ワイヤレス通信のための先進的な信号処理技術を用いた非線形補償法の研究

The inherit nonlinearity in analogue front-ends of transmitters and receivers have had primary impact on the overall performance of the wireless communication systems, as it gives arise of substantial distortion when transmitting and processing signals with such circuits. Therefore, the nonlinear compensation (linearization) techniques become essential to suppress the distortion to an acceptable extent in order to ensure sufficient low bit error rate. Furthermore, the increasing demands on higher data rate and ubiquitous interoperability between various multi-coverage protocols are two of the most important features of the contemporary communication system. The former demand pushes the communication system to use wider bandwidth and the latter one brings up severe coexistence problems. Having fully considered the problems raised above, the work in this Ph.D. thesis carries out extensive researches on the nonlinear compensations utilizing advanced digital signal processing techniques. The motivation behind this is to push more processing tasks to the digital domain, as it can potentially cut down the bill of materials (BOM) costs paid for the off-chip devices and reduce practical implementation difficulties. The work here is carried out using three approaches: numerical analysis & computer simulations; experimental tests using commercial instruments; actual implementation with FPGA. The primary contributions for this thesis are summarized as the following three points: 1) An adaptive digital predistortion (DPD) with fast convergence rate and low complexity for multi-carrier GSM system is presented. Albeit a legacy system, the GSM, however, has a very strict requirement on the out-of-band emission, thus it represents a much more difficult hurdle for DPD application. It is successfully implemented in an FPGA without using any other auxiliary processor. A simplified multiplier-free NLMS algorithm, especially suitable for FPGA implementation, for fast adapting the LUT is proposed. Many design methodologies and practical implementation issues are discussed in details. Experimental results have shown that the DPD performed robustly when it is involved in the multichannel transmitter. 2) The next generation system (5G) will unquestionably use wider bandwidth to support higher throughput, which poses stringent needs for using high-speed data converters. Herein the analog-to-digital converter (ADC) tends to be the most expensive single device in the whole transmitter/receiver systems. Therefore, conventional DPD utilizing high-speed ADC becomes unaffordable, especially for small base stations (micro, pico and femto). A digital predistortion technique utilizing spectral extrapolation is proposed in this thesis, wherein with band-limited feedback signal, the requirement on ADC speed can be significantly released. Experimental results have validated the feasibility of the proposed technique for coping with band-limited feedback signal. It has been shown that adequate linearization performance can be achieved even if the acquisition bandwidth is less than the original signal bandwidth. The experimental results obtained by using LTE-Advanced signal of 320 MHz bandwidth are quite satisfactory, and to the authors’ knowledge, this is the first high-performance wideband DPD ever been reported. 3) To address the predicament that mobile operators do not have enough contiguous usable bandwidth, carrier aggregation (CA) technique is developed and imported into 4G LTE-Advanced. This pushes the utilization of concurrent dual-band transmitter/receiver, which reduces the hardware expense by using a single front-end. Compensation techniques for the respective concurrent dual-band transmitter and receiver front-ends are proposed to combat the inter-band modulation distortion, and simultaneously reduce the distortion for the both lower-side band and upper-side band signals.電気通信大学201

Sparse Nonlinear MIMO Filtering and Identification

In this chapter system identification algorithms for sparse nonlinear multi input multi output (MIMO) systems are developed. These algorithms are potentially useful in a variety of application areas including digital transmission systems incorporating power amplifier(s) along with multiple antennas, cognitive processing, adaptive control of nonlinear multivariable systems, and multivariable biological systems. Sparsity is a key constraint imposed on the model. The presence of sparsity is often dictated by physical considerations as in wireless fading channel-estimation. In other cases it appears as a pragmatic modelling approach that seeks to cope with the curse of dimensionality, particularly acute in nonlinear systems like Volterra type series. Three dentification approaches are discussed: conventional identification based on both input and output samples, semi–blind identification placing emphasis on minimal input resources and blind identification whereby only output samples are available plus a–priori information on input characteristics. Based on this taxonomy a variety of algorithms, existing and new, are studied and evaluated by simulation

Equalization and detection for digital communication over nonlinear bandlimited satellite communication channels

This dissertation evaluates receiver-based methods for mitigating the effects due to nonlinear bandlimited signal distortion present in high data rate satellite channels. The effects of the nonlinear bandlimited distortion is illustrated for digitally modulated signals. A lucid development of the low-pass Volterra discrete time model for a nonlinear communication channel is presented. In addition, finite-state machine models are explicitly developed for a nonlinear bandlimited satellite channel. A nonlinear fixed equalizer based on Volterra series has previously been studied for compensation of noiseless signal distortion due to a nonlinear satellite channel. This dissertation studies adaptive Volterra equalizers on a downlink-limited nonlinear bandlimited satellite channel. We employ as figure of merits performance in the mean-square error and probability of error senses. In addition, a receiver consisting of a fractionally-spaced equalizer (FSE) followed by a Volterra equalizer (FSE-Volterra) is found to give improvement beyond that gained by the Volterra equalizer. Significant probability of error performance improvement is found for multilevel modulation schemes. Also, it is found that probability of error improvement is more significant for modulation schemes, constant amplitude and multilevel, which require higher signal to noise ratios (i.e., higher modulation orders) for reliable operation. The maximum likelihood sequence detection (MLSD) receiver for a nonlinear satellite channel, a bank of matched filters followed by a Viterbi detector, serves as a probability of error lower bound for the Volterra and FSE-Volterra equalizers. However, this receiver has not been evaluated for a specific satellite channel. In this work, an MLSD receiver is evaluated for a specific downlink-limited satellite channel. Because of the bank of matched filters, the MLSD receiver may be high in complexity. Consequently, the probability of error performance of a more practical suboptimal MLSD receiver, requiring only a single receive filter, is evaluated

A new approach to nonlinear feedback control for suppressing periodic disturbances: Part 1. Fundamental Theory

A new nonlinear feedback control approach is proposed in the present study to suppress periodic exogenous disturbances based on a frequency domain theory of nonlinear systems. In Part 1 of this paper, a series of fundamental theoretical results and techniques are established. It is shown that a low order nonlinear feedback may be sufficient for some control problems. A general procedure is then proposed for controller design. The new approach is demonstrated by a case study on the design of an active vibration control system in Part 2. Theoretical analysis and simulation results verify the effectiveness of the new results

Pre-distortion algorithms implemented in fixed-point arithmetic

[SPA]En la actualidad se requiere que los sistemas de comunicaciones inal ambricas proporcionen altas tasas de datos junto con una gran calidad. A n de conseguir esto, se emplean t ecnicas de transmisi on y modulaci on espectralmente e cientes, lo que resulta en se~nales con grandes uctuaciones en la envolvente y, por tanto, un PAPR (Peak-to-Average Power Ratio) alto. Adem as, debido a exigencias de e ciencia de potencia, los ampli - cadores operan en las inmediaciones de la regi on de saturaci on. Desafortunadamente, esto conlleva un comportamiento no lineal del ampli cador, lo que introduce distorsiones no lineales. Estas distorsiones provocan, por un lado, una degradaci on de la se~nal transmitida y, por otro, un ensanchamiento en el espectro del ancho de banda del canal, y, consecuentemente una interferencia en los canales de transmisi on adyacentes. La pre-distorsi on digital es una t ecnica empleada para compensar las distorsiones introducidas por el ampli cador, de manera que el sistema resultante opere como una etapa de ampli caci on lineal y e ciente. Esta soluci on reduce el tama~no de la unidad de transmisi on y ayuda a reducir costes, especialmente si se combina con otras t ecnicas de linealizaci on. Como el pre-distorsionador ha de predecir la no linealidad introducida por el ampli cador, la pre-distorsi on puede considerarse un problema de modelado de comportamiento. En este proyecto se consideran varios esquemas de pre-distorsi on basados en modelado del comportamiento ya propuestos en la literatura. Desde el modelo de polinomios sin memoria hasta las series truncadas de Volterra, un modelo m as general y con mayor coste computacional, para terminar con decomposed piecewise Volterra series propuesto por Zhu en [1], el cual permite reducir el coste computacional mediante la poda selectiva de las series truncadas de Volterra. El objetivo principal de este trabajo es evaluar la implementaci on en coma ja de dichos algoritmos. Para ello, los algoritmos han sido implementados en MATLAB tanto en coma ja como en coma otante, donde la ultima se usa como referencia para la comparaci on de su rendimiento. Adem as, en el proyecto se presenta una revisi on detallada de la teor a de los modelos que se tratan. Los algoritmos han sido evaluados mediante un modelo de referencia no-lineal: el modelo Saleh para los algoritmos sin memoria y el modelo Hammerstein para los casos con memoria. Los resultados de las simulaciones muestran que el modelo decomposed piecewise Volterra utilizando el modelo de reducci on din amica de Volterra como sub-modelo, mejora el rendimiento de los modelos tradicionales. [ENG]Nowadays, wireless communications systems are required to provide high data-rates with high quality. In order to achieve this, spectrally e cient transmission techniques are employed which rely on signals with large envelope uctuations. Moreover, due to power e ciency demands power ampli ers have to work close to their saturation region. Unfortunately, their resulting nonlinear behaviour introduces nonlinear distortions. By this, on the one hand the transmitted signal is degraded, on the other hand, it causes spectral widening beyond the channel bandwidth, and consequently interference with neighbouring transmission channels. Digital pre-distortion is a technique used to compensate the distortions introduced by the power ampli er, so that the overall system operates as a linear yet e cient amplifying stage. This solution reduces the transmission unit size and allows for cutting energy costs, especially if combined with other linearization techniques. As the pre-distorter has to predict the nonlinearity introduced by the power ampli er, pre-distortion can be considered a behavioural modeling problem. In this thesis, we consider several pre-distortion schemes found in literature that are based on behavioural modeling. Starting with the memoryless polynomial model, we move on to the general but computationally expensive truncated Volterra series and, nally end up with the decomposed piecewise Volterra series proposed by Zhu in [1] that allow to reduce the computational complexity by selectively pruning of the truncated Volterra series. The main goal of this work is to evaluate the xed-point implementation of the algorithms. In order to do so the algorithms are implmented in MATLAB in xed-point arithmetic, as well as in oating-point arithmetic; where the latter is used as reference for a comparison of performance. In addition, a detailed review of the theory is presented in this work. The algorithms are evaluated with a nonlinear reference model: a saleh model for the memoryless case and a hammerstein model for the memory cases. Simulation results show that the decomposed piecewise Volterra model employing the dynamic deviation reduction-based Volterra model as sub-model outperforms the traditional models.Escuela Técnica Superior de Ingeniería de TelecomunicaciónUniversidad Politécnica de Cartagen

Various nonlinear models and their identification, equalization and linearization

System identification is a pre-requisite to analysis of a dynamic system and design of an appropriate controller for improving its performance. The more accurate the mathematical model identified for a system, the more effective will be the controller designed for it. The identification of nonlinear systems is a topic which has received considerable attention over the last two decades. Generally speaking, when it is difficult to model practical systems by mathematical analysis method, system identification may be an efficient way to overcome the shortage of mechanism analysis method. The goal of the modeling is to find a simple and efficient model which is in accord with the practical system. In many cases, linear models are not suitable to present these systems and nonlinear models have to be considered. Since there are nonlinear effects in practical systems, e.g. harmonic generation, intermediation, desensitization, gain expansion and chaos, we can infer that most control systems are nonlinear. Nonlinear models are more widely used in practice, because most phenomena are nonlinear in nature. Indeed, for many dynamic systems the use of nonlinear models is often of great interest and generally characterizes adequately physical processes over their whole operating range. Thus, accuracy and performance of the control law increase significantly. Therefore, nonlinear system modeling is much more important than linear system identification. We will deal with various nonlinear models and their processing

Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks

neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce the complexity without significant performance penalty. First, propose a novel NN with shortcut connections, referred to as shortcut real-valued time-delay neural network (SVDEN), where trainable neuron-wise shortcut connections are added between the input and output layers. Second, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that SVDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity

Advanced signal processing techniques for the modeling and linearization of wireless communication systems.

Los nuevos estándares de comunicaciones digitales inalámbricas están impulsando el diseño de amplificadores de potencia con unas condiciones límites en términos de linealidad y eficiencia. Si bien estos nuevos sistemas exigen que los dispositivos activos trabajen cerca de la zona de saturación en busca de la eficiencia energética, la no linealidad inherente puede producir que el sistema muestre prestaciones inadecuadas en emisiones fuera de banda y distorsión en banda. La necesidad de técnicas digitales de compensación y la evolución en el diseño de nuevas arquitecturas de procesamiento de señales digitales posicionan a la predistorsión digital (DPD) como un enfoque práctico. Los predistorsionadores digitales se suelen basar en modelos de comportamiento como el memory polynomial (MP), el generalized memory polynomial (GMP) y el dynamic deviation reduction-based (DDR), etc. Los modelos de Volterra sufren la llamada "maldición de la dimensionalidad", ya que su complejidad tiende a crecer de forma exponencial a medida que el orden y la profundidad de memoria crecen. Esta tesis se centra principalmente en contribuir a la rama de conocimiento que enmarca el modelado y linealización de sistemas de comunicación inalámbrica. Los principales temas tratados son el modelo Volterra-Parafac y el modelo general de Volterra para sistemas complejos, los cuales tratan la estructura del DPD y las series de Volterra estructuradas con compressed-sensing y un método para la linealización en un rango de potencias de operación, que se centran en cómo los coeficientes de los modelos deben ser obtenidos.Premio Extraordinario de Doctorado U