optical performance modeling

Optical fiber nonlinearity fundamentally limits the capacity scaling of coherent optical communication systems. To address this, we propose a low-complexity joint framework integrating digital back-propagation (DBP) and probabilistic constellation shaping (PCS), bridging nonlinear channel capacity analysis with real-time, hardware-feasible digital signal processing (DSP). By co-optimizing nonlinear compensation and input distribution design, the framework approaches the theoretical nonlinear Shannon limit while substantially reducing computational complexity. We further develop an information-theoretic nonlinear channel modeling methodology and devise a hardware-friendly, low-overhead DSP algorithm. This work unifies fundamental capacity theory with practical implementation constraints, establishing a new paradigm for nonlinearity mitigation that simultaneously delivers high performance and engineering viability in ultra-high-speed, long-haul optical transmission systems.

This work addresses the severe performance degradation in satellite-to-ground coherent optical communication caused by nonlinear distortions introduced by high-power optical amplifiers. To mitigate this issue, the paper proposes a low-complexity digital signal processing scheme that incorporates an efficient nonlinear compensation algorithm. The approach significantly enhances the link’s tolerance to channel loss with negligible increase in system complexity. Experimental results demonstrate that the proposed method improves the allowable link loss by 6 dB, effectively alleviating nonlinear impairments and offering a practical solution for high-power satellite-to-ground optical communication systems.

In mode-division multiplexing (MDM) systems based on multimode fiber, mode-dependent gain (MDG) induces stochastic fluctuations in channel capacity, substantially reducing the average capacity and hindering universal analytical characterization. To address this, we propose a statistical channel model and derive, for the first time, a closed-form expression for the probability distribution of channel capacity for arbitrary mode count $D > 2$, overcoming the prior limitation to $D = 2$. Our approach leverages Gaussian approximation and introduces a fitting parameter to capture inter-mode capacity correlations, ensuring both accuracy and broad applicability. Validated against multi-section Monte Carlo simulations across wide-ranging practical system parameters, the proposed analytical model achieves high fidelity with controllable error. This significantly enhances computational efficiency and theoretical interpretability in capacity assessment. The framework provides a foundational theoretical tool for design optimization and performance prediction of high-dimensional MDM systems.

To address signal degradation caused by linear (e.g., chromatic dispersion) and nonlinear (e.g., self-phase modulation—SPM—and cross-phase modulation—XPM) distortions in multi-span optical links, this paper proposes and experimentally demonstrates a silicon-photonic integrated feedforward photonic neural network (PNN) equalizer. The PNN combines an adjustable 8-tap optical FIR filter with square-magnitude nonlinear operations to achieve low-latency, low-power all-optical compensation. Employing an intensity-modulation/direct-detection (IM/DD) architecture, the system integrates a tunable dispersion compensator and edge-coupled photodetectors. Experimentally, it achieves efficient equalization of 10-Gbps signals under cumulative dispersion of 200 km and SPM-induced distortion equivalent to 450 km of standard single-mode fiber. Numerical simulations further indicate scalability to 100-Gbps operation and capability to mitigate XPM impairments. This work establishes a compact, energy-efficient on-chip nonlinear equalization paradigm for high-speed short-reach optical interconnects.

This work addresses the performance optimization of zero-additional-loss multiplexing (ZALM) photon sources for quantum networks. Methodologically, we propose a modular and configurable simulation framework built upon NetSquid and the QSI controller, integrating physical models of SPDC sources, DWDM filtering, HOM interference, polarization gating, detectors, and lossy channels; it supports switching between ideal and realistic operational modes and decouples source characteristics, filter parameters, and feed-forward control to enable joint optimization. Key contributions include: (i) the first systematic characterization of the trade-off between SPDC degeneracy bandwidth and DWDM channel spacing on entanglement rate and fidelity; (ii) experimental validation that narrowing the SPDC bandwidth significantly boosts entanglement generation rate while preserving fidelity ≈ 0.8; and (iii) quantitative end-to-end analysis of e-bit rate and fidelity over a 50-km fiber link, providing both theoretical guidance and a simulation toolkit for parameter customization of ZALM sources in practical quantum network deployments.

This work addresses the challenge of modeling and compensating nonlinear distortions in intensity-modulation direct-detection (IM-DD) optical links, which significantly limits system performance. By introducing the Best Linear Approximation (BLA) framework, the study reveals—for the first time—that the orthogonal component of the nonlinear distortion exhibits non-Gaussian statistical characteristics. Leveraging this insight, the authors develop an optimization strategy for modulation depth tailored to various equalizer architectures based on BLA analysis. This approach effectively characterizes the nonlinear impairments and enables near-optimal modulation depth selection across different equalizers, thereby substantially enhancing overall system performance.

This study addresses the severe degradation of reliability in terrestrial free-space optical communication caused by atmospheric turbulence, which frequently leads to link outages. Conducted over a 4.6-km urban experimental link, the work systematically investigates the effectiveness of data interleaving in mitigating turbulence-induced impairments. It establishes, for the first time, a quantitative relationship among interleaving depth, turbulence strength, and achievable data rate, enabling principled optimization of interleaving parameters. Experimental results demonstrate that the proposed approach reduces link outage probability by two orders of magnitude while maintaining high data throughput, thereby substantially enhancing communication robustness. These findings provide critical theoretical insights and practical engineering guidance for the design of real-world free-space optical communication systems operating under turbulent atmospheric conditions.

This work addresses the performance degradation of high-order modulation signals caused by phase noise in dispersion-managed fiber channels. To mitigate this impairment, the authors propose performing phase noise compensation prior to chromatic dispersion compensation and develop efficient feedforward and iterative algorithms within the Expectation Propagation framework. Evaluated in a 100 GBaud 64-QAM long-haul transmission system spanning 10,000 km, the proposed approach significantly suppresses phase noise effects, achieving information rates approaching those of an ideal channel without phase noise. This strategy overcomes the performance limitations inherent in conventional post-compensation schemes, thereby establishing a new benchmark for phase noise resilience in high-capacity coherent optical systems.

This work addresses the limited generalization and low modeling efficiency of existing physical-layer approaches in ultra-wideband optical networks, which struggle to accurately estimate the generalized signal-to-noise ratio (GSNR) under stimulated Raman scattering. The authors propose a Link-Adaptive Digital Twin (LA-DT) framework that decomposes GSNR modeling into amplified spontaneous emission (ASE), nonlinear interference (NLI), and signal power components. Innovatively integrating a neural network architecture with a linear modulation layer and a domain discriminator, the method leverages domain-adversarial training and few-shot fine-tuning to explicitly model Raman amplifier insertion loss within the digital twin for the first time. Experiments demonstrate substantial improvements: across 35 scenarios, prediction errors are significantly reduced (RMSE of 0.151, 0.111, and 0.113 dBm for NLI, ASE, and signal power, respectively), outperforming baselines by over 52%. Moreover, on 12 unseen scenarios, it achieves a GSNR RMSE of 0.159 dB with only 20 samples, highlighting exceptional generalization and rapid adaptability.

本文针对光星间链路因指向抖动导致的可靠性问题，通过建立统计信道模型并采用高斯主瓣近似方法，分析了终端稳定性对链路性能的影响，并提出了设计指导。