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

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

Advanced DSP Algorithms For Modern Wireless Communication Transceivers

A higher network throughput, a minimized delay and reliable communications are some of many goals that wireless communication standards, such as the fifthgeneration (5G) standard and beyond, intend to guarantee for its customers. Hence, many key innovations are currently being proposed and investigated by researchers in the academic and industry circles to fulfill these goals. This dissertation investigates some of the proposed techniques that aim at increasing the spectral efficiency, enhancing the energy efficiency, and enabling low latency wireless communications systems. The contributions lay in the evaluation of the performance of several proposed receiver architectures as well as proposing novel digital signal processing (DSP) algorithms to enhance the performance of radio transceivers. Particularly, the effects of several radio frequency (RF) impairments on the functionality of a new class of wireless transceivers, the full-duplex transceivers, are thoroughly investigated. These transceivers are then designed to operate in a relaying scenario, where relay selection and beamforming are applied in a relaying network to increase its spectral efficiency. The dissertation then investigates the use of greedy algorithms in recovering orthogonal frequency division multiplexing (OFDM) signals by using sparse equalizers, which carry out the equalization in a more efficient manner when the low-complexity single tap OFDM equalizer can no longer recover the received signal due to severe interferences. The proposed sparse equalizers are shown to perform close to conventional optimal and dense equalizers when the OFDM signals are impaired by interferences caused by the insertion of an insufficient cyclic prefix and RF impairments

Digital signal processing techniques for fiber nonlinearity compensation in coherent optical communication systems

The capacity of long-haul coherent optical communication systems is limited by the detrimental effects of fiber Kerr nonlinearity. The power-dependent nature of the Kerr nonlinearity restricts the maximum launch power into the fiber. That results in the reduction of the optical signal-to-noise ratio at the receiver; thereby, the maximum transmission reach is limited. Over the last few decades, several digital signal processing (DSP) techniques have been proposed to mitigate the effects of fiber nonlinearity, for example, digital back-propagation (DBP), perturbation based nonlinearity compensation (PB-NLC), and phase-conjugated twin wave (PCTW). However, low-complexity and spectrally efficient DSP-based fiber nonlinearity mitigation schemes for long-haul transmission systems are yet to be developed. In this thesis, we focus on the computationally efficient DSP-based techniques that can help to combat various sources of fiber nonlinearity in long-haul coherent optical communication systems. With this aim, we propose a linear time/polarization coded digital phase conjugation (DPC) technique for the mitigation of fiber nonlinearity that doubles the spectral efficiency obtained in the PCTW technique. In addition, we propose to investigate the impact of random polarization effects, like polarization-dependent loss and polarization mode dispersion, on the performance of the linear-coded DPC techniques. We also propose a joint technique that combines single-channel DBP with the PCTW technique. We show that the proposed scheme is computationally efficient and achieves similar performance as multi-channel DBP in wavelength division multiplexed superchannel systems. The regular perturbation (RP) series used to analytically approximate the solution of the nonlinear Schrödinger equation (NLSE) has a serious energy divergence problem when truncated to the first-order. Recent results on the transmission of high data-rate optical signals reveal that the nonlinearity compensation performance of the first-order PB-NLC technique decreases as the product of the transmission distance and launch power increases. The enhanced RP (ERP) method can improve the accuracy of the first-order RP approximation by partially solving the energy divergence problem. On this ground, we propose an ERP-based nonlinearity compensation technique to compensate for the fiber nonlinearity in a polarization-division multiplexed dispersion unmanaged optical communication system. Another possible solution to improve the accuracy of the PB-NLC technique is to increase the order of the RP solution. Based on this idea, we propose to extend the first-order solution of the NLSE to the second-order to improve the nonlinearity compensation performance of the PB-NLC technique. Following that, we investigate a few simplifying assumptions to reduce the implementation complexity of the proposed second-order PB-NLC technique