Synchronization and channel estimation in experimental M-QAM OFDM radio over fiber systems using CAZAC based training preamble

In this paper, we propose a new technique for synchronization and channel estimation in M-QAM OFDM radio over fiber (RoF) system by using constant amplitude zero auto-correlation (CAZAC) sequence based training preamble. Delay and correlate method is used to identify the training sequence in the received signal vector and to correct the symbol timing offset. For an optimum demodulation of OFDM signal, optimum down sampling offset position has to be identified before applying symbol timing algorithm. To solve this issue, we present the iterative method of finding optimum down sampling offset position. We show that the training preamble used for synchronization can also be applied to estimate the channel response using averaging technique. Moreover, we used the least square estimation based channel estimation method using pilot subcarriers and compare the results with training preamble based estimation

Training-Aided Channel Estimation and Equalization in SDM Systems with MISO Pre-convergence under Strong Coupling

A simple DSP scheme receiver is proposed to circumvent laser frequency-offset effects in DA-CE based SDM systems using a MISO CMA pre-convergence stage. Numerical results demonstrate a successful operation for 12-mode fiber transmission under MDL, using QPSK and 16QAM mapping

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

Estudo de sequências de treino em sistemas óticos coerentes

Mestrado em Engenharia Electrónica e TelecomunicaçõesNos últimos anos, tem-se assistido a um grande aumento da capacidade de transmissão de dados em sistemas de comunicação ótica. O cada vez maior aproveitamento da capacidade oferecida pela fibra ótica tem levado a redes cada vez mais complexas, suportadas por transmissores e recetores de tecnologia de ponta. Atualmente, a deteção coerente em conjugação com o processamento digital de sinal tem-se tornado a tecnologia chave para a próxima geração de sistemas óticos, permitindo a compensação de imperfeições no sinal que parecia impossível há uns anos atrás. Mais recentemente, a utilização de sequências de treino nas tramas de dados transmitidas tem sido alvo de enorme interesse, uma vez que permite desenhar transmissores e recetores cujos algoritmos de processamento são simultaneamente simples e transparentes ao formato de modulação. Contudo, os sistemas que recorram a sequências de treino devem ser alvo de uma otimização cuidadosa, que numa primeira fase deve ser feita via simulação. Nesse sentido, esta dissertação tem como objetivo o melhoramento e a adaptação da plataforma de simulação ótica, OSIP, para a utilização de sequências de treino em sistemas óticos coerentes. Para validar os componentes adicionados no ambiente de simulação OSIP, são desenvolvidos e testados algoritmos baseados em sequências de treino para realizar sincronização de dados, estimação e correção de desvio de frequência, assim como estimação de canal. È ainda proposto um método de monitorização de um dos grandes fatores limitadores da sensibilidade do recetor, o ruído de fase. De modo a melhorar a sincronização dos dados e estimação do canal, são utilizadas sequências de treino do tipo CAZAC devido ás suas boas propriedades de correlação.Over the last years, the optical data transmission capacity has observed a steady growth. The increasingly e cient use of the optical ber spectrum has been relying on complex networks, built on cutting-edge transponders. Currently, coherent detection in combination with digital signal processing has emerged as one of the key technologies for the next generation optical communication systems, allowing the compensation of impairments which was seemingly impossible a couple of years ago. More recently, the use of training sequences embedded within transmitted data frames has been subject of signi cant interest since it allows the design of transponders whose DSP algorithms are simultaneously simple and transparent to the modulation format. However, systems based on training sequences should be rst subject to careful optimization, initially done by numerical simulations. The objective of this dissertation is to improve the optical simulation platform, OSIP, in order to enable the study of coherent optical systems using training sequences. To validate the new components in the simulator, dataaided algorithms to perform frame detection, frequency o set estimation and correction, and channel estimation were developed and tested. A new method to perform phase noise monitoring is also proposed and numerically assessed. In order to achieve an overall best-possible in all algorithms, CAZAC sequences which have excellent correlation properties were used