Applying Neural Networks in Optical Communication Systems: Possible Pitfalls

Eriksson, Tobias A.; Bülow, H.; Leven, A.

doi:10.1109/lpt.2017.2755663

Cited by 158 publications

(93 citation statements)

References 22 publications

(21 reference statements)

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“…The test set is finally used to evaluate the BER performance recovery of an independent data set, that the system was not trained on that. In this way we avoid possible pitfalls in biased training 69…”

Section: Training and Testingmentioning

confidence: 99%

Photonic machine learning implementation for signal recovery in optical communications

Argyris

Bueno

Fischer

2018

Sci Rep

141

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Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheless, processing time-dependent high-speed signals can turn into an extremely challenging task, especially when these signals have been nonlinearly distorted. Recently, analogue hardware concepts using nonlinear transient responses have been gaining significant interest for fast information processing. Here, we introduce a simplified photonic reservoir computing scheme for data classification of severely distorted optical communication signals after extended fibre transmission. To this end, we convert the direct bit detection process into a pattern recognition problem. Using an experimental implementation of our photonic reservoir computer, we demonstrate an improvement in bit-error-rate by two orders of magnitude, compared to directly classifying the transmitted signal. This improvement corresponds to an extension of the communication range by over 75%. While we do not yet reach full real-time post-processing at telecom rates, we discuss how future designs might close the gap.

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Section: Training and Testingmentioning

confidence: 99%

Photonic machine learning implementation for signal recovery in optical communications

Argyris

Bueno

Fischer

2018

Sci Rep

141

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“…The neural network (NN) is computational model loosely inspired by its biological counterparts [88]. In recent years, it has been proposed to mitigate the nonlinear impairments in optical communication system [89][90][91]. For short-reach PAM4 optical links, various research concerning the NN method has been performed to improve transmission performance [43][44][45][46][47][48][49][50][51][52][53].…”

Section: Neural Networkmentioning

confidence: 99%

Recent Advances in Equalization Technologies for Short-Reach Optical Links Based on PAM4 Modulation: A Review

Zhou

Liu

et al. 2019

Applied Sciences

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In recent years, short-reach optical links have attracted much more attention and have come to constitute a key market segment due to the rapid development of data-center applications and cloud services. Four-level pulse amplitude modulation (PAM4) is a promising modulation format to provide both a high data rate and relatively low cost for short-reach optical links. However, the direct detector and low-cost components also pose immense challenges, which are unforeseen in coherent transmission. To compensate for the impairments and to truly meet data rate requirements in a cost-effective manner, various digital signal processing (DSP) technologies have been proposed and investigated for short-reach PAM4 optical links. In this paper, an overview of the latest progress on DSP equalization technologies is provided for short-reach optical links based on PAM4 modulation. We not only introduce the configuration and challenges of the transmission system, but also cover the principles and performance of different equalizers and some improved methods. Moreover, machine learning algorithms are discussed as well to mitigate the nonlinear distortion for next-generation short-reach PAM4 links. Finally, a summary of various equalization technologies is illustrated and our perspective for the future trend is given. Appl. Sci. 2019, 9, 2342 2 of 22 multi-tone (DMT), and quadrature amplitude modulation (QAM) based on Kramers-Kronig (KK) receiver [18]. Considering the power consumption and implementation complexity, the most attractive format in short-reach optical links is PAM4, which is believed as a highly promising candidate for the next-generation passive optical network (NG-PON) and has been ratified by IEEE 802.3 bs for 400 Gbps Ethernet transmission [19][20][21][22][23][24][25].However, the low-pass filtering effects induced by the limited bandwidth of transmitter and receiver can cause severely inter-symbol interference (ISI). Low-cost devices such as lasers, modulators, photodiodes (PD) and trans-impedance amplifiers (TIA) also produce nonlinear distortions like level-dependent skew and level-dependent noise. Furthermore, the interaction between chromatic dispersion (CD) and direct detection will lead to a power-fading effect, where the detected signal may contain frequency notches after several kilometers transmission at a high symbol rate. Therefore, various equalization technologies based on digital signal processing (DSP) have been investigated for short-reach links over the years . Conventional equalizers, such as the feed-forward equalizer (FFE) or decision feedback equalizer (DFE), are employed to compensate for the linear impairments induced by limited bandwidth and CD [26][27][28], while the equalizer based on the Volterra series is utilized to mitigate nonlinear distortion [29][30][31][32]. Some new equalization techniques, such as direct detection-faster than Nyquist (DD-FTN) algorithm [33][34][35], intensity directed FFE/DFE (ID-FFE/ID-DFE) algorithm [36,37] and joint clock recovery and FFE (CR-FFE) algorithm...

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“…It is important to mention that for the training set a Mersenne twister was used as a random number generator. To ensure that during training we do not learn parts or construction rules of the pseudo-random sequence [19] and that training and testing datasets originate from different sources we used a Tausworthe [20] random number generator to generate an independent testing set of data using different 250 sequences of 10000 randomly chosen messages. Figure 3 shows a basic schematic of the sliding window sequence estimation algorithm, where the autoencoder is rep- via its final softmax layer.…”

Section: A Trainingmentioning

confidence: 99%

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

Karanov

Liga

Aref

et al. 2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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In this paper, we apply deep learning for communication over dispersive channels with power detection, as encountered in low-cost optical intensity modulation/direct detection (IM/DD) links. We consider an autoencoder based on the recently proposed sliding window bidirectional recurrent neural network (SBRNN) design to realize the transceiver for optical IM/DD communication. We show that its performance can be improved by introducing a weighted sequence estimation scheme at the receiver. Moreover, we perform bit-to-symbol mapping optimization to reduce the bit-error rate (BER) of the system. Furthermore, we carry out a detailed comparison with classical schemes based on pulse-amplitude modulation and maximum likelihood sequence detection (MLSD). Our investigation shows that for a reference 42 Gb/s transmission, the SBRNN autoencoder achieves a BER performance comparable to MLSD, when both systems account for the same amount of memory. In contrast to MLSD, the SBRNN performance is achieved without incurring a computational complexity exponentially growing with the processed memory.

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Applying Neural Networks in Optical Communication Systems: Possible Pitfalls

Cited by 158 publications

References 22 publications

Photonic machine learning implementation for signal recovery in optical communications

Photonic machine learning implementation for signal recovery in optical communications

Recent Advances in Equalization Technologies for Short-Reach Optical Links Based on PAM4 Modulation: A Review

Deep Learning for Communication over Dispersive Nonlinear Channels: Performance and Comparison with Classical Digital Signal Processing

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