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

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Blind adaptive equalization for QAM signals: New algorithms and FPGA implementation.

Adaptive equalizers remove signal distortion attributed to intersymbol interference in band-limited channels. The tap coefficients of adaptive equalizers are time-varying and can be adapted using several methods. When these do not include the transmission of a training sequence, it is referred to as blind equalization. The radius-adjusted approach is a method to achieve blind equalizer tap adaptation based on the equalizer output radius for quadrature amplitude modulation (QAM) signals. Static circular contours are defined around an estimated symbol in a QAM constellation, which create regions that correspond to fixed step sizes and weighting factors. The equalizer tap adjustment consists of a linearly weighted sum of adaptation criteria that is scaled by a variable step size. This approach is the basis of two new algorithms: the radius-adjusted modified multitmodulus algorithm (RMMA) and the radius-adjusted multimodulus decision-directed algorithm (RMDA). An extension of the radius-adjusted approach is the selective update method, which is a computationally-efficient method for equalization. The selective update method employs a stop-and-go strategy based on the equalizer output radius to selectively update the equalizer tap coefficients, thereby, reducing the number of computations in steady-state operation. (Abstract shortened by UMI.) Source: Masters Abstracts International, Volume: 45-01, page: 0401. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006

New receiver for joint blind equalization and carrier phase recovery of QAM signals

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Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Training Symbol-Based Equalization for Quadrature Duobinary PDM-FTN Systems

A training symbol-based equalization algorithm is proposed for polarization de-multiplexing in quadrature duobinary (QDB) modulated polarization division multiplexedfaster-than-Nyquist (FTN) coherent optical systems. The proposed algorithm is based on the least mean square algorithm, and multiple location candidates of a symbol are considered in order to make use of the training symbols with QDB modulation.Results show that an excellent convergence performance is obtained using the proposed algorithm under different polarization alignment scenarios. The optical signal-to-noise ratio required to attain a bit error rate of 2*10-2 is reduced by 1.7 and 1.8 dB using the proposed algorithm, compared to systems using the constant modulus algorithm with differential coding for 4-ary quadrature amplitude modulation(4-QAM) and 16-QAM systems with symbol-by-symbol detection, respectively.Furthermore, comparisons with the Tomlinson-Harashima precoding-based FTN systems illustrate that QDB is preferable when 4-QAM is utilized