low-complexity nodes? (2) How can the system performance be optimized in the presence of imperfect channel estimation? (3) Is it possible to improve the system performance by incorporating the binary detector structure in the transmitter design? (4) How can the performance of a network of multiple UWB nodes communicating through a central relay be optimized?For question (1), we propose to shift the signal processing load from the nodes to the central unit via pre-filtering (the combination of pre-rake and pre-equalization) of the transmit signal at the central node, and we provide filter design strategies for the downlink communication. Questions (2) is addressed by studying the impact of errors in estimation of the channel impulse response at the central unit. Two mathematical models are proposed to represent the channel estimation error and robust strategies are formulated for the design of downlink pre-equalization filters (PEFs). For the popular binary UWB signalling, the real-part of the received signal contains sufficient statistics for signal detection. Hence the widely linear design of PEFs is proposed to answer question (3).As for question (4), we extend our design methods to multi-way internode communication via a central relay. Two relaying strategies namely, detect-and-forward relaying and filter-and-forward relaying with partial and full self-interference cancellation are devised.

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Resource allocation in wireless systems with conventional and energy harvesting nodes (2014)

High data rate, reliable communication, and low power consumption are theforemost demands for next generation of wireless communication systems. The key challenge to the design of communication systems is to combat the detrimental effects of channel fading, noise, and high power consumption. Wireless systems are often impaired by non-Gaussian noise, and the performance of systems designed for Gaussian noise can degrade if non-Gaussian noises are present but are not taken into account. Thus, it is imperative to analyze systems that are impaired by non-Gaussian noise and to manage their resources better to improve overall performance. Furthermore, there is significant interest in using renewable energy for wireless systems. However, energy harvesting (EH) is a random process and the harvested energy should be expended judiciously to maximize aggregate system throughput. In this thesis, we consider wireless systems that are impaired by Gaussian and non-Gaussian noise and powered by conventional energy sources and energy harvesters and propose appropriate resource allocation schemes for these systems. First, we propose optimal and fair power allocation schemes for a cooperativerelay network with amplify-and-forward relays that employs best and partialrelay selections and is impaired by Gaussian and non-Gaussian noise. Wederive closed-form expressions of asymptotic bit error rate and use thisexpression to allocate transmit powers for different nodes with necessaryenergy consumption constraints. Second, we consider a network comprising a source, a relay, and a destination, where the source and the relay are EH nodes. We consider conventional and buffer-aided link adaptive relaying protocols, and propose offline and online resource allocation schemes that maximize the system throughput.Thirdly, we consider a multi-relay network with EH nodes and propose offlineand online joint relay selection and power allocation schemes that maximizethe system throughput. Fourth, we consider a single source-destination link, where the source hasa hybrid energy supply comprised of constant energy source and energy harvester. We propose offline and online power allocation schemes that minimize the energy consumption from the constant energy source and thereby utilize the harvested energy effectively.

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Spatial reuse scheduling and localization for underwater acoustic communication networks (2013)

Ocean exploration, through the development of ocean-observation systems, is a key step towards a fuller understanding of life on Earth. Underwater acoustic communication networks (UWANs) will help to fulfill the needs of these ocean-observation systems, whose applications include gathering of scientific data, early warning systems, ecosystem monitoring and military surveillance. The data derived from UWANs is typically interpreted with reference to the location of a data collecting node, e.g. when reporting an event occurrence, or the location of an object itself is of interest, e.g. when tracking a moving underwater vehicle or diver. In this dissertation, we develop methods for localization and efficient data exchange in UWANs.In the first part of this work, we focus on underwater localization (UWL). Since global positioning system signals do not propagate through water, UWL is often based on fusing information from acceleration-based sensors and ranging information to anchor nodes with known locations. We consider practical challenges of UWL. The propagation speed varies with depth and location, anchor and unlocalized nodes are not time-synchronized, nodes are moving due to ocean currents, propagation delay measurements for ranging of non-line-of-sight communication links are mistakenly identified as line-of-sight, and unpredictable changes in the ocean current makes it hard to determine motion models for tracking. Taking these features of UWL into account, we propose localization and tracking schemes that exploit the spatially correlated ocean current, nodes' constant motion, and the periodicity of ocean waves.In the second part of this thesis, we use location information to develop medium access control scheduling algorithms and channel coding schemes. We focus on adaptive scheduling in which each node transmits based on timely network information. Specifically, our scheduling algorithms utilize the long propagation delay in the channel and the sparsity of the network topology to improve throughput, reliability and robustness to topology changes.To evaluate performance, we have developed a simulator combining existing numerical models of ocean current and of power attenuation in the ocean. We have also verified simulation results in four sea experiments of different channel bathymetry structures, using both industry and self-developed underwater acoustic modems.

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Analysis and design of OFDM systems: loading and nonlinear distortion mitigation (2012)

In wireless communication systems, multiples copies of the transmitted signal arrive at the receiver. This phenomenon causes channel variations in the frequency domain over the transmission bandwidth. Conventional systems use equalizers to tackle this problem. Another approach is to use multicarrier communication systems based on orthogonal frequency division multiplexing (OFDM). In this technique, the entire bandwidth is divided into several subchannels, and, as a result, each subchannel experiences almost a flat fading channel. In this dissertation, we work on three different areas in OFDM systems: (1) We propose new analytical methods to evaluate the error rate of coded OFDM systems for a particular channel realization. (2) Assuming the instantaneous channel is known both at the transmitter and the receiver, we introduce adaptive transmission techniques to enhance the performance of these systems, and (3) we propose a new receiver-based technique to recover the distortion caused by the practical non-linear power amplifier. To address the first subject, a novel analytical method for bit error rate evaluation of coded OFDM systems for a specific channel realization is proposed. As this method might be too complex for some applications, we also propose a simpler formula. As for the second subject, we introduce new adaptive bit loading and interleaving techniques to minimize the bit error rate of the system. Also, we propose novel adaptive bit and power loading and code rate selection techniques to minimize bit error rate, to minimize the transmit power, or to maximize the throughput of the system. To address the third subject, we propose a new method to estimate the original nonclipped signal. The estimation is done at the receiver and makes use of the newly proposed compressed sensing estimation technique.

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Binary-Coded Modulation and Applications in Tree-Space Optics (2011)

Binary-coded modulation techniques such as bit-interleaved coded modulation (BICM) and multilevel coding (MLC) are pragmatic methods to achieve both high power and bandwidth efficiencies in digital communications. These techniques enable the combination of powerful and popular off-the-shelf binary codes with bandwidth-efficient multilevel signaling without considerable performance loss compared to joint coding and modulation designs. Today, binary-coded modulation has become almost universal in digital communication systems. This thesis concerns several aspects of binary-coded modulation and its applications. For BICM, we study the use of approximate decoding metrics to reduce detection complexity. Specifically, we propose metric correction functions which can improve achievable rates. We further propose a metric scaling which can improve throughput performance of symbol-by-symbol (SBS) decoding, which is the basis of state-of-the-art error-control coding systems. To this end, we also discover an intriguing relationship between the generalized Gallager function and the performance of sum-product SBS decoding. For MLC, we develop the concept of reduced-layer coding that facilitates a trade-off between performance and structural complexity. We then propose a novel rateless MLC scheme which can seamlessly adapt to the instantaneous channel quality and achieve throughput gains compared to BICM in a number of transmission scenarios. Finally, we apply binary-coded modulation techniques to free-space optics (FSO). FSO is an interesting transmission technology which has recently emerged as a low-cost solution to a range of communication challenges. We consider advanced signaling schemes such as channel coding diversity with mismatched decoding metrics and multipulse pulse-position modulation (MPPM). Combined with binary codes, these schemes are practical means to improve rate and reliability in FSO systems.

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Reliable communication in non-Gaussian environments: receiver design and analytical aspects (2011)

With the plethora of devices that operate in current communication networks, there is a non-zero probability that radio frequency signals from disparate sources may interfere with each other and therefore one has to contend with unwanted signals that corrupt the desired signal. The unwanted part, collectively referred to as noise, may be attributed to a number of factors ranging from device irregularities to varied ambient phenomena. Traditionally by applying the central limit theorem, noise in communication systems has been characterized by a Gaussian distribution. However, it has been recognized time and again, that in plenty of cases this is an abstraction of the real characteristics of the noise since for a variety of reasons the central limit theorem may not hold true for the observed noise. Such noise is generally referred to as being non-Gaussian. The general belief about non-Gaussian noise is that it deteriorates signal fidelity, resulting in unreliable communication. However, the loss in reliability is due to the fact that almost all communication systems are designed to well handle Gaussian noise and hence suffers loss when this assumption is not true.We characterize the performance of coded and uncoded communication systems in non-Gaussian noise. More specifically we consider robust decoding techniques when the noise is impulsive and is correlated. We incorporate the effect of non-ideal interleaving on system performance when the noise has memory and provide several design recommendations for such environments. We also propose techniques to acquire information on the statistics of the noise when it can be modeled as a Markovian-Gaussian process and analyse the performance of such estimators. These techniques are then applied to contemporary technologies such as cognitive transmission and impulse radio ultra wideband transmission, as a proof of concept, and to quantify the benefits that exist in accurately characterizing the interference in such systems. Furthermore, we use spatial diversity in mitigating the effects of non-Gaussian noise through a distributed multi-antenna approach. Better known as cooperative diversity, this approach is shown to require careful design when the facilitating nodes are affected by strong interference and we provide novel algorithms for the same.

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Resource allocation and optimization for multiple-user legacy and cognitive radio systems (2010)

The rapid transition towards user mobility and the increased demand it carries for bandwidth and data rates has been the driver for significant advancements in research and development in wireless communications in the last decade. These advancements materialized through enhancements to the well established legacy systems and conceptual innovations with great potential. Not far from that, in this thesis, we consider a diverse set of tools and techniques that facilitate efficient utilization of system resources in legacy and Cognitive Radio (CR) systems without hindering the integrity and robustness of the system design.First, we introduce the concept of service differentiation at the receiver, which can be realized by means of a new multiple-user Multiple-Input Multiple-Output (MIMO) detector based on the well known V-BLAST algorithm. We devise the DiffSIC algorithm that is able to differentiate between users in service based on their priorities or imminent needs. DiffSIC achieves its goal by determining the optimal order of detection, at the receiver, that best fits the users' profiles.Second, we propose a channel allocation technique for the transmitter of MIMO multiple-user access systems which enhances the system capacity without aggravating the complexity of the receiver. In particular, we allow users to share resources to take full advantage of the degrees of freedom available in the system. Moreover, we show how to realize these enhancements using simple, yet powerful, modulation and detection techniques.Next, we propose new robust system designs for MIMO CR systems under the inevitable reality of imperfect channel state information at the CR transmitter. We apply innovative tools from optimization theory to efficiently and effectively solve design problems that involve multiple secondary users operating over multiple frequency carriers.At last, we observe the effect of primary users' activity on the stability of, and quality of service provided by, CR systems sharing the same frequency resource with the primary system. We propose admission control mechanisms to limit the effect of primary users' activity on the frequency of system outages at the CR system. We also devise pragmatic eviction control measures to overcome periods of system infeasibility with a minimally disruptive approach.

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Space-time continuous phase modulation (2009)

The combination of Space-Time (ST) coding and Continuous-Phase Modulation (CPM) produces a low power, energy efficient communication scheme suitable for wireless transmission. Space-time coding increases the reliability of transmission, and continuous-phase modulation (CPM) has thepotential to provide considerable energy savings. CPM is a modulation technique that involves the transmission of a signal with continuous-phase and a constant envelope, where the continuous-phase property produces a very bandwidth efficient signal, and the constant-envelope property enables nonlinear (and thus energy efficient) signal amplification. The ST-CPM code is of special interest for wireless sensors because in the wireless sensor network environment energy consumption is highly constrained. The combination of ST codes and CPM is non-trivial and thus ST-CPM codes based uponblock-based orthogonal and diagonal signal matrices are presented. These codes are forms the basis of a distributed ST-CPM code. The distributed ST codes are designed to operate in wireless networks containing a large set of nodes, of which only a small a priori unknown subset will be active at anytime. The devised distributed ST-CPM scheme combines the ST-CPM code with a diagonal signaling matrix, (commonly assigned to all relay nodes) with signature vectors(uniquely assigned to nodes). The energy consumption of the proposed distributed ST-CPM scheme is compared with that of a distributed ST linear modulation (LM) scheme. The distributed ST-CPM scheme is shown tooutperform the distributed ST-LM scheme for all but short-range transmission. Finally, a serially concatenated code for ST-CPM is proposed. The concatenated code consists of the diagonal signalling matrix as the inner code, and a class of double parity check (DPC) codes as the outer code. Theresulting concatenated codes that are formed from the ST-CPM code and a DPC code are shown to provide performance close to capacity, and to provide performance superior to that provided by the more common combination of CPM, or ST-CPM schemes with convolutional codes.

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Towards vaulting the hurdle of short lifetimes in wireless sensor networks : distributed algorithms and UWB impulse radio (2009)

Wireless Sensor Networks (WSNs) offer a compelling solution for distributed sensing problems because they can be deployed rapidly and inexpensively, and are robust to failures. However, since they operate on batteries, they tend to have short lifetimes. We present several algorithmic techniques for reducing the power consumption of such networks, based on Algorithmic Data Reduction (ADR) and low-power Ultra-Wide-Band Impulse Radio (UWB-IR). In the ADR approach, we minimize power-hungry communication out of the network via distributed in-situ broadcast `message-passing' algorithms for filtering, compression and model identification. These algorithms are scalable, power-efficient, stable, and computationally tractable. At the same time their performance is close to the respective ultimate theoretical limits. Specifically, the filter performs close to an optimal Bayesian recursion, the compressor approaches the rate-distortion and channel-capacity bound, and the identification scheme is asymptotically efficient in the Cramer-Rao sense.The UWB-IR approach exploits a well-known tradeoff predicted by Shannon theory, namely that one can maintain reliable communication at a given data rate at a reduced transmit power provided the transmission bandwidth is requisitely increased. We propose a novel UWB-IR receiver, which is eminently suited to the bursty mode of operation of the WSN physical layer. The receiver is based on the principle of Compressed Sensing and offers a practical alternative to costly high-rate analog-to-digital conversion. It can tolerate strong inter-symbol interference and can therefore operate at high pulsing rates, which allows us to fully leverage the power-vs-bandwidth tradeoff. It is impervious to poor timing synchronization, which means that the transmitter can avoid sending training headers, thus further saving a significant amount of power. In addition, it is also robust to strong narrow-band interference from licensed systems like WiMAX. With a synergy of the ADR and UWB-IR techniques, the communication related power consumption of the WSN can be reduced by about 30 dB or more in practical scenarios, which substantially alleviates the handicap of limited lifetimes. We study a practical application of these techniques in the problem of target tracking by interpreting the received signal strength of transmissions from RFID tags.

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Multiband orthogonal frequency division multiplexing for ultra-wideband wireless communication: analysis, extensions and implementation aspects (2008)

Ultra-Wideband (UWB) wireless communication systems employ large bandwidths and low transmitted power spectral densities, and are suitable for operation as underlay systems which reuse allocated spectrum. The subject of this dissertation is Multiband Orthogonal Frequency Division Multiplexing (MB-OFDM) UWB for high data-rate communication. We address four main questions: (1) What are the theoretical performance limits and practical system performance of MB-OFDM? (2) What extensions can be used to increase the system power efficiency and range? (3) Is it possible to estimate the system error rate without resorting to time-consuming simulations? and (4) What is the effect of interference from narrowband systems on MB-OFDM, and can this interference be mitigated?As for questions 1 and 2, we investigate the MB-OFDM performance, and propose system enhancements consisting of advanced error correcting codes and OFDM bit-loading. Our methodology includes the development of information-theoretic performance measures and the comparison of these measures with performance results for MB-OFDM and our proposed extensions, which improve the power efficiency by over 6 dB at a data rate of 480 Mbps.To address question 3, we develop novel analytical methods for bit error rate (BER) estimation for a general class of coded multicarrier systems (of which MB-OFDM is one example) operating over quasi-static fading channels. One method calculates system performance for each channel realization. The other method assumes Rayleigh distributed subcarrier channel gains, and leads directly to the average BER. Both methods are also able to account for sum-of-tones narrowband interference.As for question 4, we first present an exact analysis of the uncoded BER of MB-OFDM in the presence of interference from incumbent systems such as IEEE 802.16 ("WiMAX"). We also present a Gaussian approximation for WiMAX interference, and establish its accuracy through comparison with exact analysis and simulations. We then propose a two-stage interference mitigation technique for coded MB-OFDM, consisting of interference estimation during silent periods, followed by metric weighting during decoding, which provides substantial gains in performance in return for modest increases in complexity, and without requiring any modifications to the MB-OFDM transmitter.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Fully connected optical beamforming network using microring weight banks (2023)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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Hardware implementation of a multicarrier faster-than-Nyquist decoder (2023)

Efficient utilization of time and frequency resources in communication networksis crucial to meet the demands of the fast growing global internet traffic. Theconcept of packing data more densely in time and frequency via so-called Fasterthan-Nyquist (FTN) transmission, originally proposed in the 1970’s, has thereforeregained popularity. This faster, more bandwidth-efficient transmission of dataresults in interference between symbols adjacent in time and frequency, which mustbe mitigated in the receiver. Hence, computational complexity of FTN receivers isgenerally higher than for non-FTN receivers and constitutes a bottleneck for practicaladoption.Although there have been significant advances in algorithm design for reducingthe computational complexity of state-of-the-art FTN decoders, research on efficienthardware acceleration has only been sporadic. In particular, hardware implementationsof different components that constitute the FTN decoder have been investigatedin isolation. As a result, the only hardware FTN decoder proposed to date utilizesthe hardware resources extremely inefficiently (its hardware utilization is on theorder of 54.1%).In this thesis, we offer algorithmic and architectural insights for efficient implementationof complete FTN decoder systems. We identify convergence propertiesand parallelism opportunities unique to how the computational blocks operate andcommunicate with each other when combined in an FTN decoder, and describehow to leverage the insights for speed and area-efficiency. Our proof-of-conceptfield-programmable gate-array (FPGA) implementation reduces memory and logicfootprints to within 26% and 4% of an idealized FTN hardware decoder, and achievesa throughput of 1.46 Gbps.

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Interplay between fiber nonlinearity and probabilistic amplitude shaping (2022)

The maximum information rate of a communication channel, often referred to as the Shannon capacity, is achieved by optimizing the distribution of the transmitted symbols. Probabilistic constellation shaping is one principled approach to perform this optimization. Its fairly recently developed variant known as Probabilistic Amplitude Shaping (PAS) is of particular interest as it combines constellation shaping and error-correction coding. Conventional shaping methods, including PAS, are typically designed for the Additive White Gaussian Noise (AWGN) channel, or more generally, for linear channels. However, in optical fiber communications, the main bottleneck for increasing data rate is the so-called Kerr nonlinear effect, establishing an overall nonlinear channel. Therefore, the proper understanding of the nonlinear behavior of the optical channel and the development of nonlinearity tolerant PAS have been active areas of research. This thesis revisits the interplay between channel nonlinearity and PAS. We derive an effective linear channel model for the interaction of transmitted symbol energy sequences and the nonlinear distortion. Based on this, we introduce a visualization tool for nonlinearity analysis which permits us to explain several phenomenological observations reported in previous works. Furthermore, we develop a new nonlinearity tolerant PAS scheme, which outperforms state-of-the-art methods by a significant margin.

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Machine learning aided non-linear interference compensation for optical fiber communication systems (2022)

The capacity of optical fiber communication systems needs to be increased to meet the ever-growing demand for reliable high-speed data transmission. Nonlinear impairments induced by the Kerr effect are the primary bottleneck limiting the capacity of optical fibers. Since an analytical solution to nonlinearity compensation has not been found, machine learning based solutionsto overcome this algorithm deficit have gained considerable traction. It has been demonstrated that neural network (NN) based methods can provide performance and complexity benefits over conventional digital signal processing techniques. Moreover, unlike conventional nonlinearity compensation (NLC) schemes, machine learning solutions do not require accurate knowledge of the fiber parameters. In this thesis, we investigate the numerous NN based NLC techniques proposed in the literature and classify them based on key characteristics of their design. For a dual-polarized single mode fiber, we demonstrate that a distributed compensation scheme designed based on a conventional digital signal processing (DSP) solution provides the best performance at the lowest computational complexity. We note that this is due to the simplification of the nonlinear effect and its interplay with linear effects for short sub-sections of the fiber. Based on this, we propose a novel deep convolutional recurrent neural network (DCRNN) with distributed compensation of polarization mode dispersion (PMD). Based on numerical results, we show that the proposed learned NLC method outperforms all previous solutions, learned and deterministic, in both performance and complexity. The proposed neural network model achieves a 1.43 dB Q-factor gain over state ofthe art learned NLC schemes for a 960 Km 64-QAM single channel dual polarized transmission at 32 Gbaud/s. For practical fibers, PMD may drift over time, resulting in arbitrary performance loss. In this thesis, we propose a transfer learning based selective online training method to adapt the learned model to continuous evolution of PMD in real-time. Based on numerical results using the proposed online training method, we show that the learned model maintains its superiority at various levels of PMD drift. Furthermore, the model shows fast convergence even for random abrupt changein the PMD realization of the fiber.

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Model-based machine learning techniques for fiber nonlinearity compensation (2022)

The demand for faster high-volume data transmission over fiber optical long-haul links has been ever-increasing. This has been driving the need for further enhancing the data rates that can be carried by already deployed fibers. This is a non-trivial task as the Kerr effect causes nonlinear distortions to grow with increasing transmission power. Therefore, we are faced with the unusual situation that the effective signal-to-noise ratio decreases as the transmit power increases.Conventional nonlinear compensation methods, such as digital backpropagation (DBP) and perturbation theory-based nonlinearity compensation (PB-NLC), attempt to compensate for the nonlinearity by approximating analytical solutions to the signal propagation over fibers. However, their performances are limited by model mismatch and computational complexity. Recently, machine learning (ML) techniques have been used for the optimization of parameters of model-based approaches, which traditionally have been determined analytically from physical models. In the context of optical fiber transmission, it has been shown that ML-aided model-based approaches have improved performance and/or reduced complexity. In this thesis, we consider two specific ML-aided model-based nonlinear compensation approaches: learned DBP (LDBP) and learned PB-NLC.In our first contribution, starting from LDBP proposed in the existing literature, we propose a novel perturbation theory-aided learned digital backpropagation method. The key insight is that the number of steps of LDBP can significantly be decreased by augmenting each step with a filter response, as suggested by perturbation theory. We demonstrate that our proposed approach outperforms existing LDBP in terms of both performance and complexity. Our second contribution concerns the learned PB-NLC. We conduct a comprehensive performance-complexity analysis for various learned and non-learned PB-NLC approaches presented in the literature, utilizing state-of-the-art complexity reduction methods to map out the performance-complexity trade-off among them. Our results show that least squares-based PB-NLC with clustering quantization has the best performance-complexity trade-off. We advance the state-of-the-art of learned PB-NLC by developing a bi-directional recurrent neural network for generating features that are alike those obtained from perturbation theory and are used as input for learned nonlinearity compensation. We demonstrate that our proposed feature learning network achieves a similar performance as least-squares PB-NLC, but with a reduced complexity.

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Machine learning-assisted CRAN design with hybrid RF/FSO and full-duplex self-backhauling (2021)

The ever increasing demand for higher data rates, lower latency communication, and a more reliable mobile network has led us toward the 5th generation (5G) of mobile networks. In 5G, resource allocation is one of the most challenging problems. Conventionally, model-driven methods, and analytical approaches have been used to allocate resources optimally. Despite accuracy, these methods often result in a non-convex optimization problem that is inherently challenging to handle and require proper convex approximation. To overcome such drawbacks, we need more efficient resource allocation techniques in the 5G mobile network. This research will study the downlink of a cloud radio access network. The cloud radio access network enables coordinated beamforming and better interference management in ultra-dense networks. This architecture's bottleneck is backhaul capacity restriction limiting the benefits that the cloud radio access network offers. We will use hybrid radio frequency and free-space optical links to address the backhaul capacity limitation. Also, to improve the throughput and increase the spectral efficiency of the radio-frequency links, we propose in-band full-duplex self-backhauling radio units. After formulating the mathematical model and solving it with analytical approaches, we will introduce a novel solution for the proposed scenario and show that it outperforms the state-of-the-art half-duplex backhaul technology provided enough self-interference cancellation under various weather conditions.We will derive a joint optimization problem to design the backhaul and access link precoders and quantizers subject to the fronthaul capacity, zero-forcing, and power constraints. We will show that this problem is non-convex and computationally intractable and approximate it with a semi-definite programming that can be effectively solved by alternating convex optimization. We also employed Compute Canada computational resources for solving mentioned semi-definite programming. The computational complexity of the proposed optimization approach motivates us to employ machine-learning-based optimization methods that recently received much recognition in academia and industry. We use supervised and unsupervised deep neural networks for learning the optimal resource allocation strategy and achieved 80% of the performance compared to the proposed analytical approach with only a fraction of computational cost. To meet all feasibility constraints of the problem, we also propose customized activation functions and post-processing steps.

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Machine learning inspired ship-radiated noise modelling and cancellation for underwater acoustic communication systems (2020)

Achieving high data rate and reliable communication in shallow and harbour underwater acoustic (UA) environments can be a demanding task in the presence of ship-radiated noise. However, few research studies have examined the properties of ship-radiated noise in terms of its time-domain statistical characteristics and its negative effects on UA communication systems. From the observation of spectrograms and the temporal signals of various acoustic shipping noise recordings, high frequency and impulsive characteristics are visible. These impulsive agitations can be detrimental to the performance of multi-carrier UA communication systems, thus impulse noise cancellation methods are necessary to reduce errors. In this thesis, we investigate the impulsive and correlative interference generated due to nearby shipping activity and its effects on orthogonal frequency-division multiplexing (OFDM) systems. The research objectives are twofold: (1) model the time-domain stochastic characteristics of ship-radiated noise, and (2) achieve shipping noise cancellation for UA OFDM systems.We propose the use of unsupervised learning techniques to train generative models that capture the time-domain stochastic behaviours of ship-radiated noise using a publicly available database of long-term acoustic shipping noise recordings. These models can then be used for further analysis of ship-radiated noise and performance evaluation of UA OFDM systems in the presence of such interference. The results indicate a two component Gaussian mixture model serves as a better approximation for high frequency ship-radiated noise while generative adversarial networks produce improved realizations of shipping noise in lower frequencies.We offer sparsity and deep learning-based ship-radiated noise cancellation solutions that are constructed under a compressed sensing framework. Obtained results show that the sparsity-based estimation and cancellation algorithms demonstrate competitive mitigation capabilities for high frequency impulsive ship-radiated noise. The deep learning-based cancellation methods depict measurable shipping noise mitigation results to the sparsity-based techniques, but with superior run-time performance. In addition, the deep learning-based methods outperform the sparsity-based approaches in lower frequency ship-radiated noise due to the supplementary correlative structure. Furthermore, experimental results indicate the deep learning-based cancellation approaches scale better to new realizations of high frequency and low frequency shipping noise signals compared to the sparsity-based methods.

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Modular spectrum utilization for next-generation fixed transmission networks (2020)

No abstract available.

A Comparative Evaluation of Two Synthetic Transmit Aperture with Virtual Source Beamforming Methods in Biomedical Ultrasound (2016)

This thesis studies the Synthetic Transmit Aperture with Virtual Source (STA-VS) beamforming method, which is an emerging technique in biomedical ultrasound. It promises better imaging quality compared to conventional beamforming, with the same imaging speed. Several specific realizations of the STA-VS methods have been proposed in the literature and the topic is an active research area. The first part of the thesis examines two realizations of the STA-VS method, namely the Synthetic Aperture Sequential Beamforming (SASB) method and the bi-directional pixel-based focusing (BiPBF) method. Studies are performed with both ultrasound simulation software and a commercial ultrasound scanner's research interface. The studies show that the STA-VS methods can improve the spatial and contrast resolution of ultrasound imaging. The two stage implementation of SASB has lower complexity between the two STA-VS methods. However, compared to other beamformers, SASB is more susceptible to speed-of-sound (SOS) errors in the beamforming calculations. The second part of the thesis proposes an SOS estimation and correction algorithm. The SOS estimation part of the algorithm is based on second-order polynomial fitting to point scatterers in pre-beamformed data, and is specifically applicable to the two stage realization of the SASB method. The SOS correction part of the algorithm is incorporated into the second stage beamforming of the SASB method and is shown to improve the spatial resolution of the beamformed image. This algorithm is also adapted to, and tested on, vertical two-layer structures with two distinct SOS's, through simulations and measurements on an in-house phantom. The premise is that two layers can simulate a fat/muscle or fat/organ anatomy. Spatial resolution is shown to improve with the SOS correction. Future work will investigate whether that this two-layer SOS estimation and correction algorithm will similarly improve the imaging quality in vivo such as abdominal ultrasound examinations of overweight patients.

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In-vehicle powerline communication using software-defined radio (2016)

Powerline communication is an attractive solution for in-vehicle communication. However, the research of communication over powerlines requires field-testing and full access to communication protocol layers, particularly to the physical and the Media Access Control (MAC) layers. This ability can be accomplished through the use of software-defined radio along with real-time signal processing executed on a personal computer. In this work, we present the design and implementation of an IEEE 1901-based transceiver aimed for vehicular powerlines, written for GNU Radio and operated on Ettus Universal Software Radio Peripheral (USRP) N210 hardware. The software components include a C++ physical layer signal processing library and several complementary GNU Radio blocks including a MAC layer block written in Python. The implemented capabilities include several channel estimation methods, a noise power spectral density estimator and an adaptive bit loading algorithm. We make all the software components available as an open source project to facilitate further development by the broader research community. We then show experimental results obtained with the system applied to a vehicle harness and a real vehicle powerline network. In the first part of the experiments, we demonstrate the correctness of the implementation, compare between several channel estimation methods, and test the system performance. In the second part, we examine the feasibility of reliable communication with IEEE 1901 over powerlines in a car. Our experiments show that IEEE 1901 along with the implemented receiver algorithm are capable of operating in the scenarios tested. The vehicular impulsive noise is identified as the primary cause for errors. In particular, our experiments show that it affects mainly the frame synchronization. Hence we believe that further investigations of in-vehicle powerline communication should focus on alleviating the effect of impulse noise on synchronization.

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Ethernet over Light (2015)

The advent of high-brightness, fast-switching Light Emitting Diodes (LEDs) has facilitated Visible Light Communication (VLC) as a new form of Optical Wireless Communication (OWC) over the visible light spectrum. In VLC, these LEDs serve a dual purpose of communication on top of general illumination. The biggest challenge facing VLC lies in finding the “killer application" that will propel the technology to widespread adoption. One of the ways we believe this can be achieved is by integrating VLC with pre-existing Ethernet Local Area Network (LAN) backbones. Although there has been some preliminary research in this area, specifically involving 10Base-T over VLC, none have explicitly dealt with Fast Ethernet (100 Mbps) over VLC. In this thesis, we investigate the implementation of analog transmission of 100Base-TX over VLC in an amplify-and-forward approach, which we coin as Ethernet-over-Light (EoL). We present the design of a VLC transmitter and accompanying receiver developed for EoL, and include a comprehensive channel model to analyze this Ethernet-VLC link. Equalization techniques were explored to overcome the various shortcomings associated with the EoL channel, and to improve the performance of the overall system. This VLC-LAN solution proved to be a viable alternative for providing a wireless broadcast link wherever LEDs would be deployed for illumination in an indoor setting.

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Improving the Feasibility of Energy Disaggregation in Very High- and Low-Rate Sampling Scenarios (2015)

Given the world's urgent need to reduce greenhouse gas emissions in order to avoid the most disastrous effects of climate change, efforts must be made to reduce these emissions in every way possible. A large share of these emissions come from the energy consumption in buildings and there are significant opportunities to reduce this consumption through energy saving measures. Energy disaggregation or non-intrusive load monitoring (NILM) is a useful tool that infers the energy consumption of individual appliances or equipment within a building from detailed measurements of the building's total energy consumption. This method is very attractive for providing a detailed breakdown of building energy consumption because it is less expensive and more convenient than measuring the energy use of each appliance individually. A wide variety of NILM methods have been proposed and in this thesis we focus on improving the feasibility of two different classes of NILM methods. We first explore the use of random filtering and random demodulation, two methods closely related to the new and developing field of compressed sensing (CS), to acquire and manage very-high-rate electrical measurements used for NILM. We show that these methods allow us to reduce the required sampling rate and volume of data collected while retaining valuable signal information required for NILM. Second, we switch to the analysis of very-low-rate data for NILM and develop a method to detect interesting patterns in the very-low-rate aggregate consumption signal. These patterns are shown to be responsible for a significant share of the total energy consumption in some buildings and are also related to the outdoor air temperature in some cases. Taken together, the two parts of this thesis allow us to contribute to the field of NILM by improving its feasibility and helping to facilitate its widespread use.

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Phsyical Layer Security in MIMO Power Line Communication Networks (2014)

It has been well established that multiple-input multiple-output(MIMO) transmission using multiple conductors can improve the data rate of power linecommunication (PLC) systems. In this thesis, we investigate whetherthe presence of multiple conductors could also facilitate the communication of confidential messages by means ofphysical layer security methods. In particular, this thesisfocuses on the secrecy capacity of MIMO PLC. Numericalexperiments show that multi-conductor PLC networks can enable a moresecure communication compared to the single conductor case. On theother hand, we demonstrate that the keyhole property of PLC channelsgenerally diminishes the secure communication capability compared towhat would be achieved in a similar wireless communications setting.Furthermore, we consider the cases of unknown and partially known channelstate information (CSI) about theeavesdropper channel. For this purpose, we provide deterministic channeluncertainty model parameters for PLC networks via the bottom-up channel modelling method.Numerical results show how imperfect CSI has a negative impact on secure communication,and enable us to analyze the tradeoff between choosing different transmission strategies that correspond tounknown CSI and partially known CSI.

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Advances in synthetic aperture, compounded plane wave, and spatially encoded excitation techniques for fast ultrasonography (2013)

Ultrasonography offers subcutaneous imaging at a fraction of the cost of magnetic resonance imaging (MRI) and without the ionizing radiation of X-ray or computed tomography (CT) imaging. In addition, ultrasound imaging machines are compact and portable, and do not require any sort of specialized environment to function. Ultrasonography is, however, limited by the relatively slow speed of sound and standard beamforming can only achieve low imaging frame rates (20 - 80 frames per second). This restricts its use in a number of applications that would otherwise benefit greatly from its use. For example, transient elastography, 3-dimensional volumetric imaging, and Doppler sonography would all benefit from higher frame rates.This thesis presents two new variations of fast imaging methods. The first is by combining two existing fast-imaging techniques, plane wave (PW) and synthetic aperture (SA), using an adaptive weighting algorithm to compound images generated from the techniques individually. This method improves image resolution and signal-to-noise ratio (SNR) without losing the higher frame rate of each, which is successfully demonstrated through experiments on a physical commercial ultrasound system. The second method for increasing frame rate involves two extensions on a spatial encoding technique proposed by Fredrik Gran and Jørgen Arendt Jensen in 2008; these extensions entailed implementing a compressed sensing algorithm to reduce the code length requirement presented in their paper and removing the non-imagable “deadzone” region that their method produces. These extensions are applicable for scenarios requiring high definition for a small set of high-reflectivity points in an otherwise dark region, such as intra-spinal needle guidance, and are demonstrated using the Field II ultrasound simulation software.

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Inference and communications over power lines (2013)

Power line communications (PLC) refers to the technology that reuses power supply cables for communications. Its origins go back to the early 1900's, and it has long been used for control and automation by electricity utilities. PLC has attracted considerable attention for supporting smart grid applications. Since it reuses the existing grid infrastructure, it offers cost advantages over alternative communications methods and gives electric utilities control over the communications medium. Furthermore, the "through-the-grid" property of PLC extends its possible use beyond mere communications. Since the PLC signals are bound to travel through the power grid, they can also be used for inference tasks, such as online diagnostics of power line integrity. In this thesis, we study two different problems that are related to PLC. The first problem is inference of power line topology. That is how to reconstruct the power network infrastructure using certain measurements. Forthis problem, the "through-the-grid" property of PLC is exploited. We investigate two solutions for this problem based on single-ended or multiple-ended measurements. We propose algorithms for both cases. Simulation results demonstrate the successful and accurate reconstruction of the grid topology by the proposed algorithms. The second problem is crosstalk elimination for PLC. For this problem, experiments were performed to measure the crosstalk and compare the results with analytical models. The results from the measurement campaign were used to evaluate the possible gains by the use of multiuser detection methods for crosstalk cancellation. Measurements and simulation results show the effectiveness of the proposed algorithms and methods.

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Multiuser detection for transmit-only active RFID (2012)

One of the biggest challenges in Radio Frequency Identification (RFID) systems is mitigating tag collisions. Most systems tackle this problem using Medium Access Control layer solutions. Unfortunately, these solutions are not applicable to systems with transmit-only tags, since the tags cannot detect collisions. This thesis introduces a novel reader design that employs multiuser detection techniques to jointly detect data from colliding packets in such systems. We propose a physical layer solution that exploits signal structure to mitigate tag collisions. Since the RFID tags generate their own clock using inexpensive hardware, this framework poses some unique challenges. We present methods for collision detection, synchronization, and channel estimation, as well as demodulation of the collidingsignals. We show simulation results that demonstrate the gains in performance obtained using the proposed solution.

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Localization systems using signal strength fingerprinting (2010)

The task of estimating the location of a mobile transceiver using the Received Signal Strength Indication (RSSI) values of radio transmissions to/from other radios is an inference problem. The fingerprinting paradigm is the most promising genre of methods studied in the literature. It constructs deterministic or probabilistic models from data sampled at the site. Probabilistic formulations are popular because they can be used under the Bayesian filter framework. We also categorize fingerprinting methods into regression or classification. The vast majority of existing methods perform regression as they estimate location information in terms of position coordinates. In contrast, the classification approach only estimates a specific region (e.g., kitchen or bedroom). This thesis is a continuation of studies on the fingerprinting paradigm. For the regression approach, we perform a comparison between the Unscentend Kalman Filter (UKF) and the Particle Filter (PF), two suboptimal solutions for the Bayesian filter. The UKF assumes near-linearity and imposes unimodal Gaussian densities while the PF does not. These assumptions are very fragile and we show that the UKF is not a robust solution in practice. For the classification approach, we are intrigued by a simple method we name the Simple Gaussian Classifier (SGC). We ponder if this simple method comes at a cost in terms of classfication errors. We compare the SGC against the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), two other popular classifiers. Experimental results present evidence that the SGC is very competitive. Furthermore, because the SGC is written in closed-form, it can be used directly under the Bayesian filter framework, which is better known as the Hidden Markov Model (HMM) filter. The fingerprinting paradigm is powerful but it suffers from the fact that conditions may change. We propose extending the Bayesian filter framework by utilizing the filter derivative to realize an online estimation scheme, which tracks the time-varying parameters. Preliminary results show some promise but further work is needed to validate its performance.

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