Optimization Methods for Designing Sequences with Low Autocorrelation Sidelobes

Unimodular sequences with low autocorrelations are desired in many applications, especially in the area of radar and code-division multiple access (CDMA). In this paper, we propose a new algorithm to design unimodular sequences with low integrated sidelobe level (ISL), which is a widely used measure of the goodness of a sequence's correlation property. The algorithm falls into the general framework of majorization-minimization (MM) algorithms and thus shares the monotonic property of such algorithms. In addition, the algorithm can be implemented via fast Fourier transform (FFT) operations and thus is computationally efficient. Furthermore, after some modifications the algorithm can be adapted to incorporate spectral constraints, which makes the design more flexible. Numerical experiments show that the proposed algorithms outperform existing algorithms in terms of both the quality of designed sequences and the computational complexity

Design of Waveform Set for Multiuser Ultra-Wideband Communications

The thesis investigates the design of analogue waveform sets for multiuser and UWB communications using suitably chosen Hermite-Rodriguez basis functions. The non-linear non-convex optimization problem with time and frequency domains constraints has been transformed into suitable forms and then solved using a standard optimization package. The proposed approach is more flexible and efficient than existing approaches in the literature. Numerical results show that orthogonal waveform sets with high spectral efficiency can be produced

Attacking Spectrum Sensing With Adversarial Deep Learning in Cognitive Radio-Enabled Internet of Things

Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR-IoT network, which has been investigated extensively based on deep learning (DL). Despite the unique advantages of DL in spectrum sensing, the black-box and unexplained properties of deep neural networks may lead to many security risks. This article considers the fusion of traditional interference methods and data poisoning which is an attack method on the training data of a machine learning tool. We propose a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems. We introduce a novel design of jamming waveform whose interference capability is reinforced by data poisoning. Simulation results show that significant performance enhancement and higher mobility can be achieved compared with traditional white-box attack methods

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

Sequence Design for Cognitive CDMA Communications under Arbitrary Spectrum Hole Constraint

To support interference-free quasi-synchronous code-division multiple-access (QS-CDMA) communication with low spectral density profile in a cognitive radio (CR) network, it is desirable to design a set of CDMA spreading sequences with zero-correlation zone (ZCZ) property. However, traditional ZCZ sequences (which assume the availability of the entire spectral band) cannot be used because their orthogonality will be destroyed by the spectrum hole constraint in a CR channel. To date, analytical construction of ZCZ CR sequences remains open. Taking advantage of the Kronecker sequence property, a novel family of sequences (called "quasi-ZCZ" CR sequences) which displays zero cross-correlation and near-zero auto-correlation zone property under arbitrary spectrum hole constraint is presented in this paper. Furthermore, a novel algorithm is proposed to jointly optimize the peak-to-average power ratio (PAPR) and the periodic auto-correlations of the proposed quasi-ZCZ CR sequences. Simulations show that they give rise to single-user bit-error-rate performance in CR-CDMA systems which outperform traditional non-contiguous multicarrier CDMA and transform domain communication systems; they also lead to CR-CDMA systems which are more resilient than non-contiguous OFDM systems to spectrum sensing mismatch, due to the wideband spreading.Comment: 13 pages,10 figures,Accepted by IEEE Journal on Selected Areas in Communications (JSAC)--Special Issue:Cognitive Radio Nov, 201

Adaptive waveform design for SAR in a crowded spectrum

This thesis concerns the development of an adaptive waveform design scheme for synthetic aperture radar (SAR) to support its operation in the increasingly crowded radio frequency (RF) spectrum, focusing on mitigating the effects of external RF interference. The RF spectrum is a finite resource and the rapid expansion of the telecommunications industry has seen radar users face a significant restriction in the range of available operational frequencies. This crowded spectrum scenario leads to increased likelihood of RF interference either due to energy leakage from neighbouring spectral users or from unlicensed transmitters. SAR is a wide bandwidth radar imaging mode which exploits the motion of the radar platform to form an image using multiple one dimensional profiles of the scene of interest known as the range profile. Due to its wideband nature, SAR is particularly vulnerable to RF interference which causes image impairments and overall reduction in quality. Altering the approach for radar energy transmission across the RF spectrum is now imperative to continue effective operation. Adaptive waveforms have recently become feasible for implementation and offer the much needed flexibility in the choice and control over radar transmission. However, there is a critically small processing time frame between waveform reception and transmission, which necessitates the use of computationally efficient processing algorithms to use adaptivity effectively. This simulation-based study provides a first look at adaptive waveform design for SAR to mitigate the detrimental effects of RF interference on a pulse-to-pulse basis. Standard SAR systems rely on a fixed waveform processing format on reception which restricts its potential to reap the benefits of adaptive waveform design. Firstly, to support waveform design for SAR, system identification techniques are applied to construct an alternative receive processing method which allows flexibility in waveform type. This leads to the main contribution of the thesis which is the formation of an adaptive spectral waveform design scheme. A computationally efficient closed-form expression for the waveform spectrum that minimizes the error in the estimate of the SAR range profile on a pulse to pulse basis is derived. The range profile and the spectrum of the interference are estimated at each pulse. The interference estimate is then used to redesign the proceeding waveform for estimation of the range profile at the next radar platform position. The solution necessitates that the energy is spread across the spectrum such that it competes with the interferer. The scenario where the waveform admits gaps in the spectrum in order to mitigate the effects of the interference is also detailed and is the secondary major thesis contribution. A series of test SAR images demonstrate the efficacy of these techniques and yield reduced interference effects compared to the standard SAR waveform

New Spectrally Constrained Sequence Sets With Optimal Periodic Cross-Correlation

Spectrally constrained sequences (SCSs) play an important role in modern communication and radar systems operating over non-contiguous spectrum. Despite numerous research attempts over the past years, very few works are known on the constructions of optimal SCSs with low cross-correlations. In this paper, we address such a major problem by introducing a unifying framework to construct unimodular SCS families using circular Florentine rectangles (CFRs) and interleaving techniques. By leveraging the uniform power allocation in the frequency domain for all the admissible carriers (a necessary condition for beating the existing periodic correlation lower bound of SCSs), we present a tighter correlation lower bound and show that it is achievable by our proposed SCS families including multiple SCS sets with zero correlation zone properties