认知异构网络中基于不完全频谱感知的资源分配算法

摘要

针对认知异构网络中的干扰抑制问题,文中研究了如何降低其对宏用户(MU)的干扰并提高系统吞吐量.通过全面分析干扰来源,建立不完全频谱感知下的干扰模型;结合用户拓扑信息,综合考虑总功率约束和干扰约束,以最大化下行链路的吞吐量为准则构建优化问题;然后分析KKT条件,简化优化问题,进而设计出基于不完全频谱感知的分步式资源分配算法.仿真结果及性能分析表明,相比于基于完全频谱感知的资源分配算法,所提算法对MU造成的干扰更小,并且获得了更优的吞吐量性能.%In order to solve the problem about interference mitigation in the cognitive heterogeneous networks,this paper studied how to reduce the interference to macrocell users(MU) and improve system throughput.By analyzing the source of interference completely,the interference model with imperfect spectrum sensing is established.Based on the user's topology information,the optimize problem is built to maximize the downlink throughput with considering total power constraint and interference constraint.Then the problem is simplified based on the analysis of Karush-Kuhn-Tucker(KKT) conditions,and the resource allocation algorithm is designed with the imperfect spectrum sensing.Simulation results and performance analysis show that the proposed algorithm has less interference to MU than the algorithm with perfect spectrum sensing,and achieves better throughput performance.