Modulation Format Identification in Coherent Receivers Using Deep Machine Learning

Khan, Faisal Nadeem; Zhong, Kangping; Al-Arashi, Waled Hussein; Chen, Yu; Lu, Chao; Lau, Alan Pak Tao

doi:10.1109/lpt.2016.2574800

Cited by 134 publications

(73 citation statements)

References 12 publications

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“…It is worth mentioning that the proposed algorithm can also work for coherent optical systems and single-carrier modulation. We also compared the complexity of the proposed KNN algorithm with artificial neural network [13]. The complexity calculation of ANN [27,28] and KNN are listed in Table 2, in which the complexity calculation involves two parts, namely the training part and the prediction part.…”

Section: Experimental Verification and Discussionmentioning

confidence: 99%

“…In recent years, several machine learning-based modulation format identification (MFI) techniques have been proposed both in digital coherent and directly detected receivers [13][14][15][16][17][18][19][20][21][22] for optical communications systems because of their excellent learning ability from data, which can avoid the requirement of pre-information. Khan proposed a deep machine learning method to identify three modulation formats at an accuracy of 100% in a wide optical signal-to-noise ratio range [13]. A simple and cost-effective MFI technique was also proposed by his teams using an artificial neural network based on asynchronous amplitude histogram (AAH) [14].…”

Section: Introductionmentioning

confidence: 99%

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A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

et al. 2019

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Featured Application: This paper provides a feasible method for modulation format identification and OSNR monitoring. The application of KNN reduces complexity.Abstract: In this study, a joint modulation format identification and optical signal-to-noise ratio (OSNR) monitoring algorithm is proposed and experimentally demonstrated using the k-nearest neighbor algorithm for intensity modulation and direct detection (IMDD) orthogonal frequency division multiplexing (OFDM) systems. A modified amplitude histogram of received signal is employed to serve as the classification feature to simplify the computation. Experimental results show that five common quadrature amplitude modulation (QAM) modulation formats, including 4-QAM, 16-QAM, 32-QAM, 64-QAM and 128-QAM, can be identified under 100% accurate estimation at the received optical power of −11 dBm. Robustness of the proposed scheme to constellation rotation is also experimentally assessed. At the same time, system OSNR monitoring also can be achieved and the average prediction mean square error (MSE) is 0.69 dB 2 , which is similar to that using an artificial neural network. Computational complexity assessment demonstrated that similar performance but less computing resource consumption can be achieved by using the proposed scheme rather than the artificial neural network-based scheme.

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Section: Experimental Verification and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

et al. 2019

View full text Add to dashboard Cite

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“…The issue of autonomous modulation format identification in digital coherent receivers (i.e., without requiring information from the transmitter) has been addressed by means of a variety of ML algorithms, including k-means clustering [64] and neural networks [66], [67]. Papers [63] and [68] take advantage of the Stokes space signal representation (see Fig.…”

Section: Modulation Format Recognitionmentioning

confidence: 99%

“…Conversely, features extracted from asynchronous amplitude histograms sampled from the eye-diagram after equalization in digital coherent transceivers are used in [65]- [67] to train NNs. In [66], [67], a NN is used for hierarchical extraction of the amplitude histograms' features, in order to obtain a compressed representation, aimed at reducing the number of neurons in the hidden layers with respect to the number of features. In [65], a NN is combined with a genetic algorithm to improve the efficiency of the weight selection procedure during the training phase.…”

Section: Modulation Format Recognitionmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

437

226

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Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

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“…In coherent optical communications, a number of techniques have been proposed in the most recent years. For example, machine learning algorithms are applied to recognize signal's amplitude histograms [5] or the Stokes space-based signal representation [6]; however, they require either prior training or iterative processing. Image processing techniques like the connected component analysis are employed in [7] to classify the modulation format in the Stokes space domain, but such features are appropriate only for lower-order modulation formats.…”

Section: Introductionmentioning

confidence: 99%

Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

Lin

Eldemerdash

Dobre

et al. 2017

IEEE Photon. Technol. Lett.

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Abstract-In this letter, we propose a modulation classification algorithm which is based on the received signal's amplitude for coherent optical receivers. The proposed algorithm classifies the modulation format from several possible candidates by differentiating the cumulative distribution function (CDF) curves of their normalized amplitudes. The candidate with the most similar CDF to the received signal is selected. The measure of similarity is the minimum average distance between these CDFs. Five commonly used quadrature amplitude modulation formats in digital coherent optical systems are employed. Optical backto-back experiments and extended simulations are carried out to investigate the performance of the proposed algorithm. Results show that the proposed algorithm achieves accurate classification at optical signal-to-noise ratios of interest. Furthermore, it does not require carrier recovery.

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Modulation Format Identification in Coherent Receivers Using Deep Machine Learning

Cited by 134 publications

References 12 publications

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

An Overview on Application of Machine Learning Techniques in Optical Networks

Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

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