scholarly journals Deep Neural Network Equalization for Optical Short Reach Communication

2019 ◽  
Vol 9 (21) ◽  
pp. 4675 ◽  
Author(s):  
Maximilian Schaedler ◽  
Christian Bluemm ◽  
Maxim Kuschnerov ◽  
Fabio Pittalà ◽  
Stefano Calabrò ◽  
...  

Nonlinear distortion has always been a challenge for optical communication due to the nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a second type of nonlinearities, which results from optical and electrical components. They become the dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments and memory effects using Volterra series. However, such Volterra equalizers are architecturally complex and their parametrization can be numerical unstable. This contribution proposes an alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated experimentally on coherent 88 Gbaud dual polarization 16QAM 600 Gb/s back-to-back measurements. The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6th orders at a target bit-error rate (BER) of 10 − 2 by 0.5 dB and 0.8 dB in optical signal-to-noise ratio (OSNR), respectively.

2019 ◽  
Vol 9 (24) ◽  
pp. 5438
Author(s):  
Feng Wan ◽  
Baojian Wu ◽  
Feng Wen ◽  
Kun Qiu

We propose an in-band measurement method of optical signal-to-noise ratio (OSNR) output from an all-optical regeneration system with a nonlinear power transfer function (PTF) according to the fact that there are different average gains of signal and noise. For the all-optical quadrature phase-shift keying (QPSK) regenerator as an example, the output OSNR is derived from the input OSNR and the total gain of the degraded QPSK signal. Our simulation shows that the OSNR results obtained by this method are in agreement with those calculated from the error vector magnitude (EVM) formula. The method presented here has good applicability for different data rates but is also useful for analyzing the OSNR degradation of other nonlinear devices in optical communication links.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Manjit Singh ◽  
Amandeep Singh Sappal

AbstractDue to increase in number of users and demand for higher data rates, the development of transmission systems which meets these requirements is of concern. Radio over Fiber (RoF) has emerged as such kind of technology which not only caters these requirements but also has the advantage of low system complexity, power consumption maintenance costs and low attenuation. But RoF systems suffer from nonlinear distortion caused by the presence of nonlinear system components. This nonlinear distortion results in intermodulation distortion and hence degrades system performance. For mitigating nonlinear distortion, a linearizer is required. A linearizer has inverse characteristics to that of RoF link. For design of the linearizer, accurate modelling of RoF link is required. So, in this paper, we have presented the modelling of a nonlinear RoF ink using cross term memory polynomial.


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3710 ◽  
Author(s):  
Jan Sticklus ◽  
Martin Hieronymi ◽  
Peter Hoeher

Optical communication promises to be a high-rate supplement for acoustic communication in short-range underwater applications. In the photic zone of oceanic and coastal waters, underwater optical communication systems are exposed by remaining sunlight. This ambient light generates additional noise in photodetectors, thus degrading system performance. This effect can be diminished by the use of optical filters. This paper investigates light field characteristics of different water types and potential interactions with optical underwater communication. A colored glass and different thin film bandpass filters are examined as filter/detector combinations under varying light and water conditions, and their physical constraints are depicted. This is underlined by various spectral measurements as well as optical signal-to-noise ratio calculations. The importance of matching the characteristics of the light emitting diode (LED) light source, the photodetector, and the filter on the ambient conditions using wider angle of incidents is emphasized.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hazem M. El-Hageen ◽  
Aadel M. Alatwi ◽  
Ahmed Nabih Zaki Rashed

AbstractThis work clarifies the analysis of the theoretical study of noise and transmission gain characteristics of semiconductor optical amplifiers (SOAs), which are relevant in the novel local area optical communication systems. We investigated the effects of noise on AlGaAs/GaAs SOA transmission performance through the measurement of output power, optical gain, the optical signal-to-noise ratio, and noise figure. It was observed that noise has a dramatic effect on SOAs’ operation transmission efficiency, and the performance of the amplifier structure may be limited. If the drive current and injection power at the SOA can be changed and its active region length modified, then the variation of gain, optical signal-to-noise ratio, and noise figure at the output of the structure can be obtained.


Author(s):  
S. Chef ◽  
C. T. Chua ◽  
C. L. Gan

Abstract Limited spatial resolution and low signal to noise ratio are some of the main challenges in optical signal observation, especially for photon emission microscopy. As dynamic emission signals are generated in a 3D space, the use of the time dimension in addition to space enables a better localization of switching events. It can actually be used to infer information with a precision above the resolution limits of the acquired signals. Taking advantage of this property, we report on a post-acquisition processing scheme to generate emission images with a better image resolution than the initial acquisition.


2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Rabiu Imam Sabitu ◽  
Nafizah Goriman Khan ◽  
Amin Malekmohammadi

AbstractThis report examines the performance of a high-speed MDM transmission system supporting four nondegenerate spatial modes at 10 Gb/s. The analysis adopts the NRZ modulation format to evaluate the system performance in terms of a minimum power required (PN) and the nonlinear threshold power (PTH) at a BER of 10−9. The receiver sensitivity, optical signal-to-noise ratio, and the maximum transmission distance were investigated using the direct detection by employing a multimode erbium-doped amplifier (MM-EDFA). It was found that by properly optimizing the MM-EDFA, the system performance can significantly be improved.


2021 ◽  
Vol 11 (4) ◽  
pp. 1499
Author(s):  
Bingchen Han ◽  
Junyu Xu ◽  
Pengfei Chen ◽  
Rongrong Guo ◽  
Yuanqi Gu ◽  
...  

An all-optical non-inverted parity generator and checker based on semiconductor optical amplifiers (SOAs) are proposed with four-wave mixing (FWM) and cross-gain modulation (XGM) non-linear effects. A 2-bit parity generator and checker using by exclusive NOR (XNOR) and exclusive OR (XOR) gates are implemented by first SOA and second SOA with 10 Gb/s return-to-zero (RZ) code, respectively. The parity and check bits are provided by adjusting the center wavelength of the tunable optical bandpass filter (TOBPF). A saturable absorber (SA) is used to reduce the negative effect of small signal clock (Clk) probe light to improve extinction ratio (ER) and optical signal-to-noise ratio (OSNR). For Pe and Ce (even parity bit and even check bit) without Clk probe light, ER and OSNR still maintain good performance because of the amplified effect of SOA. For Po (odd parity bit), ER and OSNR are improved to 1 dB difference for the original value. For Co (odd check bit), ER is deteriorated by 4 dB without SA, while OSNR is deteriorated by 12 dB. ER and OSNR are improved by about 2 dB for the original value with the SA. This design has the advantages of simple structure and great integration capability and low cost.


2019 ◽  
Vol 9 (11) ◽  
pp. 326 ◽  
Author(s):  
Hong Zeng ◽  
Zhenhua Wu ◽  
Jiaming Zhang ◽  
Chen Yang ◽  
Hua Zhang ◽  
...  

Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.


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