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Improved Grey Wolf-Differential Evolution Algorithm for UAV OAM-MDI-QKD Parameter Optimization
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AI
DOI：10.1002/qute.202400423.png)
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Abstract
En 中文
The integration of unconditional security of quantum key distribution (QKD) with the flexibility of unmanned aerial vehicles (UAVs) presents significant potential for the deployment of comprehensive quantum networks. However, imperfect components of practical QKD systems offer avenues for eavesdropping, while the limited payload capacity and vibrations of UAVs present substantial challenges. This paper introduces an orbital angular momentum (OAM) encoding strategy combined with decoy-state and the measurement-device-independent QKD (MDI-QKD) protocol to address security issues and reference frame misalignment in UAV-based QKD systems. Nevertheless, the communication performance of the OAM-MDI-QKD system is significantly affected by complex environmental challenges in the airborne channel. To improve the degraded communication performance in airborne platforms, an enhanced grey wolf optimization-differential evolution (GWO-DE) algorithm is developed, utilizing chaotic mapping and a nonlinear decay factor to strengthen global search and convergence capabilities, which effectively addresses the search limitations of local search algorithms (LSA) when dealing with high-dimensional objective functions. Simulation results demonstrate that the GWO-DE algorithm outperforms other traditional optimization algorithms in terms of optimization precision and transmission distance enhancement, with computational speed meeting system requirements.
Keywords:
differential evolution
grey wolf optimization
OAM-MDI-QKD
parameter optimization
unmanned aerial vehicle
Journal
A
IF:
4.3
Papers:
442
Citations:
3.2K