用激光混沌生成随机数，实现水下中继稳定分配与高吞吐。
Stable Acoustic Relay Assignment with High Throughput via Lase Chaos-based Reinforcement Learning
- 基于激光混沌生成随机数，多进程学习优化中继分配。
- 在动态环境中实现更高吞吐率和更强适应性，提升稳定性。
- 适合复杂水下场景的中继选择，尤其适用于环境变化频繁的系统。
本研究针对水下声学网络中的稳定中继分配问题，提出两种不同目标：经典稳定配置与模糊稳定配置。为实现这些目标，引入基于激光混沌的多进程学习（LC-ML）方法，高效获得高吞吐并快速达到稳定状态。该方法利用激光混沌生成的随机数，在中继到多个源节点的分配过程中进行充分探索。研究发现，激光混沌生成的随机数及交换过程中的多进程机制，显著提升了吞吐量，并增强了对随时间变化环境的适应能力。同时，模糊认知下的稳定配置相比精确配置表现出更低的波动性，验证了该方法在复杂水下环境中的实用性与有效性，可作为中继选择的重要基础。
原文摘要 · Abstract (English)
This study addresses the problem of stable acoustic relay assignment in an underwater acoustic network. Unlike the objectives of most existing literature, two distinct objectives, namely classical stable arrangement and ambiguous stable arrangement, are considered. To achieve these stable arrangements, a laser chaos-based multi-processing learning (LC-ML) method is introduced to efficiently obtain high throughput and rapidly attain stability. In order to sufficiently explore the relay's decision-making, this method uses random numbers generated by laser chaos to learn the assignment of relays to multiple source nodes. This study finds that the laser chaos-based random number and multi-processing in the exchange process have a positive effect on higher throughput and strong adaptability with environmental changing over time. Meanwhile, ambiguous cognitions result in the stable configuration with less volatility compared to accurate ones. This provides a practical and useful method and can be the basis for relay selection in complex underwater environments.
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