Optical Signal-to-noise Ratio Research Articles

Multibeam FSO-based 5G communication system using M-ary DPSK encoder

Abstract Optical wireless technologies like Free Space Optics (FSO) have proven to be a perfect choice for 5G communication systems. FSO can offer low latency, high capacity, extensive connectivity, and high reliability at a low cost. However, the major limitation of FSO is its susceptibility to atmospheric turbulences, especially rain at high frequencies. In this paper, a rain efficient 10 Gbps 5G communication system using multibeam FSO and multilevel Differential Phase Shift Keying (M-ary DPSK) encoder is proposed. To evaluate the system performance at different rain intensities, the state-of-the-art Charbonneau channel model for rain attenuation has been used. The performance efficiency of the proposed system is evaluated in terms of quality factor (Q-factor), bit error rate (BER), optical signal-to-noise ratio (OSNR), and received power. The results convey that the proposed system can perform well up to 4.25 km, 3.24 km, and 2.55 km under below average, average, and above average rain conditions, respectively, with attenuation of 6.28 dB/km, 9.65 dB/km, and 19.27 dB/km, respectively. Moreover, the comparative analyses show that the proposed system performs better than the recent conventional systems in terms of bandwidth and power efficiency.

Modulation format recognition with transfer learning assisted convolutional neural network using multiple Stokes sectional plane image in multi-core fibers.

A modulation format recognition (MFR) scheme based on multi-core fiber (MCF) is proposed for the next generation of elastic optical networks (EONs). In this scheme, multiple Stokes sectional planes images are used as signal features which are typed into a transfer learning (TL) assisted convolutional neural network (CNN) to realize MFR. Compared with the traditional Jones matrix, the Stokes space mapping method is insensitive to polarization mixing, carrier frequency skew and phase offset, therefore, it has better feature representation ability. TL is introduced to transfer the model used in standard single-mode fiber (SSMF) to MCF transmission, reducing the required training data and complexity. In addition, multiple Stokes sectional planes images are input simultaneously, which improves the accuracy of the neural network. Experimental verifications were performed for a polarization division multiplexing (PDM)-EONs system at a symbol rate of 12.5GBaud by 5 km MCF. Nine modulation formats, including three standard modulation formats (BPSK, QPSK, 8PSK), three uniformly shaped (US) modulation formats (US-8QAM, US-16QAM, US-32QAM) and three probabilistically shaped (PS) modulation formats (PS-8QAM, PS-16QAM, PS-32QAM), were recognized by our scheme. The experimental results show that the scheme achieves high recognition accuracy even at low optical signal-to-noise ratio (OSNR). Moreover, the required number of training samples is less 40% compared to the traditional CNN. The proposed scheme has a high tolerance to the crosstalk damage of MCF itself and can realize the short training time of large-capacity space division multiplexing (SDM)-EONs. Our findings have the potential to be used in the next generation of a SDM fiber transmission system.

Challenges and Enabling Technologies for Multi-Band WDM Optical Networks

The ever increasing capacity demand on optical networks and the slowdown of improving spectral efficiency lead to the solution of utilizing more wavelength band in existing optical fibers. More and more operators will extend from C band to C+L bands, or plan to deploy C+L systems, and in future they may utilize more of other bands. This paper firstly discusses different optical layer architectures for multi-band system. Using optical components that can natively support multi-band is attractive to us, because in this way the multi-band system can be planned and operated similarly with the single-band system, which is customer friendly and potentially more economical. We address mainly three technical challenges in this paper. The first one is the wide-band fiber optical amplifier (OA). After a short review of recent research progress, we propose a hybrid erbium-doped fiber (EDF) and bismuth-doped fiber (BDF) based OA approach, and demonstrated single-stage amplification over 100 nm in extended C+L bands. The second challenge is the optical cross connect based on wavelength selective switch (WSS). It is not trivial to realize a C+L-band WSS while preventing the key optical parameters (filtering bandwidth, loss, channel isolation, etc.) to be degraded. We analyze different technical schemes, and propose an optical design based on an LCoS with a large panel size and a high-dispersion diffraction grating. The optical design and simulation on a 2×35 WSS reveals this approach can support 100-nm extended C+L spectrum and is promising for commercialization. The third challenge is the SRS induced WDM channel power inequality, which is more severe for multi-band system. We propose a solution by using OAs with a novel optical spectrum processor (OSP). Simulation and experiment showed that the power of the WDM channels can be equalized on a per span basis, which can prevent accumulation of stimulated Raman scattering (SRS) induced wavelength division multiplexing (WDM) channel power transfer and can meanwhile keep the optical signal to noise ratio (OSNR) of WDM channels well equalized.

A Selectable Single-Mode Erbium Fiber Laser With Mach-Zehnder Interferometer and Rayleigh Injection Scheme

In the paper, a hybrid Mach-Zehnder interferometer (MZI) and Rayleigh backscattering (RB) injection is designed and applied in the erbium-doped fiber (EDF) ring laser configuration. The MZI scheme and RB-induced signal can accomplish the single-longitudinal-mode (SLM) oscillation and narrow the linewidth to kHz target for each generated wavelength over the available wavelength-tuning bandwidth. In the measurement, the output power, optical signal to noise ratio (OSNR) and wavelength linewidth of the designed fiber laser are also executed and discussed.

Intelligent equally weighted multi-task learning for joint OSNR monitoring and modulation format identification

A novel scheme for combining modulation format identification (MFI) and optical performance monitoring (OPM) in elastic optical networks using convolutional-neural-network-based and equally weighted multi-task learning is proposed. In this scheme, shared features of optical signal-to-noise ratio (OSNR) and modulation formats (10 GBaud QPSK, QAM8, QAM16, QAM64) are extracted from constellation maps and used for multi-label classification. Simulation results demonstrate that the accuracies of MFI and OSNR can reach 100% stably and continuously. Moreover, we evaluated the robustness of this model in a system with fixed physical parameters and concluded that it is tolerant to chromatic dispersion and transmission distance. This technology predicts an image in ∼0.51 ms. Compared with the general multi-task learning (MTL) scheme, the proposed approach massively saves execution time (∼107 h) by removing weight selection. It is time-efficient and intelligent for OPM equipment that will perform multiple monitoring tasks in next-generation optical networks.

Tunable multiwavelength Erbium-doped fiber laser based on in-fiber Fabry-Perot interferometer fiber Bragg gratings in linear and ring cavity configurations

Multiwavelength Erbium-doped fiber laser (MWEDFL) in the ring and linear cavities operating at 1.5-μm wavelength region was demonstrated. The multiwavelength fiber laser comprises a 3-m Erbium-doped fiber as an active gain medium, an in-fiber Fabry-Perot interferometer Fiber Bragg Gratings (FPI FBG) as a filter, and a 10-km dispersion compensating fiber (DCF) as a nonlinear gain medium. A comparison investigation was conducted. The MWEDFL in the linear cavity generates 12 stable lasing lines, with an optical signal-to-noise ratio (OSNR) of 45 dB. In the case of MWEDFL operated in a ring cavity, 8 stable lasing lines were generated, with an OSNR of 47 dB. The free spectral range (FSR) of 0.08 nm was measured for the FPI FBG in both linear and ring cavities MWEDFL spectrum. Besides, the stability measurement was conducted for 150 min for both MWEDFL cavities. Both lasers had a peak power fluctuation of less than 1.0 dB and negligible wavelength drifts below 0.1 nm. Furthermore, the wavelength tunability of MWEDFL in both configurations was performed by heating the FPI FBG from 28 °C to 100 °C. At the temperature of 100 °C, the MWEDFL shows a wavelength shift of 1.0 nm, and this can be further increased by heating the FPI FBG to its highest temperature limit. This MWEDFL employs in-fiber FPI FBG offers a compact, highly tunable, and simple design applicable for optical communications and optical sensing applications.

Supervised graph convolution networks for OSNR and power estimation in optical mesh networks

The optical signal-to-noise ratio (OSNR) and received optical channel power are critical parameters in determining the quality of transmission. The OSNR and received optical channel power are influenced by network impairments such as fiber loss, amplified stimulated emission noise, and nonlinear impairments. Furthermore, environmental effects and routing, modulation, and spectrum assignment schemes influence the OSNR and thus the reach of the optical channels. These impairments and effects vary with the spectral loads that are hard to predict in brownfield networks. Several deep neural network (DNN)-based methods have been explored to estimate the OSNR and nonlinear noise. However, these methods ignore the network topology. This paper bridges this gap by leveraging supervised graph convolution neural networks (GCNs), which operate directly on graphs for OSNR and received power estimation in an optical mesh network. We also develop and implement a novel graph windowed neural network (GWinN) to reduce the over-smoothing effects of a GCN and thus learn localized behaviors like fiber cuts. We apply a DNN, GCN, and GWinN in practice to a testbed of 8 reconfigurable optical add-drop multiplexers and 22 amplifiers. Our procedure accurately estimates the OSNR with a prediction error mean and a standard deviation of ( − 0.02 d B , 0.35 dB) for a reference OSNR ranging from (16 dB) to (24 dB).

A Stabilized Single-Longitudinal-Mode and Wide Wavelength Tunability Erbium Laser

An erbium-doped fiber (EDF) laser with quad-ring is designed to reach the broad continuous-wave (CW) tunability and single-longitudinal-mode (SLM) behavior. Here, while a C-band erbium-based gain medium is exploited, the tunable scope can be extended from C- to part of L-bands and narrow the linewidth to several kHz by the presented compound-ring configuration. Additionally, the optical signal to noise ratio (OSNR), output power, and stability behavior of every lasing wavelength are also demonstrated.

Optimising microring resonator based optical frequency comb distillation for optical communications systems.

Microring resonators (MRR) can be used as devices for filtering out broadband noise on optical frequency combs, in cases where significant amplification of a generated comb is required. While comb distillation has been demonstrated experimentally for optical communication systems, approaches to optimise device and sub-system parameters have not been explored. Here, we investigate how the performance of comb distillation through micro-ring filtering depends on device parameters. We also explore device parameter dependent performance when the comb and MRR are misaligned in line spacing. For the device platform we investigate, we find that the required optical signal-to-noise ratio (OSNR) of a comb line can be reduced by 16 dB, independent of modulation format, using a MRR with a resonance bandwidth of 100 MHz and coupling loss of 3 dB.

Machine Learning-Based Optical Performance Monitoring for Super-Channel Optical Networks

In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings.

Highly stable multi-wavelength fiber laser based on hybrid enclosed two-armed fiber filter

A compact multi-wavelength fiber laser (MWFL) with high stability based on hybrid enclosed two-armed fiber filter (HETAFF) is demonstrated. The HETAFF composes of an enclosed Mach-Zehnder interferometer with an in-line polarization controller (PC) in one arm of optical couplers #1 (OC1) and a section of polarization maintaining fiber with two PCs in OC2 loop. By using the HETAFF in proposed fiber laser, a lasing state switchable and wavelength-interval adjustable MWFL is obtained. It can be switched in single-, dual-, triple-, quadruple- and quintuple-wavelength states. The wavelength-interval can be adjusted as it operates in dual-, triple- and quadruple-wavelength states. Its optical signal to noise ratio (OSNR) is more than 36.03 dB, and the lasing wavelength shift and power fluctuation are less than 0.08 nm and 0.94 dB, respectively. The output characteristics, such as abundant laser states and high stability, are inseparable from the unique performance of the HETAFF.

Transfer learning approach toward joint monitoring of bit rate and modulation format.

Convolutional neural network based transfer learning (TL) is proposed to achieve joint optical performance monitoring with bit rate and modulation format identification in optical communication systems. TL is used to improve the execution of various tasks by extracting features without knowing other optical link parameters. Eye diagrams of four different modulation formats are generated at optical signal-to-noise ratios (OSNRs) varying from 15 to 30 dB for two distinct bit rates, which are then identified simultaneously with a trained deep neural network. In addition, comparisons of different TL approaches are presented. The database is divided into distinct categories with varying parameter ranges in offline mode, and prediction models are assigned to each class. The results suggest that the proposed system may greatly increase identification performance over existing strategies by utilizing TL techniques. The impacts of training, testing, and validation data size, as well as model structure based on TL, are also thoroughly investigated. The results reveal that the VGG16 achieves the highest accuracies compared to other deep learning algorithms even at low OSNR values of 20 dB. The proposed structure can intelligently evaluate the signals of future heterogeneous optical communications, and the results can be used to enhance optical network management.

Meta-learning-enabled accurate OSNR monitoring of directly detected QAM signals with one-shot training

We experimentally demonstrate meta-learning-enabled accurate optical signal-to-noise ratio (OSNR) monitoring of directly detected 16QAM signals with extremely few training data. When one-shot training, where one amplitude histogram (AH) for each OSNR value includes only 2000 data samples, is implemented for a 16QAM signal within a variable OSNR range of 15-24 dB, the experimental root mean squared error (RMSE) of the retraining technique is 1.53 dB. For transfer learning from the 16QAM simulation to the experimentally generated AH, the RMSE can be reduced to 1.11 dB. In comparison with both the retraining and transfer learning techniques, the RMSE of meta-learning-enabled OSNR monitoring can be further reduced by 42.8% and 22.3%, respectively. In order to reach the optimal accuracy with an RMSE of 0.66 dB, the meta-learning technique requires only 15 AHs for each OSNR value to be monitored, while the retraining and the transfer learning techniques need 20 and 25 AHs, respectively.

Optical performance monitoring in transparent fiber-optic networks using neural networks and asynchronous amplitude histograms

Recently, technologies such as artificial neural networks (ANNs) and asynchronous amplitude histograms (AAHs) are widely employed in the optical performance monitoring (OPM). In this paper, the number of layers and the optimization of the neural networks are investigated to complete a series of monitoring tasks in optical communication systems with a large range of chromatic dispersion (CD) and optical signal-to-noise ratio (OSNR). It is shown that the monitoring accuracy has been significantly improved with such implementations. Numerical simulations, which have been conducted for six different modulation formats, demonstrate a good monitoring performance, with an average OSNR monitoring error of 0.1064 dB (99.49% monitoring accuracy) for the OSNR range of 10–40 dB and an average CD monitoring error of 3.3324 ps/nm (99.45% monitoring accuracy) for the CD range of 170–1870 ps/nm, respectively. Furthermore, the CD monitoring and the modulation format identification (MFI) have also been investigated for the system with a large range of dispersion. An average CD error of 16.6832 ps/nm (99.45% monitoring accuracy) and a 100% MFI accuracy have been achieved for all six modulation formats. This scheme is also verified experimentally with OSNR errors of 0.41 dB and CD errors of 15.21 ps/nm, respectively. The identification accuracy of the three modulation formats in the experimental system is also 100%.

Performance investigation of the probability-aided maximum likelihood sequence detector for a probabilistic shaped 16-QAM system.

In this paper, for the first time, a probability-aided maximum-likelihood sequence detector (PMLSD) is experimentally investigated through a 64-GBaud probabilistic shaped 16-ary quadrature amplitude modulation (PS-16QAM) transmission experiment. In order to relax the impacts of PS technology on the decision module, a PMLSD decision scheme is investigated by modifying the decision criterion of maximum-likelihood sequence detector (MLSD) correctly. Meanwhile, a symbol-wise probability-aided maximum a posteriori probability (PMAP) scheme is also demonstrated for comparison. The results show that the PMLSD scheme outperforms the direct decision scheme about 1.0-dB optical signal to noise ratio (OSNR) sensitivity. Compared with symbol-wise PMAP scheme, PMLSD scheme can effectively relax the impacts of PS technology on the decision module and a more than 0.8-dB improvement in terms of OSNR sensitivity in back-to-back (B2B) case is obtained. Finally, we successfully transmit the PS-16QAM signals over a 2400-km fiber link with a bit error ratio (BER) lower than 1.00×10-3 by adopting the PMLSD scheme.

Impact of Fiber Attenuation and Effective Area on Spectrum Efficiency of Elastic Optical Networks

The ultra-low-loss optical fibers becoming available now offer large effective areas with much better transmission performance than conventional standard single-mode fibers (SSMFs). However, how low the attenuation should be and how large the effective area has to be to benefit the transmission performance of a fiber-optic system have been open questions until now. In the context of an elastic optical network (EON), this paper evaluates for the first time the extent to which both fiber attenuation and effective area impact the network capacity. We first present an optical signal-to-noise ratio (OSNR) model to evaluate the performance of a lightpath considering various optical transmission impairments. We also propose different service-provisioning approaches based on this OSNR model. Simulation studies show that a spectrum-dependent provisioning scheme that considers different levels of cross-channel interference (XCI) requires minimum spectrum resources when provisioning incremental demands, and a margin-reservation scheme that considers different alternate routes shows the lowest bandwidth blocking probability (BBP) when provisioning dynamic lightpath demands. We also estimate optimal launch powers needed by lightpaths for different fiber attenuations and effective areas. Under different launch powers, it is found that lowering fiber attenuation and increasing fiber effective area demonstrate saturation in performance improvement, i.e., initially decreasing fiber attenuation and increasing effective area can significantly improve the network service provisioning performance, but the improvement is much less when the fiber attenuation and effective area are lower than 0.165 dB/km and greater than 110 μm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. This implies that it is not necessary to fabricate a fiber with extremely low attenuation and large effective area for the best cost-to-performance ratio.

Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks

The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in super-channel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M = 8, 16, 32, 64). The classification performance is assessed under different values of optical signal-to-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1^{st}$</tex-math></inline-formula> polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.

Multi-Task Learning Convolutional Neural Network and Optical Spectrums Enabled Optical Performance Monitoring

We propose a simultaneous optical signal recognition (OSR) and optical signal-to-noise ratio (OSNR) monitoring method by using multi-task learning convolutional neural network (MTL-CNN) in conjunction with optical spectrums, which enables optical performance monitoring (OPM) in the optical transmission network. In order to achieve a trade-off between monitoring loss and time consumption of the MTL-CNN constructed for seven commonly used signals with a spectrum resolution of 10 pm, the number of feature map channels in four convolutional layers is delicately designed as 32, 32, 64, and 64 with task weights set to 0.4 and 0.2, corresponding to OSNR monitoring and OSR, respectively. Simulation results manifest that this method can realize the recognition of the received optical signals with an overall accuracy of 100% and also enable OSNR monitoring with the mean absolute error (MAE) of 0.262 dB. The proposed method shows strong robustness to various distortions and exhibits good performance in terms of time consumption. The effectiveness is further verified by proof-of-concept experiments in three signals. These illustrate that our method is a promising solution for multi-parameter monitoring with high accuracy and efficiency.

Optimization of system’s parameters for wavelength conversion of E-band signals

Current and future wireless communication systems are designed to achieve the user’s demands such as high data rate and high speed with low latency and simultaneously to save bandwidth and spectrum. In 5G and 6G networks, a high speed of transmitting and switching is required for internet of things (IoT) applications with higher capacity. To achieve these requirements a semiconductor optical amplifier (SOA) is considered as a wavelength converter to transmit a signal with an orthogonal frequency division multiplexing with subcarrier power modulation (OFDM-SPM). It exploits the subcarrier’s power in conventional OFDM block in order to send additional bits beside the normally transmitted bits. In this paper, we optimized the SOA’s parameters to have efficient wavelength conversion process. These parameters are included the injection current (IC) of SOA, power of pump and probe signals. A 7 Gbps OFDM-SPM signal with a millimeter waves (MMW) carrier of 80 GHz is considered for signal switching. The simulation results investigated and analyzed the performance of the designed system in terms of error vector magnitude (EVM), bit error rate (BER) and optical signal-to-noise ratio (OSNR). The optimum value of IC is 0.6 A while probe power is 9.45 and 8.9 dBm for pump power. The simulation is executed by virtual photonic integrated (VPI) software.

3.1 kW 1050 nm narrow linewidth pumping-sharing oscillator-amplifier with an optical signal-to-noise ratio of 45.5 dB.

In this paper, the amplified spontaneous emission (ASE) suppression in a 1050 nm fiber laser with a pump-sharing oscillator-amplifier (PSOA) structure is studied theoretically and experimentally. A theoretical model of a fiber laser with a PSOA structure is established. The characteristics of the ASE for the PSOA structure and the pump-independent oscillator-amplifier (PIOA) structure are compared and analyzed. The experimental results show that the ASE can be effectively suppressed by utilizing the PSOA structure, which agree with the simulation results. A 1050 nm high-power narrow-linewidth fiber laser with PSOA structure is demonstrated, in which the gain fiber lengths of the oscillator and amplifier are 1.6 m and 9 m, respectively, to ensure the interconnection of pump power between the oscillator and amplifier. Finally, the maximum output power of 3.1 kW has been achieved, the linewidth is 0.22 nm at 3 dB, the beam quality M2 ≈ 1.33, and the optical signal-to-noise ratio (OSNR) is 45.5 dB.