723 research outputs found
Adaptive minimum symbol error rate beamforming assisted receiver for quadrature amplitude modulation systems
An adaptive beamforming assisted receiver is proposed for multiple antenna aided multiuser systems that employ bandwidth efficient quadrature amplitude modulation (QAM). A novel minimum symbol error rate (MSER) design is proposed for the beamforming assisted receiver, where the system’s symbol error rate is directly optimized. Hence the MSER approach provides a significant symbol error ratio performance enhancement over the classic minimum mean square error design. A sample-by-sample adaptive algorithm, referred to as the least symbol error rate (LBER) technique, is derived for allowing the adaptive implementation of the system to arrive from its initial beamforming weight solution to MSER beamforming solution
Multi-Level Kernel-Based QAM Symbol Error Probability Estimation
Kernel density estimators technique has been successfully applied to efficient Bit Error Rate (BER) computation issue under a diversity of simulation frameworks. However, as contemporary and emerging digital communication systems are increasingly provided with advanced transceivers, it is questionable if the Symbol Error Rate (SER) can be anyway derived from the BER. This paper investigates for a direct way to efficiently compute the SER. Focusing on the ubiquitous multi-level Quadrature Amplitude Modulation (QAM) transmission schemes, a Gaussian kernel-based estimator is designed. Simulation of the 4-QAM transmission scheme under various channel models shows that the proposed estimator can achieve efficient estimations with a very high degree of accuracy and reliability
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
Stochastic Digital Backpropagation with Residual Memory Compensation
Stochastic digital backpropagation (SDBP) is an extension of digital
backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP
takes into account noise from the optical amplifiers in addition to handling
deterministic linear and nonlinear impairments. The decisions in SDBP are taken
on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be
present due to non-optimal processing in SDBP. In this paper, we extend SDBP to
account for memory between symbols. In particular, two different methods are
proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol
error rate (SER) for memory-based SDBP is significantly lower than the
previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP
has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.Comment: 7 pages, accepted to publication in 'Journal of Lightwave Technology
(JLT)
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
Blind equalization based on pdf distance criteria and performance analysis
In this report, we address M-QAM blind equalization by fitting the probability density functions (pdf) of the equalizer output with the constellation symbols. We propose two new cost functions, based on kernel pdf approximation, which force the pdf at the equalizer output to match the known constellation pdf. The kernel bandwidth of a Parzen estimator is updated during iterations to improve the convergence speed and to decrease the residual error of the algorithms. Unlike related existing techniques, the new algorithms measure the distance error between observed and assumed pdfs for the real and imaginary parts of the equalizer output separately. The advantage of proceeding this way is that the distributions show less modes, which facilitates equalizer convergence, while as for multi-modulus methods phase recovery keeps being preserved. The proposed approaches outperform CMA and classical pdf fitting methods in terms of convergence speed and residual error. We also analyse the convergence properties of the most efficient proposed equalizer via the ordinary differential equation (ODE) method
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