Atmospheric turbulence and channel loss represent the primary limiting factors in ground-to-satellite quantum communication systems, yet their joint impact remains insufficiently characterized across diverse operational conditions. In this work, we develop a comprehensive and open atmospheric channel model for quantum optical links along ground-to-satellite paths, integrating altitude-dependent molecular and aerosol absorptions. The model is validated with available data from missions such as Micius and Hispasat, including a secondary viability assessment for a hypothetical Starlink-based quantum communication network, where we evaluate the predicted channel behavior in terms of single-photon transmittance and power balance, alongside the channel capacity of the link for uplink and downlink exclusively. Our comparative analysis demonstrates the ability of our channel model to predict real data under simple atmospheric behavior assumptions, which offer practical guidelines for the design and optimization of future quantum satellite missions.
This paper analyzes the block error rate (BLER) and optimizes key parameters of a non-orthogonal multiple access (NOMA)-aided short-packet visible light communication (VLC) system. Specifically, we consider a short-packet VLC system having a transmitter and K randomly deployed users. By the NOMA principle and the Gaussian-Chebyshev quadrature method, we derive exact and asymptotic expressions of system average BLER under imperfect channel state information and imperfect successive interference cancellation. To further improve system performance, we formulate a joint blocklength and power optimization problem to minimize the transmission latency. Due to the intractability of the original problem, we divide it into a blocklength optimization subproblem and a power allocation subproblem. We solve them by a bisection search algorithm and a one-dimensional search algorithm. Numerical results verify the accuracy of the derived expressions and the superiority of the proposed algorithm.
With the growing demand for high-speed and high-capacity communication driven by sixth-generation communication technology (6G), optical wireless communication (OWC) has gained wide attention due to its advantages such as high bandwidth, low latency, and strong resistance to electromagnetic interference. However, in complex atmospheric environments, optical signal transmission is easily affected by factors such as turbulence, attenuation, and pointing errors, leading to reduced communication quality or even interruptions. Adaptive transmission can dynamically adjust transmission parameters to enhance link robustness and spectral efficiency. This article focuses on free space optical communication (FSO), with a particular emphasis on multiple-input multiple-output (MIMO) systems. Starting from the key technologies and research progress of adaptive transmission, it analyzes the performance optimization strategies of adaptive transmission under different channel conditions. Finally, it discusses the main challenges faced by wireless optical adaptive transmission in the future and looks ahead to the development direction of this technology, aiming for more precise compensation and prediction of complex atmospheric channels and promoting the widespread application of optical communication in integrated space-air-ground communication networks.
Efficient online scheduling is crucial for balancing resource utilization, request acceptance, and decision overhead in elastic optical networks (EONs). Existing online scheduling methods for routing and spectrum allocation (RSA) often rely on fixed rules or threshold-based strategies to make fast decisions, but they do not explicitly model recent network load evolution. As a result, their performance may be limited under medium to high network loads. To address this issue, this paper proposes a closed-loop scheduling framework, termed GRU-MLP-EDAF Scheduling (GMES), for dynamic RSA in EONs. Specifically, GMES uses a gated recurrent unit (GRU) to encode historical network-state sequences and combines the learned temporal representation with candidate-path structural and resource-state features through a multilayer perceptron (MLP) for path ranking. In addition, an Exponential Dynamic Adaptive Feedback (EDAF) mechanism is introduced to smooth runtime metrics and update a small set of online control variables for score correction and candidate-path trial control. Experimental results under the considered logical-layer RSA setting show that GMES achieves lower blocking rates and higher average link utilization than the two main reference methods in the load-scan comparison, while keeping the online decision overhead at the millisecond level. Controlled matched-scope comparisons further indicate that the blocking reduction is not only caused by examining more candidate paths. The ablation, sensitivity, and multi-topology results also show that the benefit of GMES is most evident under non-negligible congestion, while its hotspot-control and feedback gains should be interpreted as load-and topology-dependent.
