Direct digital predistortion technique for the compensation of laser chirp and fiber dispersion in long haul radio over fiber links

Hadi, Muhammad Usman; Nanni, Jacopo; Polleux, Jean Luc; Traverso, Pier Andrea; Tartarini, Giovanni

doi:10.1007/s11082-019-1923-8

Cited by 42 publications

(38 citation statements)

References 37 publications

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“…However, A-RoF technology is prone to nonlinearities that occur due to physical properties of laser, fiber and photodiode in the link [6,7]. Similarly, A-RoF transmission is dependent on optical subcarrier modulation techniques which suggests the use of orthogonal frequency divisional multiplexing (OFDM) that have high peak to average power ratio (PAPR) [8].…”

Section: Introductionmentioning

confidence: 99%

“…Many impairment mitigation methods have been proposed that includes the linearization based on Digital Predistortion [10][11][12][13]. Direct Digital Predistortion technique (DPDT) that linearizes the links by realizing the behavioral model of A-RoF links was also proposed in [6,13]. However, these linearization techniques are complex, time consuming and adds extra over heads [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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<em></em>Nonlinearities Diminution in 40 Gb/s 256 QAM Radio over Fiber Link via Machine Learning Method

Hadi¹

2019

Preprint

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Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

<em></em>Nonlinearities Diminution in 40 Gb/s 256 QAM Radio over Fiber Link via Machine Learning Method

Hadi¹

2019

Preprint

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“…Radio over Fiber (RoF) technology has been realized in different forms ranging from analog to more complex forms [1][2][3][4][5][6]. The enhancement of capacity and wireless coverage has posed significant challenges to the existing optical and wireless access networks.…”

Section: Introductionmentioning

confidence: 99%

“…Before the introduction of ML in this field, the reduction of nonlinearities was carried out through different methodologies that were proposed during last 10 years via Optical and Electrical domain methods, with latter getting more attention. Digital Predistortion has gained immense attention and therefore it is still looked to be an important methodology [1,[7][8][9]. However, these DPD based linearization techniques are not so straight forward and are rather complex, time consuming and adds extra over heads [10].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

Hadi¹

2019

Preprint

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Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

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“…Nonlinearities mitigation has been proposed via Optical and Electrical domain methods, with latter getting more attention. Digital Predistortion has gained immense attention and therefore it is still looked to be an important methodology [11][12][13][14][15][16]. However, these DPD based linearization techniques are not so straight forward and are rather complex, time consuming and adds extra over heads [17].…”

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confidence: 99%

RL-SARSA Machine Learning Based Analog Radio over Fiber System

Hadi¹

2019

Preprint

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We propose a 10-Gb/s 64-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes Reinforcement Learning (RL) SARSA based decision method to indicate an effective decision which mitigates nonlinearity. By utilizing RL-SARSA algorithm, the results demonstrate that significant reduction can be obtained in terms of bit error rate.

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Direct digital predistortion technique for the compensation of laser chirp and fiber dispersion in long haul radio over fiber links

Cited by 42 publications

References 37 publications

<em></em>Nonlinearities Diminution in 40 Gb/s 256 QAM Radio over Fiber Link via Machine Learning Method

<em></em>Nonlinearities Diminution in 40 Gb/s 256 QAM Radio over Fiber Link via Machine Learning Method

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

RL-SARSA Machine Learning Based Analog Radio over Fiber System

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