Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan, 250100, China.
Sci Rep. 2023 Jan 24;13(1):1353. doi: 10.1038/s41598-023-28282-z.
As laser chaos has been proven to be a robust tool to solve the multi-armed bandit (MAB) problem, this study investigates the problem of multiuser dynamic channel assignment using laser chaos in cognitive radio networks with K-orthogonal channels and M secondary users. A novel dynamic channel assignment algorithm with laser chaos series for multiple users, named parallel processing learning with laser chaos (PPL-LC) algorithm, is proposed to efficiently address two main objectives: stable channel assignment and fuzzy stable channel assignment. The latter objective accounts for the realistic scenario where users have fuzzy preferences and do not necessarily pursue the best preference. The PPL-LC algorithm uses the randomness properties of laser chaos to learn the assignment of channels to multiple users without any limitations on the number of channels, which has not been considered in existing laser chaos algorithms. Moreover, the PPL-LC is equipped with parallel processing channel selections, resulting in higher throughput and stronger adaptability with environmental changes over time than comparison algorithms, such as distributed stable strategy learning and coordinated stable marriage MAB algorithms. Finally, numerical examples are presented to demonstrate the performance of the PPL-LC algorithm.
由于激光混沌已被证明是解决多臂老虎机（MAB）问题的强大工具，因此本研究调查了在具有 K 个正交信道和 M 个次用户的认知无线电网络中使用激光混沌解决多用户动态信道分配的问题。针对多用户，提出了一种具有激光混沌序列的新颖动态信道分配算法，称为并行处理学习的激光混沌算法（PPL-LC 算法），以有效地解决两个主要目标：稳定的信道分配和模糊稳定的信道分配。后者的目标考虑了用户具有模糊偏好且不一定追求最佳偏好的实际情况。PPL-LC 算法利用激光混沌的随机性特性来学习为多个用户分配信道，而不受信道数量的限制，这在现有的激光混沌算法中没有考虑。此外，PPL-LC 配备了并行处理信道选择，与分布式稳定策略学习和协调稳定婚姻 MAB 算法等比较算法相比，它具有更高的吞吐量和更强的适应能力，可以随着时间的推移适应环境变化。最后，给出了数值示例来证明 PPL-LC 算法的性能。
