Dirty RF Signal Processing for Mitigation of Receiver Front-end Non-linearity

Moderne drahtlose Kommunikationssysteme stellen hohe und teilweise gegensätzliche Anforderungen an die Hardware der Funkmodule, wie z.B. niedriger Energieverbrauch, große Bandbreite und hohe Linearität. Die Gewährleistung einer ausreichenden Linearität ist, neben anderen analogen Parametern, eine Herausforderung im praktischen Design der Funkmodule. Der Fokus der Dissertation liegt auf breitbandigen HF-Frontends für Software-konfigurierbare Funkmodule, die seit einigen Jahren kommerziell verfügbar sind. Die praktischen Herausforderungen und Grenzen solcher flexiblen Funkmodule offenbaren sich vor allem im realen Experiment. Eines der Hauptprobleme ist die Sicherstellung einer ausreichenden analogen Performanz über einen weiten Frequenzbereich. Aus einer Vielzahl an analogen Störeffekten behandelt die Arbeit die Analyse und Minderung von Nichtlinearitäten in Empfängern mit direkt-umsetzender Architektur. Im Vordergrund stehen dabei Signalverarbeitungsstrategien zur Minderung nichtlinear verursachter Interferenz - ein Algorithmus, der besser unter "Dirty RF"-Techniken bekannt ist. Ein digitales Verfahren nach der Vorwärtskopplung wird durch intensive Simulationen, Messungen und Implementierung in realer Hardware verifiziert. Um die Lücken zwischen Theorie und praktischer Anwendbarkeit zu schließen und das Verfahren in reale Funkmodule zu integrieren, werden verschiedene Untersuchungen durchgeführt. Hierzu wird ein erweitertes Verhaltensmodell entwickelt, das die Struktur direkt-umsetzender Empfänger am besten nachbildet und damit alle Verzerrungen im HF- und Basisband erfasst. Darüber hinaus wird die Leistungsfähigkeit des Algorithmus unter realen Funkkanal-Bedingungen untersucht. Zusätzlich folgt die Vorstellung einer ressourceneffizienten Echtzeit-Implementierung des Verfahrens auf einem FPGA. Abschließend diskutiert die Arbeit verschiedene Anwendungsfelder, darunter spektrales Sensing, robuster GSM-Empfang und GSM-basiertes Passivradar. Es wird gezeigt, dass nichtlineare Verzerrungen erfolgreich in der digitalen Domäne gemindert werden können, wodurch die Bitfehlerrate gestörter modulierter Signale sinkt und der Anteil nichtlinear verursachter Interferenz minimiert wird. Schließlich kann durch das Verfahren die effektive Linearität des HF-Frontends stark erhöht werden. Damit wird der zuverlässige Betrieb eines einfachen Funkmoduls unter dem Einfluss der Empfängernichtlinearität möglich. Aufgrund des flexiblen Designs ist der Algorithmus für breitbandige Empfänger universal einsetzbar und ist nicht auf Software-konfigurierbare Funkmodule beschränkt.Today's wireless communication systems place high requirements on the radio's hardware that are largely mutually exclusive, such as low power consumption, wide bandwidth, and high linearity. Achieving a sufficient linearity, among other analogue characteristics, is a challenging issue in practical transceiver design. The focus of this thesis is on wideband receiver RF front-ends for software defined radio technology, which became commercially available in the recent years. Practical challenges and limitations are being revealed in real-world experiments with these radios. One of the main problems is to ensure a sufficient RF performance of the front-end over a wide bandwidth. The thesis covers the analysis and mitigation of receiver non-linearity of typical direct-conversion receiver architectures, among other RF impairments. The main focus is on DSP-based algorithms for mitigating non-linearly induced interference, an approach also known as "Dirty RF" signal processing techniques. The conceived digital feedforward mitigation algorithm is verified through extensive simulations, RF measurements, and implementation in real hardware. Various studies are carried out that bridge the gap between theory and practical applicability of this approach, especially with the aim of integrating that technique into real devices. To this end, an advanced baseband behavioural model is developed that matches to direct-conversion receiver architectures as close as possible, and thus considers all generated distortions at RF and baseband. In addition, the algorithm's performance is verified under challenging fading conditions. Moreover, the thesis presents a resource-efficient real-time implementation of the proposed solution on an FPGA. Finally, different use cases are covered in the thesis that includes spectrum monitoring or sensing, GSM downlink reception, and GSM-based passive radar. It is shown that non-linear distortions can be successfully mitigated at system level in the digital domain, thereby decreasing the bit error rate of distorted modulated signals and reducing the amount of non-linearly induced interference. Finally, the effective linearity of the front-end is increased substantially. Thus, the proper operation of a low-cost radio under presence of receiver non-linearity is possible. Due to the flexible design, the algorithm is generally applicable for wideband receivers and is not restricted to software defined radios

Channel Equalization using GA Family

High speed data transmissions over communication channels distort the trans- mitted signals in both amplitude and phase due to presence of Inter Symbol Inter- ference (ISI). Other impairments like thermal noise, impulse noise and cross talk also cause further distortions to the received symbols. Adaptive equalization of the digital channels at the receiver removes/reduces the e®ects of such ISIs and attempts to recover the transmitted symbols. Basically an equalizer is an inverse ¯lter which is placed at the front end of the receiver. Its transfer function is inverse to the transfer function of the associated channel. The Least-Mean-Square (LMS), Recursive-Least-Square (RLS) and Multilayer perceptron (MLP) based adaptive equalizers aim to minimize the ISI present in the digital communication channel. These are gradient based learning algorithms and therefore there is possibility that during training of the equalizers, its weights do not reach to their optimum values due to ..

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

An investigation of the utility of monaural sound source separation via nonnegative matrix factorization applied to acoustic echo and reverberation mitigation for hands-free telephony

In this thesis we investigate the applicability and utility of Monaural Sound Source Separation (MSSS) via Nonnegative Matrix Factorization (NMF) for various problems related to audio for hands-free telephony. We first investigate MSSS via NMF as an alternative acoustic echo reduction approach to existing approaches such as Acoustic Echo Cancellation (AEC). To this end, we present the single-channel acoustic echo problem as an MSSS problem, in which the objective is to extract the users signal from a mixture also containing acoustic echo and noise. To perform separation, NMF is used to decompose the near-end microphone signal onto the union of two nonnegative bases in the magnitude Short Time Fourier Transform domain. One of these bases is for the spectral energy of the acoustic echo signal, and is formed from the in- coming far-end user’s speech, while the other basis is for the spectral energy of the near-end speaker, and is trained with speech data a priori. In comparison to AEC, the speaker extraction approach obviates Double-Talk Detection (DTD), and is demonstrated to attain its maximal echo mitigation performance immediately upon initiation and to maintain that performance during and after room changes for similar computational requirements. Speaker extraction is also shown to introduce distortion of the near-end speech signal during double-talk, which is quantified by means of a speech distortion measure and compared to that of AEC. Subsequently, we address Double-Talk Detection (DTD) for block-based AEC algorithms. We propose a novel block-based DTD algorithm that uses the available signals and the estimate of the echo signal that is produced by NMF-based speaker extraction to compute a suitably normalized correlation-based decision variable, which is compared to a fixed threshold to decide on doubletalk. Using a standard evaluation technique, the proposed algorithm is shown to have comparable detection performance to an existing conventional block-based DTD algorithm. It is also demonstrated to inherit the room change insensitivity of speaker extraction, with the proposed DTD algorithm generating minimal false doubletalk indications upon initiation and in response to room changes in comparison to the existing conventional DTD. We also show that this property allows its paired AEC to converge at a rate close to the optimum. Another focus of this thesis is the problem of inverting a single measurement of a non- minimum phase Room Impulse Response (RIR). We describe the process by which percep- tually detrimental all-pass phase distortion arises in reverberant speech filtered by the inverse of the minimum phase component of the RIR; in short, such distortion arises from inverting the magnitude response of the high-Q maximum phase zeros of the RIR. We then propose two novel partial inversion schemes that precisely mitigate this distortion. One of these schemes employs NMF-based MSSS to separate the all-pass phase distortion from the target speech in the magnitude STFT domain, while the other approach modifies the inverse minimum phase filter such that the magnitude response of the maximum phase zeros of the RIR is not fully compensated. Subjective listening tests reveal that the proposed schemes generally produce better quality output speech than a comparable inversion technique

Integrated Self-Interference Cancellation for Full-Duplex and Frequency-Division Duplexing Wireless Communication Systems

From wirelessly connected robots to car-to-car communications, and to smart cities, almost every aspect of our lives will benefit from future wireless communications. While promise an exciting future world, next-generation wireless communications impose requirements on the data rate, spectral efficiency, and latency (among others) that are higher than those for today's systems by several orders of magnitude. Full-duplex wireless, an emergent wireless communications paradigm, breaks the long-held assumption that it is impossible for a wireless device to transmit and receive simultaneously at the same frequency, and has the potential to immediately double network capacity at the physical (PHY) layer and offers many other benefits (such as reduced latency) at the higher layers. Recently, discrete-component-based demonstrations have established the feasibility of full-duplex wireless. However, the realization of integrated full duplex radios, compact radios that can fit into smartphones, is fraught with fundamental challenges. In addition, to unleash the full potential of full-duplex communication, a careful redesign of the PHY layer and the medium access control (MAC) layer using a cross-layer approach is required. The biggest challenge associated with full duplex wireless is the tremendous amount of transmitter self-interference right on top of the desired signal. In this dissertation, new self-interference-cancellation approaches at both system and circuit levels are presented, contributing towards the realization of full-duplex radios using integrated circuit technology. Specifically, these new approaches involve elimination of the noise and distortion of the cancellation circuitry, enhancing the integrated cancellation bandwidth, and performing joint radio frequency, analog, and digital cancellation to achieve cancellation with nearly one part-per-billion accuracy. In collaboration with researchers at higher layers of the stack, a cross-layer approach has been used in our full-duplex research and has allowed us to derive power allocation algorithms and to characterize rate-gain improvements for full-duplex wireless networks. To enable experimental characterization of full-duplex MAC layer algorithms, a cross-layered software-defined full-duplex radio testbed has been developed. In collaboration with researchers from the field of micro-electro-mechanical systems, we demonstrate a multi-band frequency-division duplexing system using a cavity-filter-based tunable duplexer and our integrated widely-tunable self-interference-cancelling receiver

New Methods for Analysis of Nonlinear Systems in the Frequency Domain with Applications in Condition Monitoring and Engineering Systems

The study of nonlinear systems has received great attention in recent years because of the necessity of dealing with practical problems that cannot be modelled by linear representations. Although the availability of greater computational power and advances in the field of system identification have allowed significant progresses towards modelling real world processes, a systematic method for understanding the systems characteristics is still an open problem. In this context, as has been demonstrated in many studies, the extension of the well-known concept of linear Frequency Response Function (FRF) to nonlinear systems are a significant potential solution. The condition monitoring problem is closely associated with the analysis of systems characteristics and can therefore be considered as part of this scenario. Modern industrial processes have grown significantly in both size and complexity, creating the demand for automatic systems that can aid human operators in the important task of recognising when the process is experiencing malfunctions. Although this problem has been studied from the perspective of a wide scope of disciplines, such as modelling, signal processing, intelligent systems and statistical analysis, in many cases, data oriented methods or generic problem solvers (such as neural networks) often have to be applied. This is because complicated system behaviours are often difficult to interpret so as to associate them with possible faulty conditions.nonlinear system analysis in the frequency domain, and studies the application of these new methods for solving condition monitoring problems. The principle is based on the idea that a nonlinear system formulation can be used to deal with situations of practical interest where nonlinear behaviour cannot be neglected and that the frequency domain analysis approach can be applied to conduct an in-depth study of the system properties for the purpose of characterising systems faulty behaviours. In order to apply this principle, several issues need to be addressed, including the evaluation of the frequency characteristics of nonlinear systems and the generation of useful features that allow an effective characterisation of faulty system conditions. Motivated by these needs, the following research studies are conducted in this thesis: In order to address these challenges, this thesis proposes new methods for nonlinear system analysis in the frequency domain, and studies the application of these new methods for solving condition monitoring problems. The principle is based on the idea that a nonlinear system formulation can be used to deal with situations of practical interest where nonlinear behaviour cannot be neglected and that the frequency domain analysis approach can be applied to conduct an in-depth study of the system properties for the purpose of characterising systems faulty behaviours. In order to apply this principle, several issues need to be addressed, including the evaluation of the frequency characteristics of nonlinear systems and the generation of useful features that allow an effective characterisation of faulty system conditions. Motivated by these needs, the following research studies are conducted in this thesis: 1 - Development of new methods that allow an eficient extraction of the frequency domain representations of nonlinear systems, namely, Generalised Frequency Response Functions (GFRFs) and Nonlinear Output Frequency Response Functions (NOFRFs). The thesis first derives a comprehensive methodology that allows an efficient and systematic extraction of GFRFs from a polynomial NARX (Nonlinear Auto-Regressive with eXogenous inputs) model. Then the same idea is used for addressing issues regarding the computation of NOFRFs, providing efficient algorithms that allow an effective determination of the NORRFs in both numerical and analytical forms. 2- Establishment of a condition monitoring framework based on the new GFRFs/NOFRFs evaluation methods. This framework is constructed over a practical background where physical knowledge about the system is scarce, although process history data is available. In this context, black-box models can be built and the system properties can be extracted by computing the system's GFRFs/NOFRFs via the newly proposed methods. These functions provide fundamental information for deriving useful features that can be used for characterising faults and building effective diagnosis systems. The effectiveness of the proposed methods has been verified by both simulation studies and real data analysis tests, demonstrating the advantage of the new condition monitoring framework for engineering applications. These studies significantly improve current frequency analysis methods for nonlinear systems and, at the same time, provide effective condition monitoring approaches for a wide range of engineering systems

Nonlinear processing of non-Gaussian stochastic and chaotic deterministic time series

It is often assumed that interference or noise signals are Gaussian stochastic processes. Gaussian noise models are appealing as they usually result in noise suppression algorithms that are simple: i.e. linear and closed form. However, such linear techniques may be sub-optimal when the noise process is either a non-Gaussian stochastic process or a chaotic deterministic process. In the event of encountering such noise processes, improvements in noise suppression, relative to the performance of linear methods, may be achievable using nonlinear signal processing techniques. The application of interest for this thesis is maritime surveillance radar, where the main source of interference, termed sea clutter, is widely accepted to be a non-Gaussian stochastic process at high resolutions and/or at low grazing angles. However, evidence has been presented during the last decade which suggests that sea clutter may be better modelled as a chaotic deterministic process. While the debate over which model is more suitable continues, this thesis investigates whether nonlinear processing techniques can be used to improve the performance of maritime surveillance radar, relative to the performance achievable using linear techniques. Linear and nonlinear prediction of chaotic signals, sea clutter data sets, and stochastic surrogate clutter data sets is carried out. Volterra series filter networks and radial basis function networks are used to implement nonlinear predictors. A novel structure for a forward-backward nonlinear predictor, using a radial basis function network, is presented. Prediction results provide evidence to support the view that sea clutter is better modelled as a stochastic process, rather than as a chaotic process. The clutter data sets are shown to have linear predictor functions. Linear and nonlinear predictors are used as the basis of target detection algorithms. The performance of these predictor-detectors, against backgrounds of sea clutter data and against a background of chaotic noise data is evaluated. The detection results show that linear predictor-detectors perform as well as, or better than, nonlinear predictor-detectors against the non-Gaussian clutter backgrounds considered in this thesis, whilst the reverse is true for a background of chaotic noise. An existing, nonlinear inverse, noise cancellation technique, referred to as Broomhead’s filtering technique in this thesis, is re-investigated using a sine wave corrupted by broadband chaotic noise. It is demonstrated that significant improvements can be obtained using this nonlinear inverse technique, relative to results obtained using linear alternatives, despite recent work which suggested otherwise. A novel bandstop filtering approach is applied to Broomhead’s filtering method, which allows the technique to be applied to the cancellation of signals with a band of interest greater than that of a sine wave. This modified Broomhead filtering technique is shown to cancel broadband chaotic noise from a narrowband Gaussian signal better than alternative linear methods. The modified Broomhead filtering technique is shown to only perform as well as, o

Bayesian learning of continuous time dynamical systems with applications in functional magnetic resonance imaging

Temporal phenomena in a range of disciplines are more naturally modelled in continuous-time than coerced into a discrete-time formulation. Differential systems form the mainstay of such modelling, in fields from physics to economics, geoscience to neuroscience. While powerful, these are fundamentally limited by their determinism. For the purposes of probabilistic inference, their extension to stochastic differential equations permits a continuous injection of noise and uncertainty into the system, the model, and its observation. This thesis considers Bayesian filtering for state and parameter estimation in general non-linear, non-Gaussian systems using these stochastic differential models. It identifies a number of challenges in this setting over and above those of discrete time, most notably the absence of a closed form transition density. These are addressed via a synergy of diverse work in numerical integration, particle filtering and high performance distributed computing, engineering novel solutions for this class of model. In an area where the default solution is linear discretisation, the first major contribution is the introduction of higher-order numerical schemes, particularly stochastic Runge-Kutta, for more efficient simulation of the system dynamics. Improved runtime performance is demonstrated on a number of problems, and compatibility of these integrators with conventional particle filtering and smoothing schemes discussed. Finding compatibility for the smoothing problem most lacking, the major theoretical contribution of the work is the introduction of two novel particle methods, the kernel forward-backward and kernel two-filter smoothers. By harnessing kernel density approximations in an importance sampling framework, these attain cancellation of the intractable transition density, ensuring applicability in continuous time. The use of kernel estimators is particularly amenable to parallelisation, and provides broader support for smooth densities than a sample-based representation alone, helping alleviate the well known issue of degeneracy in particle smoothers. Implementation of the methods for large-scale problems on high performance computing architectures is provided. Achieving improved temporal and spatial complexity, highly favourable runtime comparisons against conventional techniques are presented. Finally, attention turns to real world problems in the domain of Functional Magnetic Resonance Imaging (fMRI), first constructing a biologically motivated stochastic differential model of the neural and hemodynamic activity underlying the observed signal in fMRI. This model and the methodological advances of the work culminate in application to the deconvolution and effective connectivity problems in this domain