544 research outputs found
State–of–the–art report on nonlinear representation of sources and channels
This report consists of two complementary parts, related to the modeling of two important sources of nonlinearities in a communications system. In the first part, an overview of important past work related to the estimation, compression and processing of sparse data through the use of nonlinear models is provided. In the second part, the current state of the art on the representation of wireless channels in the presence of nonlinearities is summarized. In addition to the characteristics of the nonlinear wireless fading channel, some information is also provided on recent approaches to the sparse representation of such channels
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Signal Processing in Wireless Communications: Device Fingerprinting and Wide-Band Interference Rejection
The rapid progress of wireless communication technologies that has taken place in recent years has significantly improved the quality of everyday life. However with this expansion of wireless communication systems come significant security threats and significant technological challenges, both of which are due to the fact that the communication medium is shared. The ubiquity of open wireless Internet access networks creates a new avenue for cyber-criminals to impersonate and act in an unauthorized way. The increasing number of deployed wide-band wireless communication systems entails technological challenges for effective utilization of the shared medium, which implies the need for advanced interference rejection methods. Wireless security and interference rejection in wide-band wireless communications are therefore often considered as the two main challenges in wireless network\u27s design and research. Important aspects of these challenges are illuminated and addressed in this dissertation.
This dissertation considers signal processing approaches for exploiting or mitigating the effects of non-ideal components in wireless communication systems. In the first part of the dissertation, we introduce and study a novel, model-based approach to wireless device identification that exploits imperfections in the transmitter caused by manufacturing process nonidealities. Previous approaches to device identification based on hardware imperfections vary from transient analysis to machine learning but have not provided verifiable accuracy. Here, we detail a model-based approach, that uses statistical models of RF transmitter components: digital-to-analog converter, power amplifier and RF oscillator, which are amenable for analysis. Our proposed approach examines the key device characteristics that cause anonymity loss, countermeasures that can be applied by the nodes to regain the anonymity, and ways of thwarting such countermeasures. We develop identification algorithms based on statistical signal processing methods and address the challenging scenario when the units that need to be distinguished from one another are of the same model and from the same manufacturer. Using simulations and measurements of components that are commonly used in commercial communications systems, we show that our anonymity breaking techniques are effective.
In the second part of the dissertation, we consider innovative approaches for the acquisition of frequency-sparse signals with wide-band receivers when a weak signal of interest is received in the presence of a very strong interference, and the effects of the nonlinearities in the low-noise amplifier at the receiver must be mitigated. All samples with amplitude above a given threshold, dictated by the linear input range of the receiver, are discarded to avoid the distortion caused by saturation of the low noise amplifier. Such a sampling scheme, while avoiding nonlinear distortion that cannot be corrected in the digital domain, poses challenges for signal reconstruction techniques, as the samples are taken non-uniformly, but also non-randomly. The considered approaches fall into the field of compressive sensing (CS); however, what differentiates them from conventional CS is that a structure is forced upon the measurement scheme. Such a structure causes a violation of the core CS assumption of the measurements\u27 randomness. We consider two different types of structured acquisition: signal independent and signal dependent structured acquisition. For the first case, we derive bounds on the number of samples needed for successful CS recovery when samples are drawn at random in predefined groups. For the second case, we consider enhancements of CS recovery methods when only small-amplitude samples of the signal that needs to be recovered are available for the recovery. Finally, we address a problem of spectral leakage due to the limited processing block size of block processing, wide-band receivers and propose an adaptive block size adjustment method, which leads to significant dynamic range improvements
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
Mitigation of nonlinear receiver effects in modern radar: advanced signal processing techniques
This thesis presents a study into nonlinearities in the radar receiver and investigates
advanced digital signal processing (DSP) techniques capable of mitigating the resultant
deleterious effects. The need for these mitigation techniques has become more prevalent
as the use of commercial radar sensors has increased rapidly over the last decade. While
advancements in low-cost radio frequency (RF) technologies have made mass-produced
radar systems more feasible, they also pose a significant risk to the functionality of the
sensor. One of the major compromises when employing low-cost commercial off-theshelf
(COTS) components in the radar receiver is system linearity. This linearity trade-off
leaves the radar susceptible to interfering signals as the RF receiver can now be driven
into the weakly nonlinear regime. Radars are not designed to operate in the nonlinear
regime as distortion is observed in the radar output if they do. If radars are to maintain
operational performance in an RF environment that is becoming increasingly crowded,
novel techniques that allow the sensor to operate in the nonlinear regime must be developed.
Advanced DSP techniques offer a low-cost low-impact solution to the nonlinear
receiver problem in modern radar. While there is very little work published on this topic
in the radar literature, inspiration can be taken from the related field of communications
where techniques have been successfully employed.
It is clear from the communications literature that for any mitigation algorithm to be
successful, the mechanisms driving the nonlinear distortion in the receiver must be understood
in great detail. Therefore, a behavioural modelling technique capable of capturing
both the nonlinear amplitude and phase effects in the radar receiver is presented before
any mitigation techniques are studied. Two distinct groups of mitigation algorithms
are then developed specifically for radar systems with their performance tested in the
medium pulse repetition frequency (MPRF) mode of operation. The first of these is the
look-up table (LUT) approach which has the benefit of being mode independent and computationally
inexpensive to implement. The limitations of this communications-based
technique are discussed with particular emphasis placed on its performance against receiver
nonlinearities that exhibit complex nonlinear memory effects. The second group
of mitigation algorithms to be developed is the forward modelling technique. While this
novel technique is both mode dependent and computationally intensive to implement,
it has a unique formalisation that allows it to be extended to include nonlinear memory
effects in a well-defined manner. The performance of this forward modelling technique
is analysed and discussed in detail.
It was shown in this study that nonlinearities generated in the radar receiver can be
successfully mitigated using advanced DSP techniques. For this to be the case however,
the behaviour of the RF receiver must be characterised to a high degree of accuracy both
in the linear and weakly nonlinear regimes. In the case where nonlinear memory effects
are significant in the radar receiver, it was shown that memoryless mitigation techniques
can become decorrelated drastically reducing their effectiveness. Importantly however, it
was demonstrated that the LUT and forward modelling techniques can both be extended
to compensate for complex nonlinear memory effects generated in the RF receiver. It was
also found that the forward modelling technique dealt with the nonlinear memory effects
in a far more robust manner than the LUT approach leading to a superior mitigation
performance in the memory rich case
WAVEFORM AND TRANSCEIVER OPTIMIZATION FOR MULTI-FUNCTIONAL AIRBORNE RADAR THROUGH ADAPTIVE PROCESSING
Pulse compression techniques have been widely used for target detection and remote sensing. The primary concern for pulse compression is the sidelobe interference. Waveform design is an important method to improve the sidelobe performance. As a multi-functional aircraft platform in aviation safety domain, ADS-B system performs functions involving detection, localization and alerting of external traffic. In this work, a binary phase modulation is introduced to convert the original 1090 MHz ADS-B signal waveform into a radar signal. Both the statistical and deterministic models of new waveform are developed and analyzed. The waveform characterization, optimization and its application are studied in details. An alternative way to achieve low sidelobe levels without trading o range resolution and SNR is the adaptive pulse compression - RMMSE (Reiterative Minimum Mean-Square error). Theoretically, RMMSE is able to suppress the sidelobe level down to the receiver noise floor. However, the application of RMMSE to actual radars and the related implementation issues have not been investigated before. In this work, implementation aspects of RMMSE such as waveform sensitivity, noise immunity and computational complexity are addressed. Results generated by applying RMMSE to both simulated and measured radar data are presented and analyzed. Furthermore, a two-dimensional RMMSE algorithm is derived to mitigate the sidelobe effects from both pulse compression processing and antenna radiation pattern. In addition, to achieve even better control of the sidelobe level, a joint transmit and receive optimization scheme (JTRO) is proposed, which reduces the impacts of HPA nonlinearity and receiver distortion. Experiment results obtained with a Ku-band spaceborne radar transceiver testbed are presented
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