认知无线电中结合差异性的免疫克隆优化频谱分配算法

频谱分配是认知无线电中的重要问题，而传统的频谱分配算法并未考虑频谱的差异性。提出一种基于免疫克隆优化算法、考虑频谱差异性的频谱分配算法，算法引入可信度矩阵对频谱的时间差异性进行建模。进行约束处理时，通过差异性算子(DCSO)的使用能将可信度更高的频谱分配给认知用户，从而提高系统的总收益。对于冲突激烈的认知用户，使用公平性算子（FCSO）能够增加它们被分配频谱资源的可能性，从而提高系统的公平性效益。仿真实验表明，相较于传统的免疫克隆优化算法、颜色敏感算法和遗传算法，本算法能显著增加网络的总收益、可信度，提高网络的公平性。

Spectrum allocation algorithm in cognitive radio based on immune-clonal-selection and difference

Spectrum allocation is an important issue in cognitive radio, but the spectrum difference is rarely discussed in traditional spectrum allocation algorithms. A new spectrum allocation algorithm is proposed, which is based on Immune-Clonal-Selection (ICS) and takes account of the spectrum difference. A reliability matrix is introduced to model the time difference of spectrum. The algorithm shows a rise in total network bandwidth by using the Difference-Constraint Satisfaction Operator when two cognitive users have interference using the same spectrum. The Difference Constraint Satisfaction Operator can allocate spectrum in higher credibility to cognitive users. The algorithm also shows an improvement in fairness by using the Fairness-Constraint Satisfaction Operator to deal with the interference. The Fairness-Constraint Satisfaction Operator can allocate spectrum to cognitive users with more intensive conflicts. Simulation results show that, compared with the traditional ICS algorithm and color-sensitive graph coloring (CSGC) algorithm and Genetic algorithm (GA), DICS can achieve higher total network bandwidth, higher credibility and better fairness.