在认知无线电网络中,不连续的频谱片段可以通过频谱聚合来满足认知用户的用频需求。常用的频谱聚合算法有具有聚合能力的频谱分配算法、最大满足算法和最小切换算法等。文章在这几种算法的基础上提出了一种能够预测高级用户出现概率的方法,从而减少网络开销;对于用频需求较大的情况,提出了折中算法和片段最大化算法,增加了可支持的用户数量,并减少了网络开销。仿真结果表明,上述算法具有较好的效果。 In cognitive radio networks, discontinuous spectrum segments can meet the frequency requirements of cognitive users through spectrum aggregation. Commonly used spectral aggregation algorithm has the ability to aggregate the spectrum allocation algorithm, the maximum to meet the algorithm and the minimum switching algorithm. Based on these algorithms, the paper proposes a method that can predict the probability of emergence of advanced users, thus reducing the network overhead. For the case of large demand for frequency, a compromise algorithm and fragment maximization algorithm are proposed, Support for the number of users, and reduce network overhead. Simulation results show that the above algorithm has good effect.