超密集组网下一种基于干扰增量降低的分簇算法

超密集网络(UDNs)拉近了终端与节点间的距离，使得网络频谱效率大幅度提高，扩展了系统容量，但是小区边缘用户的性能严重下降。合理规划的虚拟小区(VC)只能降低中等规模UDNs的干扰，而重叠基站下的用户的干扰需要协作用户簇的方法来解决。该文提出了一种干扰增量降低(IIR)的用户分簇算法，通过在簇间不断交换带来最大干扰的用户，最小化簇内的干扰和，最终最大化系统和速率。该算法在不提高K均值算法的复杂度的同时，不需要指定簇首，避免陷入局部最优。仿真结果表明，网络密集部署时，有效提高系统和速率，尤其是边缘用户的吞吐量。

A Cluster Algorithm Based on Interference Increment Reduction in Ultra-Dense Network

Ultra-Dense Networks (UDNs) shorten the distance between terminals and nodes, which improve greatly the spectral efficiency and expand the system capacity. But the performance of cell edge users is seriously degraded. Reasonable planning of Virtual Cell (VC) can only reduce the interference of moderate scale UDNs, while the interference of users under overlapped base stations in a virtual cell needs to be solved by cooperative user clusters. A user clustering algorithm with Interference Increment Reduction (IIR) is proposed, which minimizes the sum of intra-cluster interference and ultimately maximizes system sum rate by continuously switching users with maximum interference between clusters. Compared with K-means algorithm, this algorithm, no need of specifying cluster heads, avoids local optimum without increasement of the computation complexity. The simulation results show that the system sum rate, especially the throughput of edge users, can be effectively improved when the network is densely deployed.