## Behavioral Modeling and Digital Predistortion of Radio Frequency Power Amplifiers (2018)

Complex Baseband Modeling and Digital Predistortion for Wideband RF Power Amplifiers

Modern modulation methods as used in 3rd generation mobile communications (UMTS) generate strongly fluctuating transmission signal envelopes with high peak-to-average power ratios. These properties result in significant distortion due to the nonlinear behavior of the radio-frequency power amplifier (RF PA). We propose different nonlinear model structures for such amplifiers, based on memory polynomials and frequency-domain Volterra kernel expansion, where we can reduce the number of free parameters by 80% compared to traditional Volterra series approaches. Because these nonlinear models incorporate memory, we are able to model the nonlinear distortion of RF PAs with sufficient accuracy (e.g., −30 dB relative modeling error ), including the wideband case (bandwidth B = 20 MHz as needed for four-carrier WCDMA). Furthermore, we propose a method to construct RF PA models from frequency-dependent AM/AM and AM/PM conversions. For the compensation of the nonlinearities, we analyze ...

Singerl, Peter — Graz University of Technology

Contributions to Analysis and DSP-based Mitigation of Nonlinear Distortion in Radio Transceivers

This thesis focuses on different nonlinear distortion aspects in radio transmitter and receivers. Such nonlinear distortion aspects are generally becoming more and more important as the communication waveforms themselves get more complex and thus more sensitive to any distortion. Also balancing between the implementation costs, size, power consumption and radio performance, especially in multiradio devices, creates tendency towards using lower cost, and thus lower quality, radio electronics. Furthermore, increasing requirements on radio flexibility, especially on receiver side, reduces receiver radio frequency (RF) selectivity and thus increases the dynamic range and linearity requirements. Thus overall, proper understanding of nonlinear distortion in radio devices is essential, and also opens the door for clever use of digital signal processing (DSP) in mitigating and suppressing such distortion effects. On the receiver side, the emphasis in this thesis is mainly on the analysis and DSP ...

Shahed hagh ghadam, Ali — Tampere University of Technology

Signal Processing for Energy-Efficient Burst-Mode RF Transmitters

Modern wireless communication systems utilize complex modulated signals such as OFDM signals to achieve increased data rates and spectral efficiency. These signals are characterized by a high peak-to-average-power ratio (PAPR). Thus, highly linear transmitters are required to provide sufficient transmission signal linearity. Conventional linear PAs, such as Class A or Class AB, produce high efficiency only near or at the peak output power region. As a result, the average efficiency is quite low for high PAPR signals. For non-portable devices such as base stations or mobile devices like mobile phones, low PA efficiency means higher heat dissipation which is often a design criterion. In addition, in mobile devices, a direct consequence of the low PA efficiency is the reduced battery lifetime, especially when the mobile device is required to operate at quite different output power levels. This thesis addresses the ...

Chi, Shuli — Signal Processing and Speech Communication Laboratory

Modeling and Digital Mitigation of Transmitter Imperfections in Radio Communication Systems

To satisfy the continuously growing demands for higher data rates, modern radio communication systems employ larger bandwidths and more complex waveforms. Furthermore, radio devices are expected to support a rich mixture of standards such as cellular networks, wireless local-area networks, wireless personal area networks, positioning and navigation systems, etc. In general, a "smart'' device should be flexible to support all these requirements while being portable, cheap, and energy efficient. These seemingly conflicting expectations impose stringent radio frequency (RF) design challenges which, in turn, call for their proper understanding as well as developing cost-effective solutions to address them. The direct-conversion transceiver architecture is an appealing analog front-end for flexible and multi-standard radio systems. However, it is sensitive to various circuit impairments, and modern communication systems based on multi-carrier waveforms such as Orthogonal Frequency Division Multiplexing (OFDM) and Orthogonal Frequency Division Multiple ...

Kiayani, Adnan — Tampere University of Technology

Digital Pre-distortion of Microwave Power Amplifiers

With the advent of spectrally efficient wireless communication systems employing modulation schemes with varying amplitude of the communication signal, linearisation techniques for nonlinear microwave power amplifiers have gained significant interest. The availability of fast and cheap digital processing technology makes digital pre-distortion an attractive candidate as a means for power amplifier linearisation since it promises high power efficiency and fleexibility. Digital pre-distortion is further in line with the current efforts towards software defined radio systems, where a principal aim is to substitute costly and inflexible analogue circuitry with cheap and reprogrammable digital circuitry. Microwave power amplifiers are most efficient in terms of delivered microwave output power vs. supplied power if driven near the saturation point. In this operational mode, the amplifier behaves as a nonlinear device, which introduces undesired distortions in the information bear- ing microwave signal. These nonlinear distortions ...

Aschbacher, E. — Vienna University of Technology

Stability of Coupled Adaptive Filters

Nowadays, many disciplines in science and engineering deal with problems for which a solution relies on knowledge about the characteristics of one or more given systems that can only be ascertained based on restricted observations. This requires the fitting of an adequately chosen model, such that it “best” conforms to a set of measured data. Depending on the context, this fitting procedure may resort to a huge amount of recorded data and abundant numerical power, or contrarily, to only a few streams of samples, which have to be processed on the fly at low computational cost. This thesis, exclusively focuses on the latter scenario. It specifically studies unexpected behaviour and reliability of the widely spread and computationally highly efficient class of gradient type algorithms. Additionally, special attention is paid to systems that combine several of them. Chapter 3 is dedicated ...

Dallinger, Robert — TU Wien

Modeling Analog to Digital Converters at Radio Frequency

This work considers behavior modeling of analog to digital converters with applications in the radio frequency range, including the field of telecommunication as well as test and measurement instrumentation, where the conversion from analog to digital signals often is a bottleneck in performance. The models are intended to post-process output data from the converter and thereby improve the performance of the digital signal. By building a model of practical converters and the way in which they deviate from ideal, imperfections can be corrected using post-correction methods. Behavior modeling implies generation of a suitable stimulus, capturing the output data, and characterizing a model. The demands on the test setup are high for converters in the radio frequency range. The test-bed used in this thesis is composed of commercial state-of-the-art instruments and components designed for signal conditioning and signal capture. Further, in ...

Björsell, Niclas — KTH, Signal Processing

Polynomial Predictive Filters: Implementation and Applications

In this thesis, smoothness of sampled real-world signals is exploited through the application of polynomial predictive filters. The principal reason for employing the polynomial signal model is principally twofold: firstly, assuming that the sampling rate is adequate, all real-world signals exhibit piecewise polynomial-like behavior, and secondly, polynomial-based signal processing is computationally efficient. By definition, polynomial predictive filters provide estimates of future values of polynomial-like signals. Thus, the potential applications of this research include a vast number of different delay sensitive operations on measurements like temperature, position, velocity, or power, especially in control engineering field. The polynomial-based predictive signal processing is a well-known technique, but polynomial-predictive filters have had severe drawbacks, which have hindered their application; their white noise attenuation is generally low, or they exhibit considerable passband gain peaks, rendering them unattractive for most applications. It has been possible to ...

Tanskanen, Jarno M. A. — Helsinki University of Technology

Measurement Methods for Estimating the Error Vector Magnitude in OFDM Transceivers

The error vector magnitude (EVM) is a standard metric to quantify the performance of digital communication systems and related building blocks. Regular EVM measurements require expensive equipment featuring inphase and quadrature (IQ) demodulation, wideband analog-to-digital converters (ADCs), and dedicated receiver algorithms to demodulate the data symbols. With modern high data rate communication standards that require high bandwidths and low amounts of error, it is difficult to avoid bias due to errors in the measurement chain. This thesis develops and discusses measurement methods that address the above-described issues with EVM measurements. The first method is an extension of the regular EVM, yielding two results from a single measurement. One result equals the regular EVM result, whereas the other excludes potential errors due to mismatches of the I- and Q- paths of direct conversion transmitters and receivers (IQ imbalance). This can be ...

Freiberger, Karl — Graz University of Technology

This dissertation is concerned with the development of Markov chain Monte Carlo (MCMC) methods for the Bayesian restoration of degraded audio signals. First, the Bayesian approach to time series modelling is reviewed, then established MCMC methods are introduced. The first problem to be addressed is that of model order uncertainty. A reversible-jump sampler is proposed which can move between models of different order. It is shown that faster convergence can be achieved by exploiting the analytic structure of the time series model. This approach to model order uncertainty is applied to the problem of noise reduction using the simulation smoother. The effects of incorrect autoregressive (AR) model orders are demonstrated, and a mixed model order MCMC noise reduction scheme is developed. Nonlinear time series models are surveyed, and the advantages of linear-in- the-parameters models explained. A nonlinear AR (NAR) model, ...

Troughton, Paul Thomas — University of Cambridge

Ultra-wideband (UWB) communication systems use radio signals with a bandwidth in the range of some hundred MHz to several GHz. Radio channels with dense multipath propagation achieve high multipath diversity, which can be used to improve the robustness and capacity of the communication channel. Furthermore the large bandwidth allows to transmit signals with a small power spectral density such that the interference to other radio signals will be negligible, even if they lie within the same frequency band. In this work the focus is on low-complexity receiver architectures for communication systems in presence of multiple-access interference (MAI). The main objective of this thesis is to develop and to study a framework for communications for transmitted reference (TR) UWB systems and energy detection UWB systems. First, we study the hybrid matched-filter (HMF) receiver for TR UWB systems, which employs matched filters ...

Jimmy Baringbing — Graz University of Technology

Information Loss in Deterministic Systems

A fundamental theorem in information theory – the data processing inequality – states that deterministic processing cannot increase the amount of information contained in a random variable or a stochastic process. The task of signal processing is to operate on the physical representation of information such that the intended user can access this information with little effort. In the light of the data processing inequality, this can be viewed as the task of removing irrelevant information, while preserving as much relevant information as possible. This thesis defines information loss for memoryless systems processing random variables or stochastic processes, both with and without a notion of relevance. These definitions are the basis of an information-theoretic systems theory, which complements the currently prevailing energy-centered approaches. The results thus developed are used to analyze various systems in the signal processor’s toolbox: polynomials, quantizers, ...

Geiger, Bernhard C. — Graz University of Technology

Contributions to Signal Processing for MRI

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties. Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information. In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued ...

Björk, Marcus — Uppsala University

Energy-Efficient Spectrum Sensing for Cognitive Radio Networks

Dynamic spectrum access employing cognitive radios has been proposed, in order to opportunistically use underutilized spectrum portions of a heavily licensed electromagnetic spectrum. Cognitive radios opportunistically share the spectrum, while avoiding any harmful interference to the primary licensed users. One major category of cognitive radios consists of is interweave cognitive radios. In this category, cognitive radios employ spectrum sensing to detect the empty bands of the radio spectrum, also known as spectrum holes. Upon detection of such a spectrum hole, cognitive radios dynamically share this empty band. However, as soon as the primary user appears in the corresponding band, cognitive radios have to vacate the band and look for a new spectrum hole. This way, reliable spectrum sensing becomes a key functionality of a cognitive radio network. The hidden terminal problem and fading effects have been shown to limit the ...

Maleki, Sina — TU Delft

Adaptive Digital Predistortion of Nonlinear Systems

Compensating or reducing the nonlinear distortion - usually resulting from a nonlinear system - is becoming an essential requirement in many areas. In this thesis adaptive digital predistortion techniques for a wide class of nonlinear systems are presented. For estimating the coefficients of the predistorter, different learning architectures are considered: the Direct Learning Architecture (DLA) and Indirect Learning Architecture (ILA). In the DLA approach, we propose a new adaptation algorithm - the Nonlinear Filtered-x Prediction Error Method (NFxPEM) algorithm, which has much faster convergence and much better performance compared to the conventional Nonlinear Filtered-x Least Mean Squares (NFxLMS) algorithm. All of these time domain adaptive algorithms require accurate system identification of the nonlinear system. In order to relax or avoid this strict requirement, the NFxLMS with Initial Subsystem Estimates (NFxLMS-ISE) and NFxPEM-ISE algorithms are proposed. Furthermore, we propose a frequency ...

Gan, Li — Graz University of Technology

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