针对传统机会认知无线电网络容量有限的问题，提出了基于拉格朗日对偶的认知无线电网络最优资源分配算法。首先，将一个用户分配给每个子载波；然后，使用标准的凸优化方法确定每个子载波的对应功率，仅一个用户可获得功率正值；最后，利用拉格朗日对偶分解法同时分配CR网络中的子载波和功率，最大限度地提高系统的总容量。使用长期演进真实场景参数与空间信道传播模型评估了所提算法的有效性，仿真结果表明，相比次优资源分配算法，所提算法的总容量平均分别提高了9.3%，相比基于任意输入分布的最优资源分配算法，总容量提高了28.7%，并取得了较快的收敛速率，可以很好地用于解决无线电网络资源配置中的容量问题。%Since the traditional opportunistic cognitive radio(CR)network is limited by the capacity,an optimal source al⁃location algorithm of cognitive radio network based on Lagrange duality is proposed. With the algorithm,a user is allocated to each subcarrier;the corresponding power of each subcarrier is determined with the standard convex optimization method,and only a user can obtain the positive power value;the Lagrange duality decomposition method is used to allocate the subcarrier and power in CR network simultaneously to increase the system total capacity to the maximum extent. The effectiveness of the proposed algorithm was verified by means of the long term real scene parameters evoluting and space channel propagation model. The experimental results show that the total capacity of the proposed algorithm is 9.3% higher than that of the sub⁃optimal re⁃source allocation algorithm,and 28.7% higher than that of the optimal resource allocation algorithm based on arbitrary input dis⁃tribution,the propose algorithm has fast convergence rate,and can solve the capacity problem in cognitive radio network re⁃source allocation effectively.
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