AI 中文总结
研究水下光无线通信性能下降问题，提出基于智能混沌的CDMA方案，利用逻辑映射生成混沌序列，通过多智能体强化学习框架动态调整参数，并经实验验证该方案性能优越，适用于分布式水下网络。
AI 中文摘要
本文提出了一种用于水下光无线通信（UOWC）的基于智能混沌的新型码分多址（CDMA）方案，以解决水下环境中严重散射和多径色散导致的关键性能下降问题。与传统调制技术不同，该方案利用逻辑映射生成的不可预测确定性混沌序列，增强对散射引起的损伤的鲁棒性。多智能体强化学习（MARL）框架使分布式智能体能够根据实时环境反馈动态调整混沌映射参数。通过两米水箱试验台的实验验证表明，该方案性能优于传统方案，具有快速收敛和宽松同步要求，适用于分布式水下网络。
英文摘要
This paper presents a novel intelligent chaotic-based code-division multiple access (CDMA) scheme for underwater optical wireless communication (UOWC), addressing critical performance degradation caused by severe scattering and multipath dispersion in underwater environments. Unlike conventional modulation techniques such as on-off keying, which depend on precise pulse timing and show high sensitivity to channel distortions, the proposed approach leverages unpredictable deterministic chaotic sequences generated by the logistic map to enhance robustness against scattering-induced impairments. A Multi-Agent Reinforcement Learning (MARL) framework enables distributed agents to dynamically adapt chaotic map parameters, including initial conditions and bifurcation parameters, based on real-time environmental feedback, optimizing sequence generation to maintain low cross-correlation properties and improve resilience to multipath effects. Experimental validation using a 2-meter water tank testbed with controlled turbidity demonstrates superior performance compared to conventional schemes. The adaptive framework exhibits rapid convergence and relaxed synchronization requirements, making it highly suitable for distributed underwater networks where centralized coordination is impractical.
DOI：10.23919/OCEANS59106.2025.11244960
