基于元胞量子蜂群算法和信道案例库的认知无线电混合跨层决策引擎研究

针对认知无线电系统参数重配置问题，提出了一种基于元胞量子蜂群算法和信道案例库的混合跨层认知决策引擎。该认知决策引擎充分考虑无线通信网络各层参数，以网络整体性能最优为优化目标；提出的元胞量子蜂群算法，利用双策略对种群进行混沌初始化，设计了基于元胞自动机原理和社会认知策略的快速量子旋转角调整策略用于实现引领蜂和跟随蜂的邻域搜索；构建基于信道增益的认知无线电参数案例库，用于实现快速决策。仿真结果表明，该认知决策引擎能够根据无线通信环境和用户需求的变化，动态的进行参数的重配置，同时其在收敛速度、收敛精度和算法稳定性上都明显优于基于二进制人工蜂群算法和量子遗传算法的认知决策引擎。

Hybrid Cross-layer Decision Engine for Cognitive Radio based on the CQABC Algorithm and Channel Case library

In order to treat the problem of parameter reconfiguration of the cognitive radio system, an improved hybrid cross-layer decision engine based on the Cellular Quantum Artificial Bee Colony Algorithm (CQABC) and the channel case base was proposed. In the decision engine, the parameters at different layers of a wireless communication network were considered and the overall performance of the network was the optimization goal. A fast strategy with quantum rotation angle adjustment based on cellular automata and social cognitive strategy and two kinds of chaos initialization methods were used in the proposed CQABC. Furthermore, the historical experience and expertise was referred to build up the case library of the cognitive radio parameters based on the channel gain for a quick decision-making process. The results of simulation showed that the cross-layer decision engine is capable of dynamic re-configuration of parameters according to changes in the wireless communication environment and user requirements, on the meantime the proposed decision engine has better convergence, precision and stability than the traditional decision engine based on Binary artificial bee colony algorithm and quantum genetic algorithm.