This paper presents a novel optical performance monitoring (OPM) scheme based on reservoir computing (RC) and Resnet network for monitoring the optical signal-to-noise ratio (OSNR) of few-mode transmission channels, without the need for any demodulation process. By using 300 RC nodes, the number of floating-point operations (FLOPs) is reduced by 75%, with almost no change in prediction accuracy compared to a network without RC. In a system ranging from 0 to 30 dB, 40 simulation runs yield an OSNR prediction accuracy band around 0.9900. We also investigate the prediction accuracy when using different spectral information, with results showing that frequency-domain information effectively captures the characteristics of both signal and noise. Additionally, we examine the impact of different mode combinations on OSNR prediction accuracy and find that the prediction performance is nearly unaffected by the mode combination.

Using the linear polarization (LP) mode decomposition method, we theoretically analyze the beat effect between the same polarization components of the vector pump modes and propose a pump beat model of few-mode erbium-doped fiber amplifiers (FM-EDFAs). The relationship of the overlap factor with the signal gain in the beat model is verified by the amplification of LP01 and LP11 modes for the first time, that is, the differential modal gain (DMG) is dependent on the integral of the overlap factor difference over the EDF length. We also simulate the effect of pump mode beating on signal amplification: the modal gain varies periodically with the pump mode phase, and the beat length can affect the fluctuation period and amplitude of the DMG. The DMG can further be reduced by controlling the initial phases of the vector modes and properly designing the beat length of the FM-EDF. The similar process can expand to more signal modes, such as the simultaneous amplification of LP01x, LP11ex, LP11oy, LP21ex and LP21oy modes. The LP mode decomposition method is also applied to the beat effect between signal modes or the amplification of orbital angular momentum (OAM) modes. One can expect to implement an experiment on the FM-EDFA amplification with pump mode beating by optimally designing the FM-EDF structure, using a properly narrow linewidth pump laser and precisely controlling the pump mode coherence. © 2024

An eccentric-circle-assisted ring-core fiber (ECRF) structure is proposed to effectively separate spatial-degenerated linear polarization (LP) modes while maintaining birefringence at the 10-6 level, which provides more degrees of freedom in the design of few-mode fiber employment in space division multiplexing (SDM) systems. Our simulation analysis demonstrates that the effective refractive index difference (Aneff) between the spatially degenerate modes in this fiber falls within the range of (2.44-6.42) x 10-4 at 1530-1565 nm, given the refractive index difference level typical of conventional fiber core and cladding. In addition, the polarization separation level for each mode is on the order of 10-6 and below. The design requirements of significant separation of spatial degeneracy and basically no separation of polarization degeneracy are achieved. Based on the characteristics of spatially degenerate mode in this fiber, we extend the regulation law for spatially degenerate mode separation of the LPmn mode groups in center-assisted ring-core fibers. In comparison to the existing center-assisted ring-core fiber structure, the ECRF can further reduce the fabrication difficulty and increase the feasibility of preparation. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

Deep learning has become a valuable tool for analyzing and compensating phenomena in optical communication systems. However, to achieve zero-shot with no experimental samples training, it is essential to generate simulation datasets that accurately reflect the behavior of real systems under various conditions. Therefore, in this paper, we propose a novel approach for constructing simulation training datasets based on a core-offset model based on vector modes in few-mode transmission systems. This model accurately characterizes the coupling between vector modes under core offset conditions, generating optical spot distributions that closely resemble those observed in experimental environments. Through the simulation of optical spots and comparison with experimental data, we achieve an average correlation coefficient exceeding 0.80. Our research demonstrates that the core-offset model servers as a reliable tool for simulating complex optical phenomena, laying the foundation for achieving zero-shot with no experimental samples training based on deep learning models in few-mode transmission systems. © 2025 SPIE.

For the optical vector-eigenmode (VE) decomposition, we introduce a novel deep learning-based optical-mode decomposition (OMD) technique for accurate retrieval of near-field beams generated from few-mode fiber (FMF) channel, in which this residual network (Resnet) scheme could obtain complete and accurate amplitude and phase coefficients from the near-field beams. For the case of 3 intra-module modes in the FMF, the correlation coefficient of the OMD reaches 0.9808, while the modal weight error and modal phase error are as low as 0.0287 and 0.0331, respectively. And for the case of 10 inter-module modes in the FMF, the correlation coefficient of the OMD achieves 0.9878, while the mode weight error and mode phase error are recorded down to 0.0071 and 0.0205, respectively. Moreover, the noise-added near-field beams show a good robustness of the OMD based on deep learning. Finally, we also investigate the relationship between modal differences and correlation coefficients, revealing the nature of VE-based OMD functions. © 2024 Elsevier B.V.

We propose a transfer-learning multi-input multi-output (TL-MIMO) scheme to significantly reduce the required training complexity for converging the equalizers in mode-division multiplexing (MDM) systems. Based on a built three-mode (LP01, LP11a, and LP11b) multiplexed experimental system, we thoughtfully investigate the TL-MIMO performances on the three-typed data, collecting from different sampling times, launching optical powers, and inputting optical signal-to-noise ratios (OSNRs). A dramatic reduction of approximately 40%-83.33% in the required training complexity is achieved in all three scenarios. Furthermore, the good stability of TL-MIMO in both the launched powers and OSNR test bands has also been proved.

We propose a method for reducing the polarization-dependent loss (PDL) of mode division multiplexing (MDM) system with the help of a few-mode polarization controller (FMPC), in order to transport high-speed dual-polarization (DP) signals. The detailed PDL measurement is implemented by the commercial PSGA-101A polarization analyzer along with the external light source with the dual-polarization quadrature phase shift keying (DP-QPSK) format, and the feasibility is verified by the back-to-back (B2B) MDM and MDM amplification systems. In the amplification system, the home-made few-mode erbium-doped fiber amplifier (FM-EDFA) is used for simultaneously amplifying four modes of LP01, LP11, LP21 and LP02, with the gain of more than 20 dB and the minimum differential modal gain (DMG) of 1.55 dB. By adjusting the FMPC to equalize the PDL, the entire MDM amplification system can provide optical power margin about 24 dB for DP-QPSK signals. In other words, the FMPC state can also be characterized by the PDL values. © 2024 Elsevier Ltd

All-fiber few-mode erbium-doped fiber amplifiers (FM-EDFAs) with isolation and wavelength division multiplexers (IWDMs) have been developed to enable flexible pumping in different directions. The FM-EDFA can achieve >30 dB modal gain with © 2024 Chinese Optics Letters.

The enhanced remote area communication (eRAC) scenario is an important growth point in the communication market. In some remote areas where optical fiber access cannot be realized or the laying cost is too high, fixed wireless access (FWA) is an appropriate supplementary solution for eRAC. Adopting analog radio over fiber (A-RoF) technology to implement FWA can overcome the bandwidth limitation of electronic devices and realize high-frequency carrier communication economically to achieve high-capacity wireless communication. Also, probabilistic shaping (PS) technology can be combined with A-RoF to further improve the flexibility of the network and coverage of service provision. However, in the PS-A-RoF network, the high RF power introduces more undesired nonlinear effects into the network, and it is often necessary to deploy supervised machine learning (ML) compensation modules in wireless receivers (WRs). But the module performances are affected by the uneven probability distribution of PS-QAM constellation points. In this paper, we employ the PS-A-RoF nonlinear model to theoretically investigate the correlation between the distribution of training symbols and the wireless A-RoF system’s performance. Our analysis reveals that reducing the variance of training symbol power contributes to a lower BER in the A-RoF network. We introduce a borderline random over-sampling (B-ROS) that matches with the PS-A-RoF nonlinear model, instead of the mainstream ROS, which is only at the data level. Based on the B-ROS scheme, only the minority examples below the borderline are over-sampled to reach a better variance performance. Introducing the B-ROS method into the supervised complex value nonlinear compensation module can further improve the decision accuracy of WRs with the restoration of phase information, without increasing additional computational resource consumption. The vector noise power, training symbol power variance, and noise factor metrics have been calculated to optimize the borderline value of our ML-based approach. We also present experimental data on the proof-of-concept A-RoF experiment for PS-64QAM. The results demonstrate a promising nonlinear compensation performance of the B-ROS WR, and the optimal borderline agrees well with the one deduced from the theoretical model under certain transmission conditions. Our proposed B-ROS scheme lessens the training size demand and can improve the receiver sensitivity by 0.51 dB compared to the common ML-based WR and by 0.7 dB compared to the conventional ROS scheme. © 2024 Optica Publishing Group.

We propose a time-frequency dual-dimension deep neural network (TF-DD-DNN)-based optical performance monitoring (OPM) scheme to jointly monitor the optical signal-to-noise ratio (OSNR) and the mode-coupling induced crosstalk strength (XT) in mode-division multiplexing (MDM) systems, by revealing the diversity of the amplified spontaneous emission (ASE) and XT distortions on optical signals in the time- and frequency-domains. We numerically quantify the predictions of OSNR and XT for a coherent MDM transmission link, achieving the OSNR prediction accuracy of more than 0.99 and the XT prediction accuracy of up to 1 with the root mean square error (RMSE) of 0.11 dB at OSNR = 30 dB @0.1 nm for the case of quadrature-phase-shift keying (QPSK) signals. We evaluate the impacts of the chromatic dispersion (CD) of fibers and the low-pass filter bandwidth on the prediction procedures. The dependency of the modulation formats is also discussed, confirming the robust operation of the proposed scheme. Finally, the TF-DD-DNN scheme is experimental verified. High consistency is obtained between the simulation and experimental results. © 2024 Elsevier B.V.