119 research outputs found
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
Performance comparison of blind and non-blind channel equalizers using artificial neural networks
In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI). To reduce the effect of ISI different channel equalization algorithms are used. Complex equalization algorithms allow for achieving the best performance but they do not meet the requirements for implementation of real-time detection at low complexity, thus limiting their application. In this paper, we present different blind and non-blind equalization structures based on Artificial Neural Networks (ANNs) and, also, we analyze their complexity versus performance. Since the activation function at the output layer depends on the cost function with respect to the input, in the present work we use mean squared error as loss function for the output layer. The simulated network is based on multilayer feedforward perceptron ANN, which is trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network to improve the convergence speed. Simulation results demonstrate that the implementation of equalizers using ANN provides an upper hand over the performance and computational complexity with respect to conventional methods
Wireless Channel Equalization in Digital Communication Systems
Our modern society has transformed to an information-demanding system, seeking voice, video, and data in quantities that could not be imagined even a decade ago. The mobility of communicators has added more challenges. One of the new challenges is to conceive highly reliable and fast communication system unaffected by the problems caused in the multipath fading wireless channels. Our quest is to remove one of the obstacles in the way of achieving ultimately fast and reliable wireless digital communication, namely Inter-Symbol Interference (ISI), the intensity of which makes the channel noise inconsequential.
The theoretical background for wireless channels modeling and adaptive signal processing are covered in first two chapters of dissertation.
The approach of this thesis is not based on one methodology but several algorithms and configurations that are proposed and examined to fight the ISI problem. There are two main categories of channel equalization techniques, supervised (training) and blind unsupervised (blind) modes. We have studied the application of a new and specially modified neural network requiring very short training period for the proper channel equalization in supervised mode. The promising performance in the graphs for this network is presented in chapter 4.
For blind modes two distinctive methodologies are presented and studied. Chapter 3 covers the concept of multiple cooperative algorithms for the cases of two and three cooperative algorithms. The select absolutely larger equalized signal and majority vote methods have been used in 2-and 3-algoirithm systems respectively. Many of the demonstrated results are encouraging for further research.
Chapter 5 involves the application of general concept of simulated annealing in blind mode equalization. A limited strategy of constant annealing noise is experimented for testing the simple algorithms used in multiple systems. Convergence to local stationary points of the cost function in parameter space is clearly demonstrated and that justifies the use of additional noise. The capability of the adding the random noise to release the algorithm from the local traps is established in several cases
Coherent Optical OFDM Modem Employing Artificial Neural Networks for Dispersion and Nonlinearity Compensation in a Long-Haul Transmission System
In order to satisfy the ever increasing demand for the bandwidth requirement in broadband services the optical orthogonal frequency division multiplexing (OOFDM) scheme is being considered as a promising technique for future high-capacity optical networks. The aim of this thesis is to investigate, theoretically, the feasibility of implementing the coherent optical OFDM (CO-OOFDM) technique in long haul transmission networks. For CO-OOFDM and Fast-OFDM systems a set of modulation formats dependent analogue to digital converter (ADC) clipping ratio and the quantization bit have been identified, moreover, CO-OOFDM is more resilient to the chromatic dispersion (CD) when compared to the bandwidth efficient Fast-OFDM scheme. For CO-OOFDM systems numerical simulations are undertaken to investigate the effect of the number of sub-carriers, the cyclic prefix (CP), and ADC associated parameters such as the sampling speed, the clipping ratio, and the quantisation bit on the system performance over single mode fibre (SMF) links for data rates up to 80 Gb/s. The use of a large number of sub-carriers is more effective in combating the fibre CD compared to employing a long CP. Moreover, in the presence of fibre non-linearities identifying the optimum number of sub-carriers is a crucial factor in determining the modem performance. For a range of signal data rates up to 40 Gb/s, a set of data rate and transmission distance-dependent optimum ADC parameters are identified in this work. These parameters give rise to a negligible clipping and quantisation noise, moreover, ADC sampling speed can increase the dispersion tolerance while transmitting over SMF links. In addition, simulation results show that the use of adaptive modulation schemes improves the spectrum usage efficiency, thus resulting in higher tolerance to the CD when compared to the case where identical modulation formats are adopted across all sub-carriers. For a given transmission distance utilizing an artificial neural networks (ANN) equalizer improves the system bit error rate (BER) performance by a factor of 50% and 70%, respectively when considering SMF firstly CD and secondly nonlinear effects with CD. Moreover, for a fixed BER of 10-3 utilizing ANN increases the transmission distance by 1.87 times and 2 times, respectively while considering SMF CD and nonlinear effects. The proposed ANN equalizer performs more efficiently in combating SMF non-linearities than the previously published Kerr nonlinearity electrical compensation technique by a factor of 7
Direct-form adaptive equalization for underwater acoustic communication
Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2012Adaptive equalization is an important aspect of communication systems in various
environments. It is particularly important in underwater acoustic communication
systems, as the channel has a long delay spread and is subject to the effects of time-
varying multipath fading and Doppler spreading.
The design of the adaptation algorithm has a profound influence on the performance of the system. In this thesis, we explore this aspect of the system. The
emphasis of the work presented is on applying concepts from inference and decision
theory and information theory to provide an approach to deriving and analyzing
adaptation algorithms. Limited work has been done so far on rigorously devising
adaptation algorithms to suit a particular situation, and the aim of this thesis is to
concretize such efforts and possibly to provide a mathematical basis for expanding it
to other applications.
We derive an algorithm for the adaptation of the coefficients of an equalizer when
the receiver has limited or no information about the transmitted symbols, which we
term the Soft-Decision Directed Recursive Least Squares algorithm. We will demonstrate connections between the Expectation-Maximization (EM) algorithm and the
Recursive Least Squares algorithm, and show how to derive a computationally efficient, purely recursive algorithm from the optimal EM algorithm.
Then, we use our understanding of Markov processes to analyze the performance
of the RLS algorithm in hard-decision directed mode, as well as of the Soft-Decision
Directed RLS algorithm. We demonstrate scenarios in which the adaptation procedures fail catastrophically, and discuss why this happens. The lessons from the
analysis guide us on the choice of models for the adaptation procedure. We then
demonstrate how to use the algorithm derived in a practical system for underwater
communication using turbo equalization. As the algorithm naturally incorporates
soft information into the adaptation process, it becomes easy to fit it into a turbo
equalization framework. We thus provide an instance of how to use the information of a turbo equalizer in an adaptation procedure, which has not been very well explored in the past. Experimental data is used to prove the value of the algorithm in
a practical context.Support from the agencies
that funded this research- the Academic Programs Office at WHOI and the Office of
Naval Research (through ONR Grant #N00014-07-10738 and #N00014-10-10259)
Intelligent optical performance monitor using multi-task learning based artificial neural network
An intelligent optical performance monitor using multi-task learning based
artificial neural network (MTL-ANN) is designed for simultaneous OSNR
monitoring and modulation format identification (MFI). Signals' amplitude
histograms (AHs) after constant module algorithm are selected as the input
features for MTL-ANN. The experimental results of 20-Gbaud NRZ-OOK, PAM4 and
PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI
simultaneously with higher accuracy and stability compared with single-task
learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% and
OSNR monitoring root-mean-square error of 0.63 dB for the three modulation
formats under consideration. Furthermore, the number of neuron needed for the
single MTL-ANN is almost the half of STL-ANN, which enables reduced-complexity
optical performance monitoring devices for real-time performance monitoring
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