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

Transmitter Linearization Adaptable to Power-Varying Operation

This paper presents the design of a power-scalable digital predistorter (DPD) for transmitter architectures. The target is to accomplish the joint compensation of impairments due to the I/Q modulator and nonlinearities associated with the power amplifier, and procure a maintained linearization performance in a range of average working operation levels. The identification method for the linearizer parameters enriches the standard least-squares procedure with a synergistic integration with sparsity-based model pruning strategies. The method has been tested with a general complex-valued Volterra model applied to the linearization of two communications transmitters operating at 3.6 GHz. The linearizers designed for the two transmitters effectively provide the joint compensation of the nonlinear behavior. In addition to their good performance in terms of adjacent channel power ratio, the DPDs exhibit a wide range of power-varying adaptation.Comisión Interministerial de Ciencia y Tecnología (CICYT) TEC2014-53103-

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

Predistorsión de amplificadores de potencia con técnicas de aprendizaje distribuido

Este trabajo introduce ciertos aspectos que son necesarios para trabajar con el modelado y la linealización de amplificadores de potencia en entornos distribuidos. La problemática de la distorsión en amplificadores de potencia se vuelve más compleja conforme aumentan características como el ancho de banda y que cada vez adquieren mayor demanda en los sistemas de comunicaciones móviles 5G. Además, surge especial interés en técnicas que permitan la computación distribuida, que no solo permite operar sobre dispositivos más simples, sino que también habilita arquitecturas en paralelo, acelerando este tipo de cómputos. Este trabajo trata de combinar técnicas como genetic-based Volterra subespace generator (GVG) para la búsqueda del modelo y el método de dirección alterna de multiplicadores (ADMM) para modelar y linealizar amplificadores de potencia, mediante una solución distribuida. Además, se profundiza en el estudio de la validación, mediante la comprensión del impacto de la relación potencia de pico a potencia promedio (PAPR) en métricas como la divergencia de Kullback-Leibler. Éstas, se pusieron a prueba con señales 5G-NR con un ancho de banda de 50 MHz, demostrando que ADMM es capaz de alcanzar prestaciones competitivas al comparar con la resolución por mínimos cuadrados.This work introduces certain aspects that are necessary to work with the modelling and linearisation of power amplifiers in distributed environments. The problem of distortion in power amplifiers becomes more complex as features such as bandwidth grow and are increasingly in demand in 5G mobile communications systems. In addition, there is a special interest in techniques that enable distributed computing, which not only allows operating on simpler devices, but also enables parallel architectures, accelerating this type of computation. The combination of techniques such as genetic-based Volterra subspace generator (GVG) for model search and the alternating direction method of multipliers (ADMM) for modelling and linearising power amplifiers, by means of a distributed solution, is proposed. In addition, the study of model validation is further explored by understanding the impact of the peak-to-average power ratio (PAPR) on metrics such as Kullback-Leibler divergence. These were tested on 5G-NR signals with a bandwidth of 50 MHz, demonstrating that ADMM is able to achieve competitive performance when compared to least squares resolution.Universidad de Sevilla. Máster Universitario en Ingeniería de Telecomunicació

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

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