Q-factor Improvement Research Articles

Multiple-signal-joint-processing-based guard interval shortening method for a CS-NFDM system.

Aiming to further improve the spectral efficiency (SE) of a continuous spectrum modulated nonlinear frequency division multiplexing (CS-NFDM) system, we propose a novel ,to the best of our knowledge, multiple-signal-joint-processing (MSJP)-based guard interval (GI) shortening method. In this method, multiple NFDM time-domain signals are jointly processed as a whole to carry out nonlinear Fourier transform and inverse nonlinear Fourier transform (NFT-INFT) operations. These operations can fuse the multiple NFDM time-domain signals together, which is equivalent to the corresponding inverse process of a fiber transmission. Experimental results show that the normalized SE of the proposed method can reach 0.99 when approaching the limit value of 1 and obtain a 2.33 dB Q2-factor improvement compared with the pre-dispersion compensation (PDC) method under the same GI of 0.03 ns in an 80 km SSMF transmission of a 46 GHz signal bandwidth. Furthermore, in comparison with the PDC method, the proposed method can achieve 32.86% normalized SE improvement in the 1120 km SSMF transmission of 32 GHz signal bandwidth under the SD-FEC of 2.4E-2.

Novel mirror-flipped mode permutation technique for long-haul 6-mode transmission

Mode-division multiplexing based on few-mode fibers is expected to overcome capacity limits of current single-mode fiber communications. Mode permutation as an approach for extending transmission reach has attracted great attention. In this paper, we propose a mirror-flipped mode permutation (MFMP) scheme based on newly developed fibers with symmetric differential mode delay (DMD). We demonstrate a 28-GBaud transmission over a 1542-km 6-mode fiber link, which suppresses modal-dispersion impact by 36% and mitigates mode-dependent-loss effect with 3.1-dB Q-factor improvement, compared with the traditional cyclic mode permutation at 1233 km. Then, we experimentally verify that the proposed MFMP works well even using a fiber with a not exactly symmetric group delay distribution. Benefiting from a low DMD in this fiber, we demonstrate a 6-mode PM-QPSK transmission at 25 GBaud with 16-λ channels, extending the transmission distance to 3000 km.

On the generalization of cognitive optical networking applications using composable machine learning

Model generalization characterizes the sustainability of machine learning (ML) designs applied to novel system states and therefore plays a vital role toward the realization of cognitive networking. In this paper, we present a composable ML framework (namely, CompML), aiming at generalizing ML-aided cognitive applications for optical networks. CompML makes use of three basic functional modules, i.e., the Loading, Recursion, and Readout modules, to model the loading/initialization processes (e.g., the launch of a signal), extract cumulative features by recursive operations, and produce model inferences, respectively. By the composition of the three modules and adoption of an end-to-end training mechanism, CompML allows for generalizing multiple tasks of the same domain [e.g., quality-of-transmission (QoT) estimation for different lightpaths]. We perform case studies of CompML on QoT estimation and nonlinearity compensation using both simulation and experimental data. Results show the superior generalization ability of CompML compared with the baselines, achieving mean absolute error (MAE) for generalized signal-to-noise ratio (GSNR) prediction error of below 1.06 dB for unseen lightpaths and up to 3 dB Q-factor improvement for nonlinearity compensation.

Joint intra and inter-channel nonlinear compensation scheme based on improved learned digital back propagation for WDM systems.

In this paper, we improve the learned digital back propagation (LDBP) and propose a novel joint intra and inter-channel nonlinearity compensation scheme for polarization division multiplexing wavelength-division multiplexed (PDM-WDM) systems. From the perspective of interpretable neural network, the scheme realizes the alternating compensation of chromatic dispersion (CD) and nonlinearity based on physical models. The chromatic dispersion compensation (CDC) adopts one-dimensional convolution operation in the time domain. Moreover, the pulse-broadening effect is introduced into the overlap-and-save method. For nonlinear compensation, the improved joint model is applied, and the impact of the intra-channel pulse broadening and the walk-off effect between different channels caused by CD on the nonlinear effect is considered. To validate the effectiveness of the proposed scheme, we construct an 11-channel simulation system of 36 GBaud PDM uniform 16 quadrature amplitude modulation (PDM-16QAM) 1600 km and 64 GBaud PDM-64QAM 400 km, as well as a 5-channel experimental system of 28 GBaud PDM-16QAM 806.4 km. The simulation results show that the performance of PDM-16QAM with 0.5 steps per span and PDM-64QAM with 2 steps per span improve the Q-factor by approximately 0.75 dB and 0.54 dB at the optimal launch power, compared with the linear compensation scheme. The transmission performance of PDM-16QAM is higher than that of digital back propagation with 5 steps per span (DBP-5StPS), and the complexity is only 31.36% of that of DBP-5StPS. The performance of PDM-64QAM is higher than that of DBP-10StPS, with a complexity of 62.72%. The experimental results show that the performance of PDM-16QAM with 0.5 steps per span is improved with 0.86 dB Q-factor improvement compared with the linear compensation scheme at the optimal launch power, and the performance of the proposed scheme is higher than that of DBP-5StPS with a complexity of only 23.68%.

Generalized Rayleigh quotient optimization method for inter-channel nonlinearity compensation in coherent optical communication systems.

Inter-channel nonlinearity compensation plays a crucial role in wavelength division multiplexing (WDM) systems for improving transmission capacity and distance. In this work, we propose a novel, to the best of our knowledge, inter-channel nonlinearity compensation method called generalized Rayleigh quotient optimization (GRQO) method with two different working modes. In an 8 × 64 GBaud 16-ary quadrature amplitude modulation (16-QAM) experimental system over 1600 km standard single-mode fiber (SSMF), the proposed method shows a 0.40 dB Q2 factor improvement over nonlinear polarization cross talk canceller (NPCC) with a moderately low computational complexity of about 2000 real multiplications per bit (RMb).

Fast fiber nonlinearity compensation method for PDM coherent optical transmission systems based on the Fourier neural operator.

Fiber nonlinearity compensation (NLC) is likely to become an indispensable component of coherent optical transmission systems for extending the transmission reach and increasing capacity per fiber. In this work, we introduce what we believe to be a novel fast black-box neural network model based on the Fourier neural operator (FNO) to compensate for the chromatic dispersion (CD) and nonlinearity simultaneously. The feasibility of the proposed approach is demonstrated in uniformly distributed as well as probabilistically-shaped 32GBaud 16/32/64-ary quadrature amplitude modulation (16/32/64QAM) polarization-division-multiplexed (PDM) coherent optical communication systems. The experimental results demonstrate that about 0.31 dB Q-factor improvement is achieved compared to traditional digital back-propagation (DBP) with 5 steps per span for PDM-16QAM signals after 1600 km standard single-mode fiber (SSMF) transmission at the optimal launched power of 4 dBm. While, the time consumption is reduced from 6.04 seconds to 1.69 seconds using a central processing unit (CPU), and from 1.54 seconds to only 0.03 seconds using a graphic processing unit (GPU), respectively. This scheme also reveals noticeable generalization ability in terms of launched power and modulation format.

Study of all-optical mode regeneration based on few-mode Mach–Zehnder interferometer

Mode division multiplexing (MDM) technology is a promising approach to improve the spectral efficiency of optical fibers. In MDM systems, all-optical mode regenerators can suppress the amplified spontaneous emission (ASE) noise and crosstalk of few-mode signals directly in the optical domain. In this paper, we focus on the mode regenerator based on few-mode Mach–Zehnder interferometer (FM-MZI) with a highly-nonlinear fiber, and propose an optimization method for the working points (WPs) of mode regeneration. An FM-MZI regenerator capable of supporting LP01, LP11, and LP21 modes is optimally designed for on/off keying (OOK) signals and the simulation results is in agreement with the theoretical analysis. Our simulation shows that, (1) when the input optical signal-to-noise ratios (OSNRs) of the three modes are 25 dB, 27 dB, and 27.5 dB, the FM-MZI regenerator has the maximum OSNR improvement by 1.7 dB, 1.5 dB, and 1.51 dB, respectively; (2) the introduction of the mode regenerator can reduce the bit error ratio (BER) of the 10 Gbit/s OOK system from 10−9 to 10−11, equivalent to the OSNR improvement by 1.87 dB, 2 dB, and 1.76 dB at BER = 10−9 for the three modes, respectively. In addition, we discuss the influence of optimization precision for the optical coupler, Q-factor improvement at different data rates, and Phase-preserving regeneration characteristics.

Intelligent and adaptive intra-channel and inter-channel fiber nonlinearity compensation in coherent optical transmission systems.

Fiber nonlinearity mitigation is a crucial technology for extending transmission reach and increasing channel capacity in high-baud rate wavelength division multiplexing (WDM) systems. In this work, we propose a novel, to the best of our knowledge, architecture that combines learned modified digital back-propagation (L-MDBP) to compensate for intra-channel nonlinearity and a two-stage decision-directed least mean square (DDLMS) adaptive equalizer to mitigate inter-channel nonlinearity. By leveraging globally optimized model parameters and adaptive channel estimation, the proposed scheme achieves superior performance and lower computation complexity compared with conventional DBP. Specifically, in an 8 × 64 Gbaud 16-ary quadrature amplitude modulation (16QAM) experimental system over 1600 km of standard single-mode fiber (SSMF), our approach shows a 0.30-dB Q2-factor improvement and a complexity reduction of 82.3% compared with DBP with 8 steps per span (SPS). Furthermore, we enhance the adaptability of the architecture by introducing an online transfer learning (TL) technique, which requires only 2% of initial training epochs.

An enhanced 2-D color space diversity using RGB-LED overlapping for optical camera communication

In this paper, an enhanced two-dimension (2-D) color space diversity technique using red, green, and blue light emitting diode (RGB LED) overlapping is proposed and investigated experimentally to improve the system reliability for optical camera communication (OCC). The transmitter adopts an 8-color shift keying (8-CSK) modulation scheme with two RGB LED overlapping. The receiver uses a camera to form a multi-input single-output system to improve capacity. Different mapping methods are proposed and compared in 2-D YCbCr and CIElab color space to avoid the influence of brightness in conventional 3-D RGB space. Besides, clustering-based demapping is applied in the OCC system to transfer the color information into a data stream. The experimental results show that the mapping scheme with the maximum Euclidean distance in 2-D YCbCr color space has the best bit error rate (BER) performance. Compared with the conventional 3-D RGB color space, the proposed enhanced 2-D YCbCr color space diversity has a higher resolution to signal, which can realize 1.46 dB Q-factor improvement at an illumination of 660 lux. In addition, it can achieve a net data rate of 11.52 kb/s and maintain a 7% forward error correction (FEC) requirement even under low illumination. It is verified that the proposed scheme is a potential application for capacity and performance improvement.

Performance analysis of spectrally shaped DDO-OFDM based on nonlinear differential coding and real-valued precoding

This paper presents a cost-effective spectrally shaped quadrature amplitude modulation (SS-QAM) signal in bandwidth limited direct detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems. The concatenation of the nonlinear differential coding (NLDC) and real-valued precoding sequentially implements spectral-shaping and multi-carrier modulation. Real-value discrete cosine transform (DCT) and discrete Hartley transform (DHT) are used to realize the uniform distribution of the fluctuation signal-to-noise ratio (SNR) and reduce the peak-to-average power ratio (PAPR). To broaden the flexibility of constellation mapping and improve the fine granularity of signals, six modulation formats are analyzed and evaluated in DDO-OFDM systems. Results show that SS-QAM is superior to uniformly distributed QAM in terms of reducing bit error rate (BER) and increasing normalized generalized mutual information (NGMI). Compared to the uniform 25/36/49/64/81/100 QAM OFDM signal, when the transmission distance is 20 km, SS-25/36/49 QAM signal can achieve error free transmission and SS-64/81/100 QAM can achieve 0.9971/1.0037/1.0255 Q-factor improvement and 0.0009/0.0014/0.0015 NGMI gain. When the transmission distance is 50 km, SS-25/36/49/64/81/100 QAM can achieve 0.4182/0.5169/0.9311/0.9988/1.0216/1.1487 Q-factor improvement and 0.0004/0.0008/0.0031/0.0043/0.0066/0.0083 NGMI gain. When the transmission distance is 80 km, SS-25/36/49/64/81/100 QAM can achieve 0.4754/0.7486/1.181/1.2012/1.2302/1.3904 Q-factor improvement and 0.0015/0.0046/0.0108/0.0152/0.0172/0.0205 NGMI gain. In addition, the Q-factor and NGMI gain increases as the fiber length increases, which verifies that the presented scheme is robust to the nonlinear effect.

Meta-Learning Assisted Source Domain Optimization for Transfer Learning Based Optical Fiber Nonlinear Equalization

Transfer learning (TL) has been demonstrated its feasibility on fast remodeling for fiber nonlinearity equalization. It will be very efficient with fine-tuning rather than retraining when the channel parameters have been changed, e.g., the optical launch power and fiber distance. However, the performance is dependent on the highly correlation between the source and target tasks. An improper source domain cannot improve the efficiency or even negative transfer. In this paper, we propose a meta-learning assisted source domain optimization scheme in single- and multi-channel transmissions. We construct the support dataset and query dataset from the information of the inner-task and the across-task to train the neural network so that we can find a unified source domain and efficiently adapt to different tasks with fewer epochs. We conduct an experiment to transmit 120-Gb/s dual polarization (DP) 16-QAM signal over 800-km standard single mode fiber (SSMF). The required epochs for convergence can be significantly reduced and there is slight Q-factor improvement with meta-learning algorithm compared with retraining and conventional TL. We also analyze the TL strategy in multi-channel system. The simulation results show that meta-learning can also help to optimize the source domain with lower complexity.

Performance evaluation of fiber impairment mitigation for high capacity communication systems using optical compensation method

Due to their ability to boost transmission rate and reach by compensating for Kerr nonlinearity-induced distortions, optical compensation techniques are interesting for broadband wavelength division multiplexing. This is due to the fact that these techniques have been shown to increase transmission rate. Yet, in the majority of instances, they call for more devices to be integrated into the network. This can be challenging for systems that are already in place, and it can be even more challenging for transmissions that are not repeated. The linear and nonlinear compensation system for the Dense Wave Division Multiplexing (DWDM) transmission system has been created and researched in this work. Along the optical link, a new method known as optical phase conjugation (OPC) is used as part of the process of improving the performance of communication systems. This method is used to improve the performance of 896 Gb/s sixteen channels dual polarization signalling of 50 GHz channel spacing over 800 km with three scenarios that use mid and multiple OPC. The results of the simulation indicate that multiple OPC with backward Raman amplifier method on the received signal is effective for nonlinearity compensation when compared with the mid OPC module in terms of enhanced Q-factor improvement, BER performance, EVM, and maximum transmission link length.

Low-Bandwidth Photonics-Assisted Receiver for Broad-Bandwidth Wireless Signals

This paper introduces a photonics-assisted receiver that enables the reception of high-bandwidth wireless signals with low-bandwidth electronics. The receiver down-converts the input signal into parallel low-bandwidth sub-signals, employing photonics-based orthogonal sampling. This sampling is based on a multiplication and not switching, so, it does not introduce additional aperture jitter. Therefore, the photonics-assisted analog-to-digital converter (ADC) converts the wireless signal with a higher signal-to-noise-and-distortion ratio (SINAD), which improves the Q-factor for the detection. This Q-factor improvement is especially high, when the orthogonal sampling is carried out with low-jitter oscillators. Compared to the direct detection with 30 GHz, the simulation demonstrates a 2.2 dB Q-factor enhancement for the detection of a 30 GHz signal, with 10 GHz electronics. The same improvement is revealed in the experiment for the detection of 12 GHz signals with 4 GHz electronics.

A silicon photonic–electronic neural network for fibre nonlinearity compensation

In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on the digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonice-lectronic neural network for solving fibre nonlinearity compensation of submarine optical fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a CMOS-compatible silicon photonic platform. We show that the platform can be used to compensate optical fibre nonlinearities and improve the signal quality (Q)-factor in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a 32-bit graphic processing unit-assisted workstation. Our reconfigurable photonic-electronic integrated neural network promises to address pressing challenges in high-speed intelligent signal processing.

Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical Systems

We present the results of the comparative analysis of the performance versus complexity for several types of artificial neural networks (NNs) used for nonlinear channel equalization in coherent optical communication systems. The comparison has been carried out using an experimental set-up with transmission dominated by the Kerr nonlinearity and component imperfections. For the first time, we investigate the application to the channel equalization of the convolution layer (CNN) in combination with a bidirectional long short-term memory (biLSTM) layer and the design combining CNN with a multi-layer perceptron. Their performance is compared with the one delivered by the previously proposed NN equalizer models: one biLSTM layer, three-dense-layer perceptron, and the echo state network. Importantly, all architectures have been initially optimized by a Bayesian optimizer. We present the derivation of the computational complexity associated with each NN type -- in terms of real multiplications per symbol so that these results can be applied to a large number of communication systems. We demonstrated that in the specific considered experimental system the convolutional layer coupled with the biLSTM (CNN+biLSTM) provides the highest Q-factor improvement compared to the reference linear chromatic dispersion compensation (2.9 dB improvement). We examine the trade-off between the computational complexity and performance of all equalizers and demonstrate that the CNN+biLSTM is the best option when the computational complexity is not constrained, while when we restrict the complexity to lower levels, the three-layer perceptron provides the best performance. Our complexity analysis for different NNs is generic and can be applied in a wide range of physical and engineering systems.

RIN induced penalties in G.654.E and G.652.D based distributed Raman amplifiers for coherent transmission systems.

Relative intensity noise (RIN) induced penalties were experimentally measured in distributed Raman amplifiers (DRAs) for G.654.E and G.652.D fibres with forward, backward and bidirectional pumping configurations. The measured signal RIN using the G.654.E fibre was ∼3.5 dB and ∼2 dB lower than the G.652.D fibre with forward (FW) pump configuration for PM-QPSK and PM-8QAM signals, with single span transmission showing a Q-factor improvement of ∼3 dB and ∼2.5 dB for G.654.E over G.652.D fibres. The performance penalty in a long haul coherent system was evaluated for 28 GBaud PM-QPSK signals using a recirculation loop for backward and bidirectional distributed Raman amplifiers. Our experimental results demonstrate an additional transmission distance of more than 1000 km for G.654.E over its counterpart G.652.D assuming a HD-FEC limit of 8.5 dB.

Fiber nonlinearity mitigation with a perturbation based Siamese neural network receiver

We simulate nonlinear distortion compensation of single frequency dual polarization signals propagating in single mode optical fibers with a combination of Siamese Neural Networks (SNNs) and a perturbative nonlinearity compensation technique. We find that a 2-branched SNN can enhance the quality factor (Q-factor) of a 3200 km 16-QAM optical system from the 8 dB that is associated with only applying chromatic dispersion compensation (CDC) to 8.9 dB. We then investigate the Q-factor improvement associated with different numbers and widths of SNN branches. We finally demonstrate that the number of inputs to the SNN and hence the computational complexity can be reduced by employing Principal Component Analysis (PCA). This results in a 0.75 dB Q-factor enhancement with 50% fewer inputs than previous designs.

Interchannel nonlinearity compensation using a perturbative machine learning technique

We propose an extension of the perturbation-based approach for fiber nonlinearity compensation that is capable of mitigating both intra- and interchannel nonlinearity with a moderate increase in implementation complexity. Being guided by inverse perturbation theory we develop a straight-forward modification of the conventional model that takes into account nonlinear interactions between symbols from neighboring spectral channels. We employ machine learning techniques such as the normal equation model with regularization for joint identification of perturbation coefficients that are responsible for intra- and interchannel interactions. We investigate the application of the proposed approach for compensating nonlinear signal distortions in a 1200 km fiber-optic 3 x 400 Gbit/s WDM DP-64QAM transmission link. It was shown up to 0.83 dB and 0.51 dB Q2-factor improvement compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We estimate the implementation complexity of the approach.

Compensation of SOA-induced nonlinear phase distortions by optical phase conjugation

To answer the question: "Is optical phase conjugation (OPC) capable of compensating nonlinear distortions caused by not only Kerr effect of optical fibre, but also the carrier dynamics of semiconductor optical amplifiers (SOAs)?", we investigate the effectiveness of OPC-based nonlinear compensation for SOAs amplifying a few-channel WDM signal modulated with m-QAM. We use a pair of SOAs with an OPC stage sandwiched between the two so that the combination works as a low-distortion amplifier. Symbol-period longer than the gain recovery time is chosen in our experiments to avoid bit-pattern effects introduced by the SOA. We amplify a 12Gbaud, 16QAM modulated three-channel WDM signal with this technique in the back-to-back configuration which remarkably outperforms a single SOA in the nonlinear regime of operation with an average Q2 improvement better than 4 dB for an output power of 4 dBm. We further demonstrate the practical advantage of the low distortion higher output power capability of the SOA shown in the back-to-back result by carrying out a transmission of the amplified signal through a 160-km fibre, where relatively high launch power is desirable. We also study the case of 64QAM signals and show that approximately a 3 dB Q2 factor improvement can be obtained over single SOA, while without nonlinear phase distortion compensation, the demodulation is nearly impracticable.

Experimental demonstration of pseudo-pilots-aided Gaussian basis expansion-based phase noise suppression scheme for CO-OFDM

In this paper, we propose a novel phase noise suppression scheme called pseudo-pilots-aided Gaussian basis expansion (PS-GBE) for coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. Integrating with the pseudo-pilots, the Gaussian basis can suppress and fit phase noise effectively with high spectral efficiency (SE). We experimentally demonstrate the proposed scheme in 200-Gb/s optical QPSK-OFDM superchannel and 400-Gb/s optical 16QAM-OFDM superchannel. According to the experimental results, the proposed PS-GBE has a phase noise suppression performance similar to the GBE scheme. However, different from the GBE scheme, fewer pilots are needed for phase noise estimation in PS-GBE. Hence, the proposed PS-GBE can achieve higher SE. Compared to the pseudo-pilots-aided orthogonal basis expansion-based (PS-OBE) phase noise suppression and traditional common phase noise compensation (CPEC) methods, the proposed PS-GBE can get a significant performance enhancement. In the QPSK-OFDM system, a Q-factor improvement of 1.1 dB is obtained compared to the PS-OBE method. In the 16QAM-OFDM system, the BER is enhanced from 1.00E-03 to 2.05E-04. Further simulation results indicate that our proposed PS-GBE scheme can effectively increase laser linewidth tolerance. The proposed scheme can be applied to CO-OFDM and extended to coherent optical filterbank multicarrier (CO-FBMC) and coherent optical single carrier frequency domain multiplexing (CO-SCFDM) transmission system.