Blind channel identification/equalization with applications in wireless communications

Ph.DDOCTOR OF PHILOSOPH

Implementation and Analysis of Spectral Subtraction and Signal Separation in Deterministic Wide-Band Anti-Jamming Scenarios

With the increasing volume of wireless traffic that military operations require, the likelihood of transmissions interfering with each other is steadily growing to the point that new techniques need to be employed. Furthermore, to combat remotely operated improvised explosive devices, many ground convoys transmit high-power broadband jamming signals, which block both hostile as well as friendly communications. These wide-band jamming fields pose a serious technical challenge to existing anti-jamming solutions that are currently employed by the Navy and Marine Corps. This thesis examines the feasibility of removing such deterministic jammers from the spectral environment, enabling friendly communications. Anti-jamming solutions in self-jamming environments are rarely considered in the literature, principally due to the non-traditional nature of such jamming techniques. As a result, a combination of approaches are examined which include: Antenna Subset Selection, Spectral Subtraction, and Source Separation. These are combined to reduce environmental interference for reliable transmissions. Specific operational conditions are considered and evaluated, primarily to define the limitations and utility of such a system. A final prototype was constructed using a collection of USRP software defined radios, providing solid conclusions of the overall system performance

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

A unified approach to sparse signal processing

A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, compo-nent analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing and rate of innovation. The redundancy introduced by channel coding i

Equalização digital para sistemas de transmissão ópticos coerentes

This thesis focus on the digital equalization of fiber impairments for coherent optical transmission systems. New efficient and low-complexity equalization and mitigation techniques that counteract fiber nonlinear impairments are proposed and the tradeoff between performance and complexity is numerically assessed and experimentally demonstrated in metro and long-haul 400G superchannels-based transmission systems. Digital backpropagation (DBP) based on low-complexity split-step Fourier method and Volterra series nonlinear equalizers are experimentally assessed in an uniform superchannel system. In contrast with standard DBP methods, these techniques prove to be able to be implemented with larger step-sizes, consequently requiring a reduced number of multiplications, and still achieve a significant reach extension over linear equalization techniques. Moreover, given its structure, the complexity of the proposed Volterra-based DBP approach can be easily adjusted by changing the nonlinear filter dimension according to the system requirements, thus providing a higher flexibility to the nonlinear equalization block. A frequency-hybrid superchannel envisioning near-future flexible networks is then proposed as a way to increase the system bit-rate granularity. The problematic of the power-ratio between superchannel carriers is addressed and optimized for linear and nonlinear operation regimes using three distinct FEC paradigms. Applying a single FEC to the entire superchannel has a simpler implementation and is found to be a more robust approach, tolerating larger uncertainties on the system parameters optimization. We also investigate the performance gain provided by the application of different DBP techniques in frequency-hybrid superchannel systems, and its implications on the optimum power-ratio. It is shown that the application of DBP can be restricted to the carrier transporting the higher cardinality QAM format, since the DBP benefit on the other carriers is negligible, which might bring a substantially complexity reduction of the DBP technique applied to the superchannel.A presente tese foca-se na equalização digital das distorções da fibra para sistemas óticos de transmissão coerente. São propostas novas técnicas eficientes e de baixa complexidade para a equalização e mitigação das distorções não lineares da fibra, e o compromisso entre desempenho e complexidade é testado numericamente e demonstrado experimental em sistemas de transmissão metro e longa distância baseados em supercanais 400G. A propagação digital inversa baseada no método de split-step Fourier e equalizadores não lineares de séries de Volterra de baixa complexidade são testadas experimentalmente num sistema baseado em supercanais uniformes. Ao contrário dos métodos convencionais utilizados, estas técnicas podem ser implementadas utilizando menos interações e ainda extender o alcance do sistema face às técnicas de equalização linear. Para além disso, a complexidade do método baseado em Volterra pode ser facilmente ajustada alterando a dimensão do filtro não linear de acordo com os requisitos do sistema, concedendo assim maior flexibilidade ao bloco de equalização não linear. Tendo em vista as futuras redes flexı́veis, um supercanal hı́brido na frequência é proposto de modo a aumentar a granularidade da taxa de transmissão do sistema. A problemática da relação de potência entre as portadoras do supercanal é abordada e optimizada em regimes de operação linear e não linear utilizando paradigmas diferentes de códigos correctores de erros. A aplicação de um único código corrector de erros à totalidade do supercanal mostra ser a abordagem mais robusta, tolerando maiores incertezas na optimização dos parâmetros do sistema. O ganho de desempenho dado pela aplicação de diferentes técnicas de propagação digital inversa em sistemas de supercanais hı́bridos na frequência é tamém analizado, assim como as suas implicações na relação óptima de potência. Mostra-se que esta pode ser restringida à portadora que transporta o formato de modulação de ordem mais elevada, uma vez que o benefı́cio trazido pelas restantes portadotas é negligenciável, permitindo reduzir significativamente a complexidade do algoritmo aplicado.Programa Doutoral em Telecomunicaçõe