Frequency-based radar waveform design for target classification performance optimisation using Fisher analysis

This thesis presents non-adaptive radar waveform and receiver designs to improve radar target identification performance. The designs are based on the theory of Fisher discriminants analysis and Fisher separability functions. Introducing Fisher discriminants analysis in waveform design for target maximisation is the first contribution of this thesis. By using the concepts of Fisher analysis both for 2-class or multiclass scenarios, a separability rational function can be derived for practical extended targets classification. The separability functions are formulated to maximise the distance between the means of data classes while minimising their variance. Fisher separability is used as an objective function for the optimisation problem to find the optimal waveform that maximises it under constant energy constraints. The classifiers are derived and inspired by Fisher minimum distance classifiers. The second contribution of the thesis is deriving low-energy low-covariance (LELC) closed-form solutions for the optimisation problem under additive white Gaussian noise (AWGN) conditions. These solutions perform well especially when the signal-to-noise ratio is low. Further, a closed-form solution for the optimisation problem is derived for the 2-class scenario. The solution achieves classification performance comparable to solutions obtained using general optimisation solvers. The proposed waveform and receiver design methods are tested using synthetic and real target data and is shown to achieve better performance than the wideband chirp and other non-adaptive waveform design methods reported in the literature

An Optimal Medium Access Control with Partial Observations for Sensor Networks

We consider medium access control (MAC) in multihop sensor networks, where only partial information about the shared medium is available to the transmitter. We model our setting as a queuing problem in which the service rate of a queue is a function of a partially observed Markov chain representing the available bandwidth, and in which the arrivals are controlled based on the partial observations so as to keep the system in a desirable mildly unstable regime. The optimal controller for this problem satisfies a separation property: we first compute a probability measure on the state space of the chain, namely the information state, then use this measure as the new state on which the control decisions are based. We give a formal description of the system considered and of its dynamics, we formalize and solve an optimal control problem, and we show numerical simulations to illustrate with concrete examples properties of the optimal control law. We show how the ergodic behavior of our queuing model is characterized by an invariant measure over all possible information states, and we construct that measure. Our results can be specifically applied for designing efficient and stable algorithms for medium access control in multiple-accessed systems, in particular for sensor networks

Resource Constrained Adaptive Sensing.

RESOURCE CONSTRAINED ADAPTIVE SENSING by Raghuram Rangarajan Chair: Alfred O. Hero III Many signal processing methods in applications such as radar imaging, communication systems, and wireless sensor networks can be presented in an adaptive sensing context. The goal in adaptive sensing is to control the acquisition of data measurements through adaptive design of the input parameters, e.g., waveforms, energies, projections, and sensors for optimizing performance. This dissertation develops new methods for resource constrained adaptive sensing in the context of parameter estimation and detection, sensor management, and target tracking. We begin by investigating the advantages of adaptive waveform amplitude design for estimating parameters of an unknown channel/medium under average energy constraints. We present a statistical framework for sequential design (e.g., design of waveforms in adaptive sensing) of experiments that improves parameter estimation (e.g., scatter coefficients for radar imaging, channel coefficients for channel estimation) performance in terms of reduction in mean-squared error (MSE). We derive optimal adaptive energy allocation strategies that achieve an MSE improvement of more than 5dB over non adaptive methods. As a natural extension to the problem of estimation, we derive optimal energy allocation strategies for binary hypotheses testing under the frequentist and Bayesian frameworks which yield at least 2dB improvement in performance. We then shift our focus towards spatial design of waveforms by considering the problem of optimal waveform selection from a large waveform library for a state estimation problem. Since the optimal solution to this subset selection problem is combinatorially complex, we propose a convex relaxation to the problem and provide a low complexity suboptimal solution that achieves near optimal performance. Finally, we address the problem of sensor and target localization in wireless sensor networks. We develop a novel sparsity penalized multidimensional scaling algorithm for blind target tracking, i.e., a sensor network which can simultaneously track targets and obtain sensor location estimates.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57621/2/rangaraj_1.pd

Advanced multi-dimensional signal processing for wireless systems

Die florierende Entwicklung der drahtlosen Kommunikation erfordert innovative und fortschrittliche Signalverarbeitungsalgorithmen, die auf eine verbesserte Performance hinsichtlich der Zuverlässigkeit, des Durchsatzes, der Effizienz und weiterer Faktoren abzielen. Die vorliegende Arbeit befasst sich mit der Lösung dieser Herausforderungen und präsentiert neue und faszinierende Fortschritte, um diesen Herausforderungen zu erfüllen. Hauptsächlich konzentrieren wir uns auf zwei innovative Aspekte der mehrdimensionalen Signalverarbeitung für drahtlose Systeme, denen in den letzten Jahren große Aufmerksamkeit in der Forschung geschenkt wurde. Das sind Mehrträgerverfahren für Multiple-Input Multiple-Output (MIMO) Systeme und die mehrdimensionale harmonische Schätzung (Harmonic Retrieval). Da es sich bei MIMO-Systemen und Mehrträgerverfahren um Schlüsseltechnologien der drahtlosen Kommunikation handelt, sind ihre zahlreichen Vorteile seit langem bekannt und haben ein großes Forschungsinteresse geweckt. Zu diesen Vorteilen zählen zum Beispiel die Steigerung der Datenrate und die Verbesserung der Verbindungszuverlässigkeit. Insbesondere OFDM-basierte MIMO Downlink Systeme für mehrere Teilnehmer (Multi-User MIMO Downlink Systems), die durch SDMA (Space-Division Multiple Access) getrennt werden, kombinieren die Vorteile von MIMO-Systemen mit denen von Mehrträger-Modulationsverfahren. Sie sind wesentliche Elemente des IEEE 802.11ac Standards und werden ebenfalls für 5G (die fünfte Mobilfunkgeneration) ausschlaggebend sein. Obwohl die bisherigen Arbeiten über das Precoding (Vorcodierung) für solche Multi-User MIMO Downlink Systeme schon fruchtbare Ergebnisse zeigten, werden neue Fortschritte benötigt, die den Mehrträger-Charakter des Systems in einer effizienteren Weise ausnutzen oder auf eine höhere spektrale Effizienz des Gesamtsystems abzielen. Andererseits gilt die Filterbank-basierte Mehrträger Modulation (Filter Bank-based Multi-Carrier modulation, FBMC) mit einem gut konzentrierten Spektrum und einer somit niedrigen Out-of-band Leackage als eine vielversprechende Alternative zu OFDM. FBMC ermöglicht eine effiziente Nutzung von Fragmenten im Frequenzspektrums, z. B. in 5G oder Breitband Professional Mobile Radio (PMR) Netzwerken. Jedoch leiden die vorhandenen Verfahren zur Sende- und-Empfangs-Verarbeitung für FBMC-basierte MIMO Systeme unter Einschränkungen in Bezug auf mehrere Aspekte, wie z. B. der erlaubten Dimensionalität des Systems und der zulässigen Frequenzselektivität des Kanals. Die Formen der MIMO Einstellungen, die in der Literatur untersucht wurden, sind noch begrenzt auf MIMO-Systeme für einzelne Teilnehmer und vereinfachte Multi-User MIMO Systeme. Fortschrittlichere Techniken sind daher erforderlich, die diese Einschränkungen der existierenden Verfahren aufheben. MIMO-Szenarien, die weniger Einschränkungen unterliegen, müssen außerdem untersucht werden, um die Vorteile von FBMC zu weiter herauszuarbeiten. Im Rahmen der mehrdimensionalen harmonischen Schätzung (Harmonic Retrieval) hat sich gezeigt, dass eine höhere Genauigkeit bei der Schätzung durch Tensoren erreicht werden kann. Das liegt daran, dass die Darstellung mehrdimensionaler Signale mit Tensoren eine natürlichere Beschreibung und eine gute Ausnutzung ihrer mehrdimensionalen Struktur erlaubt, z. B. für die Modellordnungsschätzung und die Unterraumschätzung. Wichtige offene Themen umfassen die statistische Robustheit und wie man die Schätzung in zeitlich variierenden Szenarien adaptiv gestalten kann. In Teil I dieser Arbeit präsentieren wir zunächst eine effiziente und flexible Übertragungsstrategie für OFDM-basierten Multi-User MIMO Downlink Systeme. Sie besteht aus einer räumlichen Scheduling-Methode, der effizienten Mehrträger ProSched (Efficient Multi-Carrier ProSched, EMC-ProSched) Erweiterung mit einer effektiven Scheduling-Metrik, die auf Mehrträger-Systeme zugeschnitten wird. Weiterhin werden zwei neuartige Precoding Algorithmen vorgestellt, die lineare Precoding-basierte geometrische Mittelwert-Zerlegung (Linear Precoding-based Geometric Mean Decomposition, LP-GMD) und ein Coordinated Beamforming Algorithmus geringer Komplexität (Low Complexity Coordinated Beamforming, LoCCoBF). Diese beiden neuen Precoding-Verfahren können flexibel entsprechend den Abmessungen des Systems gewählt werden. Wir entwickeln auch einen System Level-Simulator, in dem die Parameter für das Link-to-System Level Interface kalibriert werden können. Diese Kalibrierung ist Standard-spezifisch, z. B. kann der Standard IEEE 802.11ac gewählt werden. Numerische Ergebnisse zeigen, dass diese Übertragungsstrategie Scheduling Fairness garantiert, einen weitaus höheren Durchsatz als die existierenden Verfahren erzielt, eine geringere Komplexität besitzt und nur einen geringen Signalisierungsoverhead erfordert. Der Schwerpunkt des Rests von Teil I bilden MIMO Systeme basierend auf Filter Bank-basierten Mehrträger-Verfahren mit Offset Quadrature Amplitude Modulation (FBMC/OQAM). Es wird ein umfassender Überblick über FBMC gegeben. Nachfolgend werden für verschiedene FBMC/OQAM-basierte MIMO Varianten neue Verfahren zur Sende- und Empfangs-Verarbeitung entwickelt, die unterschiedliche Grade von Frequenz-Selektivität des Kanals voraussetzen. Zunächst wird die Verwendung von weitgehend linearer Verarbeitung (widely linear processing) untersucht. Ein Zwei-Schritt-Empfänger wird für FBMC/OQAM-basierte MIMO Systeme mit einzelnen Teilnehmern entwickelt. Hierbei ist die Frequenz-Selektivität des Kanals niedrig. Verglichen mit linearen MMSE-Empfänger ist die Leistung des Zwei-Schritt-Empfängers viel besser. Das Grundprinzip dieser Zwei-Schritt-Empfänger ist zuerst die Verringerung der intrinsischen Interferenz, um die Ausnutzung von nicht-zirkulären Signalen zu ermöglichen. Es motiviert weitere Studien über weitgehend lineare Verfahren für FBMC/OQAM-basierte Systeme. Darüber hinaus werden zwei Coordinated Beamforming-Algorithmen für FBMC/OQAM-basierte MIMO Systeme mit einzelnen Teilnehmern entwickelt. Sie verzichten auf die Einschränkung der Dimensionalität der bestehenden Methode, bei der die Anzahl der Sendeantennen größer als die Anzahl der Empfangsantennen sein muss. Der Kanal auf jedem Träger wird als flacher Schwund (Flat Fading) modelliert, was einer Klassifizierung als „intermediate frequency selective channel“ entspricht. Unter der Kenntnis der Kanalzustandsinformation am Sender (Channel-State-Information at the Transmitter, CSIT) basiert die Vorcodierung entweder auf einem Zero Forcing (ZF) Kriterium oder auf der Maximierung der Signal-to-Leackage-plus-Noise-Ratio (SLNR). Die Vorcodierungsvektoren und die Empfangsvektoren werden gemeinsam und iterativ berechnet. Daher führen die zwei Coordinated Beamforming-Algorithmen zu einer wirksamen Verringerung der intrinsischen Interferenz in FBMC/OQAM-basierten Systemen. Die Vorteile der Coordinated Beamforming-Konzepte werden in FBMC/OQAM-basierten Multi-User MIMO Downlink Systeme und koordinierte Mehrpunktverbindung (Coordinated Multi-Point, CoMP-Konzepte) eingebracht. Dafür werden drei intrinsische Interferenz mildernde koordinierte Beamforming-Verfahren (Intrinsic Interference Mitigating Coordinated Beamforming, IIM-CBF) vorgeschlagen. Die ersten beiden IIM-CBF Algorithmen werden für die FBMC/OQAM-basierten Multi-User MIMO Downlink Varianten mit unterschiedlichen Dimensionen entwickelt. Es wird gezeigt, dass diese Verfahren zu einer Abschwächung der Multi-User-Interferenz (MUI) sowie einer Verringerung der intrinsischen Interferenz führen. Bei der dritten IIM-CBF Methode wird ein neuartiges FBMC/OQAM-basiertes-CoMP Konzept vorgestellt. Dieses wird durch die gemeinsame Übertragung von benachbarten Zellen zu Teilnehmern, die sich am Zellenrand befinden, ermöglicht, um den Daten-Durchsatz am Zellenrand zu erhöhen. Die Leistungsfähigkeit der vorgeschlagenen Algorithmen wird durch umfangreiche numerische Simulationen evaluiert. Das Konvergenzverhalten wird untersucht sowie das Thema der Komplexität angesprochen. Außerdem wird die geringere Anfälligkeit von FBMC verglichen mit OFDM gegenüber Frequenzsynchronisationsfehlern demonstriert. Darüber hinaus wird auf die FBMC/OQAM-basierten Multi-User MIMO Downlink Systeme mit stark frequenzselektiven Kanälen eingegangen. Dafür werden Lösungen erarbeitet, die für die Unterdrückung der MUI, der Inter-Symbol Interferenz (ISI) sowie der Inter-Carrier Interferenz (ICI) anwendbar ist. Mehrere Kriterien der multi-tap Vorcodierung werden entwickelt, beispielsweise die Mean Squared Error (MSE) Minimierung sowie die Signal-to-Leakage-Ratio (SLR) und die SLNR Maximierung. An Endgeräten, die eine schwächere Rechenleistung besitzen als sie an der Basisstation vorhanden ist, wird dadurch nur ein single-tap Empfangsfilter benötigt. Teil II der Arbeit konzentriert sich auf die mehrdimensionale harmonische Schätzung (Harmonic Retrieval). Der Einbau von statistischer Robustheit in mehrdimensionale Modellordnungsschätzverfahren wird demonstriert.The thriving development of wireless communications calls for innovative and advanced signal processing techniques targeting at an enhanced performance in terms of reliability, throughput, robustness, efficiency, flexibility, etc.. This thesis addresses such a compelling demand and presents new and intriguing progress towards fulfilling it. We mainly concentrate on two advanced multi-dimensional signal processing challenges for wireless systems that have attracted tremendous research attention in recent years, multi-carrier Multiple-Input Multiple-Output (MIMO) systems and multi-dimensional harmonic retrieval. As the key technologies of wireless communications, the numerous benefits of MIMO and multi-carrier modulation, e.g., boosting the data rate and improving the link reliability, have long been identified and have ignited great research interest. In particular, the Orthogonal Frequency Division Multiplexing (OFDM)-based multi-user MIMO downlink with Space-Division Multiple Access (SDMA) combines the twofold advantages of MIMO and multi-carrier modulation. It is the essential element of IEEE 802.11ac and will also be crucial for the fifth generation of wireless communication systems (5G). Although past investigations on scheduling and precoding design for multi-user MIMO downlink systems have been fruitful, new advances are desired that exploit the multi-carrier nature of the system in a more efficient manner or aim at a higher spectral efficiency. On the other hand, a Filter Bank-based Multi-Carrier modulation (FBMC) featuring a well-concentrated spectrum and thus a low out-of-band radiation is regarded as a promising alternative multi-carrier scheme to OFDM for an effective utilization of spectrum fragments, e.g., in 5G or broadband Professional Mobile Radio (PMR) networks. Unfortunately, the existing transmit-receive processing schemes for FBMC-based MIMO systems suffer from limitations in several aspects, e.g., with respect to the number of supported receive antennas (dimensionality constraint) and channel frequency selectivity. The forms of MIMO settings that have been investigated are still limited to single-user MIMO and simplified multi-user MIMO systems. More advanced techniques are therefore demanded to alleviate the constraints imposed on the state-of-the-art. More sophisticated MIMO scenarios are yet to be explored to further corroborate the benefits of FBMC. In the context of multi-dimensional harmonic retrieval, it has been demonstrated that a higher estimation accuracy can be achieved by using tensors to preserve and exploit the multidimensional nature of the data, e.g., for model order estimation and subspace estimation. Crucial pending topics include how to further incorporate statistical robustness and how to handle time-varying scenarios in an adaptive manner. In Part I of this thesis, we first present an efficient and flexible transmission strategy for OFDM-based multi-user MIMO downlink systems. It consists of a spatial scheduling scheme, efficient multi-carrier ProSched (EMC-ProSched), with an effective scheduling metric tailored for multi-carrier systems and two new precoding algorithms, linear precoding-based geometric mean decomposition (LP-GMD) and low complexity coordinated beamforming (LoCCoBF). These two new precoding schemes can be flexibly chosen according to the dimensions of the system. We also develop a system-level simulator where the parameters for the link-to-system level interface can be calibrated according to a certain standardization framework, e.g., IEEE 802.11ac. Numerical results show that the proposed transmission strategy, apart from guaranteeing the scheduling fairness and a small signaling overhead, achieves a much higher throughput than the state-of-the-art and requires a lower complexity. The remainder of Part I is dedicated to Filter Bank-based Multi-Carrier with Offset Quadrature Amplitude Modulation (FBMC/OQAM)-based MIMO systems. We begin with a thorough overview of FBMC. Then we present new transmit-receive processing techniques for FBMC/OQAM-based MIMO settings ranging from the single-user MIMO case to the Coordinated Multi-Point (CoMP) downlink considering various degrees of channel frequency selectivity. The use of widely linear processing is first investigated. A two-step receiver is designed for FBMC/OQAM-based point-to-point MIMO systems with low frequency selective channels. It exhibits a significant performance superiority over the linear MMSE receiver. The rationale in this two-step receiver is that the intrinsic interference is first mitigated to facilitate the exploitation of the non-circularity residing in the signals. It sheds light upon further studies on widely linear processing for FBMC/OQAM-based systems. Moreover, two coordinated beamforming algorithms are devised for FBMC/OQAM-based point-to-point MIMO systems to relieve the dimensionality constraint of existing schemes that the number of transmit antennas must be larger than the number of receive antennas. The channel on each subcarrier is assumed to be flat fading, which is categorized as the class of intermediate frequency selective channels. With the Channel State Information at the Transmitter (CSIT) known, the precoder designed based on a Zero Forcing (ZF) criterion or the maximization of the Signal-to-Leakage-plus-Noise-Ratio (SLNR) is jointly and iteratively computed with the receiver, leading to an effective mitigation of the intrinsic interference inherent in FBMC/OQAM-based systems. The benefits of the coordinated beamforming concept are successfully translated into the FBMC/OQAM-based multi-user MIMO downlink and the CoMP downlink. Three intrinsic interference mitigating coordinated beamforming (IIM-CBF) schemes are developed. The first two IIM-CBF schemes are proposed for FBMC/OQAM-based multi-user MIMO downlink settings with different dimensions and are able to effectively suppress the Multi-User Interference (MUI) as well as the intrinsic interference. A novel FBMC/OQAM-based CoMP concept is established via the third IIM-CBF scheme which enables the joint transmission of adjacent cells to the cell edge users to combat the strong interference as well as the heavy path loss and to boost the cell edge throughput. The performance of the proposed algorithms is evaluated via extensive numerical simulations. Their convergence behavior is studied, and the complexity issue is also addressed. In addition, the stronger resilience of FBMC over OFDM against frequency misalignments is demonstrated. Furthermore, we cover the case of highly frequency selective channels and provide solutions to the very challenging task of suppressing the MUI, the Inter-Symbol Interference (ISI), as well as the Inter-Carrier Interference (ICI) and supporting per-user multi-stream transmissions. Several design criteria of the multi-tap precoders are devised including the Mean Squared Error (MSE) minimization as well as the Signal-to-Leakage-Ratio (SLR) and SLNR maximization. By rendering a larger computational load at the base station, only single-tap spatial receive filters are required at the user terminals with a weaker computational capability, which enhances the applicability of the proposed schemes in real-world multi-user MIMO downlink systems. Part II focuses on the context of multi-dimensional harmonic retrieval. We demonstrate the incorporation of statistical robustness into multi-dimensional model order estimation schemes by substituting the sample covariance matrices of the unfoldings of the measurement tensor with robust covariance estimates. It is observed that in the presence of a very severe contamination of the measurements due to brief sensor failures, the robustified tensor-based model order estimation schemes lead to a satisfactory estimation accuracy. This philosophy of introducing statistical robustness also inspires robust versions of parameter estimation algorithms. Last but not the least, we present a generic framework for Tensor-based subspace tracking via Kronecker-structured projections (TeTraKron) for time-varying multi-dimensional harmonic retrieval problems. It allows to extend arbitrary matrix-based subspace tracking schemes to track the tensor-based subspace estimate in an elegant and efficient manner. By including forward-backward-averaging, we show that TeTraKron can also be employed to devise real-valued tensor-based subspace tracking algorithms. Taking a few matrix-based subspace tracking approaches as an example, a remarkable improvement of the tracking accuracy is observed in case of the TeTraKron-based tensor extensions. The performance of ESPRIT-type parameter estimation schemes is also assessed where the subspace estimates obtained by the proposed TeTraKron-based subspace tracking algorithms are used. We observe that Tensor-ESPRIT combined with a tensor-based subspace tracking scheme significantly outperforms the combination of standard ESPRIT and the corresponding matrix-based subspace tracking method. These results open the way for robust multi-dimensional big data signal processing applications in time-varying environments

A Probabilistic Model of Spectrum Occupancy, User Activity, and System Throughput for OFDMA based Cognitive Radio Systems

With advances in communications technologies, there is a constant need for higher data rates. One possible solution to overcome this need is to allocate additional bandwidth. However, due to spectrum scarcity this is no longer feasible. In addition, the results of spectrum measurement campaigns discovered the fact that the available spectrum is under-utilized. One of the most significant solutions to solve the under- utilization of radio-frequency (RF) spectrum is the cognitive radio (CR) concept. A valid mathematical model that can be applied for most practical scenarios and also captures the random fluctuations of the spectrum is necessary. This model provides a significant insight and also a better quantitative understanding of such systems and this is the topic of this dissertation. Compact mathematical formulations that describe the realistic spectrum usage would improve the recent theoretical work to a large extent. The data generated for such models, provide a mean for a more realistic evaluation of the performance of CR systems. However, measurement based models require a large amount of data and are subject to measurement errors. They are also likely to be subject to the measurement time, location, and methodology. In the first part of this dissertation, we introduce cognitive radio networks and their role on solving the problem of under-utilized spectrum. In the second part of this dissertation, we target the random variable which accounts for the fraction of available subcarriers for the secondary users in an OFDMA based CR system. The time and location dependency of the traffic is taken into account by a non-homogenous Poisson Point Process (PPP). In the third part, we propose a comprehensive statistical model for user activity, spectrum occupancy, and system throughput in the presence of mutual interference in an OFDMA-based CR network which accounts for the sensing procedure of spectrum sensor, spectrum demand-model and spatial density of primary users, system objective for user satisfaction which is to support as many users as possible, and environment-dependent conditions such as propagation path loss, shadowing, and channel fading. In the last part of this dissertation, unlike the second and the third parts that the modeling is theoretical and based on limiting assumptions, the spectrum usage modeling is based on real data collected from an extensive measurement

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

On Random Sampling for Compliance Monitoring in Opportunistic Spectrum Access Networks

In the expanding spectrum marketplace, there has been a long term evolution towards more market€“oriented mechanisms, such as Opportunistic Spectrum Access (OSA), enabled through Cognitive Radio (CR) technology. However, the potential of CR technologies to revolutionize wireless communications, also introduces challenges based upon the potentially non€“deterministic CR behaviour in the Electrospace. While establishing and enforcing compliance to spectrum etiquette rules are essential to realization of successful OSA networks in the future, there has only been recent increased research activity into enforcement. This dissertation presents novel work on the spectrum monitoring aspect, which is crucial to effective enforcement of OSA. An overview of the challenges faced by current compliance monitoring methods is first presented. A framework is then proposed for the use of random spectral sampling techniques to reduce data collection complexity in wideband sensing scenarios. This approach is recommended as an alternative to Compressed Sensing (CS) techniques for wideband spectral occupancy estimation, which may be difficult to utilize in many practical congested scenarios where compliance monitoring is required. Next, a low€“cost computational approach to online randomized temporal sensing deployment is presented for characterization of temporal spectrum occupancy in cognitive radio scenarios. The random sensing approach is demonstrated and its performance is compared to CS€“based approach for occupancy estimation. A novel frame€“based sampling inversion technique is then presented for cases when it is necessary to track the temporal behaviour of individual CRs or CR networks. Parameters from randomly sampled Physical Layer Convergence Protocol (PLCP) data frames are used to reconstruct occupancy statistics, taking account of missed frames due to sampling design, sensor limitations and frame errors. Finally, investigations into the use of distributed and mobile spectrum sensing to collect spatial diversity to improve the above techniques are presented, for several common monitoring tasks in spectrum enforcement. Specifically, focus is upon techniques for achieving consensus in dynamic topologies such as in mobile sensing scenarios

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Design and analysis of space-time block and trellis coding schemes for single-band UWB communications systems

Ultra Wide-Band (UWB) technology has recently attracted much research interest due to its appealing features in short-range mobile communications. These features include high-data rates, low power consumption, multiple-access communications and precise positioning capabilities. Space-Time Coding (STC) techniques, such as block coding and trellis coding, are known to be simple and practical ways to increase both the spectral efficiency and the capacity in wireless communications. The the- sis aims at designing robust and efficient space-time coding schemes well adapted to single-band UWB signalling. Thus, this work incorporates a fine analysis of a stan- dard Single Input Single Output (SISO) single-band UWB system, scrutinising every important aspect of this system including transceiver structure, channel modelling, multiple-access techniques and detection process. Research also leads to the deriva- tion of a novel closed-form approximation for the average probability of bit-error for single-band UWB systems. This in-depth study highlights drawbacks inherent to UWB systems such as time-jitter effects or rake-receiver complexity and proposes schemes that benefit from spatial diversity to mitigate these problems. Thus, the thesis concentrates on the design of new multiple-antenna space-time coding systems tailored for UWB communications. As a result, this work derives and generates gen- eralised full-rate space-time block codes based on orthogonal pulses to capture both spatial and multipath diversities. Space-time trellis coded modulation is then revis- ited to further improve the spectral efficiency limit and to deliver the high-data rates promised by UWB technology. A new version of space-time trellis coding is developed for the peculiar UWB signalling structure. Finally, thanks to a novel closed-form ap- proximation, a theoretical comparison is undertaken between any SISO-UWB system and the multiple antenna UWB systems proposed in this thesis. The results clearly underline the impact of STC on a single-band UWB system in terms of enhanced robustness against timing-jitter effects, higher spectral efficiency and capacity im- provement. These advantages are finally confirmed through the numerical evaluation of the error-rate performance.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts