Abstract
We propose and analyze a classifier based on logistic regression (LR) to mitigate the impact of nonlinear phase noise (NPN) caused by Kerr-induced self-phase-modulation in digital coherent systems with single-channel unrepeated links. Simulation results reveal that the proposed approach reduces the bit error ratio (BER) in a 100-km-long 16 quadrature amplitude modulation (16-QAM) system operating at 56-Gbps. Thus, the BER is reduced from 6.88 × 10−4 when using maximum likelihood to 4.27 × 10−4 after applying the LR-based classification, representing an increase of 0.36 dB in the effective Q-factor. This performance enhancement is achieved with only 624 operations per symbol, which can be easily parallelized into 16 lines of 39 operations.
Highlights
Nonlinear phase noise (NPN) caused by Kerr-induced self-phase modulation (SPM) is the dominant nonlinear distortion mechanism in single-channel unrepeated links with coherent reception [1,2,3]
To the best of our knowledge, the first time logistic regression (LR) is applied to the compensation of NPN, we provide a detailed explanation of this approach [16, 17]
In order to gain some knowledge on the structure of the clusters, in Fig. 2(a) and (b), we show examples of constellations affected by SPM and receiver noise in absence and in presence of chromatic dispersion (CD)-induced inter-symbol interference (ISI), respectively
Summary
Nonlinear phase noise (NPN) caused by Kerr-induced self-phase modulation (SPM) is the dominant nonlinear distortion mechanism in single-channel unrepeated links with coherent reception [1,2,3]. When CD is present, the instantaneous power depends on the actual symbol and on the adjacent interfering symbols This causes a word-dependent rotation that seems stochastic and hinders NPN compensation [7]. Since this nonlinear phase rotation can severely affect the system bit error ratio (BER), several machine learning techniques have been proposed to mitigate its impact. In [10], a k-nearest neighbors (KNN) algorithm is used to mitigate the effect of several impairments, including SPM Another supervised approach denominated support vector machine (SVM) is employed in [11], [12], and [13].
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