physical link performance modeling

Optical space–ground communication networks face high deployment costs, slow responsiveness, and limited flexibility. Method: This study systematically compares portable versus large optical ground stations (OGSs) in low Earth orbit (LEO) small-satellite constellations, integrating orbital dynamics, multi-scenario atmospheric channel fading models, link budget analysis, and network availability simulations. Contribution/Results: It provides the first quantitative assessment of low-cost portable terminals as viable replacements for traditional high-capacity OGSs. Results show that a portable OGS network maintains reliable optical links over >95% of operational time, reduces deployment cost by >60%, and cuts end-to-end latency by an order of magnitude. The paper proposes a novel “distributed lightweight OGS + dynamic scheduling” network architecture, which significantly enhances global coverage elasticity and rapid deployment capability while preserving robustness.

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.

In free-space optical satellite networks, existing scheduling approaches often neglect or oversimplify pointing, acquisition, and tracking (PAT) latency, leading to inefficient contact planning and overestimation of network capacity. To address this, we propose the first multimodal PAT latency model grounded in real-world measurements from multiple sources—including NASA’s TBIRD, LLCD, and DSOC missions, as well as ESA’s lunar terminal—capturing nonlinear latency characteristics during coarse-to-fine pointing transitions and fine-tracking establishment. The model explicitly quantifies the strong dependence of PAT latency on initial pointing error and optical beamwidth. By integrating this empirically validated model into inter-satellite link scheduling and routing algorithms, we significantly improve scheduling accuracy and enable rigorous, verifiable capacity assessment for large-scale optical satellite constellations. This work establishes a foundational, measurement-based framework for capacity evaluation and algorithm design in next-generation optical space networks.

Accurately predicting channel power, optical signal-to-noise ratio (OSNR), and generalized signal-to-noise ratio (GSNR) in operational optical networks remains challenging. This work proposes a hybrid modeling paradigm anchored by a digital link model (DLM), which synergistically integrates physical principles with data-driven techniques to achieve high-accuracy prediction of these key performance metrics without requiring full-network model reconstruction. By leveraging the DLM to calibrate inter-span and inline amplifier (ILA) boundaries, the proposed approach achieves OSNR and GSNR prediction errors within 0.39 dB and 0.43 dB, respectively, in both single-channel and OSaaS deployment scenarios—significantly outperforming existing methods.

To address the challenge of real-time synchronization between digital twins (DTs) and physical optical networks—limiting dynamic service adaptability throughout the network lifecycle—this paper proposes a dynamically updated DT framework for fiber channel performance prediction. Methodologically, it introduces the first DT dynamic update mechanism for optical networks, integrating physics-informed neural networks (PINNs), partial differential equation (PDE)-constrained hybrid modeling, real-time parameter identification, and a closed-loop feedback architecture. This enables synchronous, adaptive updates of multi-physical parameters—including Raman gain, amplifier frequency response, and connection loss—across C- and L-bands. Experimental results demonstrate a 100× speedup in prediction over conventional numerical methods; a 1.4 dB reduction in performance estimation error following device replacement; and validation of high accuracy (sub-dB), low latency (millisecond-level), and physical consistency in both large-scale simulations and live C+L-band field trials.

This study addresses the challenges of weather-induced disruptions in free-space optical (FSO) links and stringent on-board cache and energy constraints in low Earth orbit (LEO) hybrid radio frequency (RF)/FSO satellite networks. To tackle these issues, the authors propose an interference-aware transmission scheduling mechanism that integrates a weather-dependent FSO outage model with finite-buffer queueing analysis to jointly optimize end-to-end throughput and cache capacity allocation. Innovatively, instead of increasing transmit power, the scheme dynamically adjusts scheduling priorities to maintain multi-hundred-Gbps data rates while substantially reducing buffer requirements and packet loss. Simulation results demonstrate that the proposed approach effectively enhances system throughput under practical operational constraints, enabling efficient and reliable high-speed data transmission.

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 study addresses critical gaps in laser diode–based LiFi prototype research, including throughput mismeasurement, absent noise modeling, and unclear coverage–performance trade-offs, by presenting the first reproducible, closed-loop validation framework spanning hardware to full protocol stack simulation. The system integrates a 500-mW laser source, a holographic diffuser, an IM/DD receiver, and adaptive M-QAM modulation, coupled with the ns-3 network simulator and a Monte Carlo link-level error model to enable joint PHY/MAC/network-layer evaluation. Experimental results demonstrate 930 Mb/s at 14 m (16-QAM), 1.86 Gb/s at 5 m (256-QAM), and a maximum OOK range of 23.3 m; a 20° diffuser covers a 4.2-m radius area. ns-3 simulations confirm a saturated throughput reaching 93% of the physical-layer rate and a 99th-percentile latency below 0.11 ms under 70% load, quantifying for the first time the trade-off between beam width and coverage.

This work proposes an adaptive fault detection method based on online learning to address the performance degradation of static models in optical network fault detection caused by concept drift. By introducing an online learning mechanism into optical network fault diagnosis for the first time, the approach integrates concept drift detection with dynamic model updating to enable real-time adaptation to environmental changes. Experimental results demonstrate that, compared to conventional static models, the proposed method achieves up to a 70% improvement in fault detection performance while maintaining low latency, significantly enhancing both adaptability and robustness.

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.