Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM

Tsokanos, Athanasios; Ghanbarisabagh, Mohammad; Mhatli, Sofien; Barry, Liam P.

doi:10.1109/lpt.2018.2832617

Cited by 9 publications

(11 citation statements)

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“…The aforementioned Newton-based algorithm steps and related equations are depicted in Figure 6d which involves an Armijo step-size [29]. In the It should be indicated that an unsupervised and faster version of SVM was employed in [31,33] and [29], respectively. For the unsupervised SVM, the Sato's and Godard's-based constant modulus algorithm (CMA) cost functions were employed in the penalty term of an SVM-like cost function, being iteratively minimized by re-weighted least squares (IRWLS) [23,25].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The experimental setup and parameters are shown in Figure 9 and Table 1 for both single-channel and WDM CO-OFDM at 2000 km and 3200 km of transmission, respectively, using EDFA-based recirculating loops and a standard single-mode fiber (SSMF). The set-up, procedures and parameters are identical to [21,22,28,31,33,35,36]. In summary, 400 OFDM symbols (20.48 ns length) were generated using a 512-point IFFT on 210 QPSK/16-QAM subcarriers.…”

Section: Experimental Setup and Performance Of Machine Learning Algormentioning

confidence: 99%

“…The NLEs performances were assessed by the total subcarriers' bit-error-rate (BER) and Q-factor (=20log 10 √ 2er f c −1 (2BER) )) measurements averaging over 10 recorded traces (~10 6 bits) by error counting (hard-decision-decoding, HDD). [21,22,28,31,33,35,36]. ECL: external cavity laser, AWG: arbitrary waveform generator, AOM: acousto-optic modulator, EDFA: Erbiumdoped fiber amplifier, GFF: gain-flattening filter, LO: local oscillator, DFB: distributed feedback laser, ASE: amplified spontaneous emission, PMM: polarization maintaining multiplexer, WSS: wavelength selective switch, BPF: bandpass filter, OSA: optical spectrum analyzer.…”

Section: Experimental Setup and Performance Of Machine Learning Algormentioning

confidence: 99%

“…(e) (f) Figure 11. WDM QPSK CO-OFDM results [22,28,29,33,35,36] successfully tackled by AP, which seems to effectively compensating intra-channel deterministic and stochastic nonlinearities. However, it should be noted that all algorithms at low powers present poor performance due to the stronger PNA and ASE noise.…”

Section: Experimental Setup and Performance Of Machine Learning Algormentioning

confidence: 99%

“…Machine learning has been recently under the spotlight for many photonic-related applications [18,19]. In long-haul CO-OFDM several supervised and unsupervised machine learning algorithms (MLAs) have been harnessed to mainly perform DSP-based fiber-induced nonlinearity compensation, including artificial neural network (ANNs) [20][21][22][23][24][25][26], support vector machine (SVMs) [27][28][29][30][31][32][33][34], and machine learning clustering such as Fuzzy-logic C-means (FL or FLC) [35], K-means [35] or affinity propagation (AP) [36].In this paper we review the aforementioned MLAs for CO-OFDM, showing key results for single-polarization and standard SMF (SSMF)-based long-haul transmission by comparing them with full-step DBP (FS-DBP) and IVSTF. We also briefly discuss the operation of popular deterministic nonlinearity cancellation techniques and a full computational complexity analysis is presented, for the first time, among key MLAs, FS-DBP and IVSTF.…”

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confidence: 99%

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Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

et al. 2018

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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.Future Internet 2019, 11, 2 2 of 20 between the spatial modes, compared to the SMFs. On the other hand, the drive towards higher-order modulation formats, such as 16-QAM, and spectral-efficient techniques, such as orthogonal frequency division multiplexing (OFDM), lead to greater transmission impairments, reducing the maximum distance over which increased capacity can be provided. More specific, denser constellation diagrams render higher-order modulation formats are more susceptible to circularly-symmetric Gaussian noise as generated by Erbium-doped fiber amplifiers (EDFAs) along the transmission link [6]. Even though the launch power per wavelength channel can be increased to improve the signal-to-noise ratio (SNR) at the receiver, transmission is limited by nonlinear distortions due to the Kerr effect, which have a more severe impact on higher-order modulation formats and spectral-efficient modulation schemes [5,7].Moreover, the transmission of more than two signal wavelengths (wavelength-division multiplexing, WDM) through an optical fibre generates four-wave mixing (FWM), a process caused by the power dependence of the refractive index of the optical fibre [3]. FWM is related to fibre nonlinearity and gives rise to new wavelengths which significantly degrade the signal quality especially at high optical powers and when signals are spectrally close to each other. FWM is one of the most dominant nonlinear effects in optical networks and a primary root of the capacity crunch [3]. Since nonlinear noise such as FWM is highly correlated to signals themselves, nonlinearity can be mitigated by performing special treatment of the signals or conducting post-transmission digital signal processing (DSP) on received signals [5,7].On the other hand, coherent optical OFDM (CO-OFDM) [8] has attracted a lot of interest in optical fiber communications due to its simplified DSP units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM's high peak-to-average power ratio (PAPR) imposes high vulnerability to fiber-induced nonlinearities [8]. Attempts to combat nonlinearities in CO-OFDM have been performed by deterministic nonlinearity compensators which take advantage of the fact that light scattering within a fibre is a deterministic process. Key te...

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Section: Experimental Setup and Performance Of Machine Learning Algormentioning

confidence: 99%

Section: Experimental Setup and Performance Of Machine Learning Algormentioning

confidence: 99%

Section: Experimental Setup and Performance Of Machine Learning Algormentioning

confidence: 99%

mentioning

confidence: 99%

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Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

et al. 2018

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A Deep Learning-Based Approach for Channel Estimation and Equalization for Cognitive Radio Systems

Shah¹,

Kakde²

2023

Lecture Notes in Mechanical Engineering

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Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code

Hu,

Chen,

Han

et al. 2024

IEEE Photonics J.

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In this paper, we propose a multi-impairment compensation scheme based on deep learning and low-density parity-check (LDPC) code for the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. We first propose a multi-impairment compensation autoencoder (MICAE) based on deep neural network (DNN). Then we combine the proposed MICAE with LDPC code and design a DNN-based decoder for LDPC decoding to replace the traditional belief propagation (BP) decoder. The proposed scheme can compensate for multiple impairments simultaneously and has faster decoding speed, greatly improving the performance of the CO-OFDM transmission system. We demonstrate the superiority of the proposed scheme through simulations in different CO-OFDM transmission systems. Simulation results show that the proposed scheme can effectively improve the Q-factor and reduce the bit error rate (BER) of the system, and is more suitable for complex long-distance and high-speed optical transmission scenarios.

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Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM

Cited by 9 publications

References 10 publications

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

A Deep Learning-Based Approach for Channel Estimation and Equalization for Cognitive Radio Systems

Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code

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