超密集网络中基于分簇的资源分配算法

针对超密集网络(ultra-dense network， UDN)中，严重的小区间干扰制约终端用户的数据速率问题，提出一种基于染色分簇的资源分配方案。该方案采用图论中的染色算法对微蜂窝接入点(femtocell access points， FAPs)进行分簇，利用簇内每个微蜂窝用户(femtocell user equipments，FUEs)的待发送数据量、排队等待时延以及受到的干扰强度来构建相应的优先级，计算每个簇的优先级，并设定高优先级的簇可优先获得信道增益良好的子信道；最后由拉格朗日乘子法求解功率分配方案，即利用KKT(Karush-Kuhn-Tucker)条件和注水算法为FUEs分配功率。仿真结果表明，该方案能够有效地减小微蜂窝接入点之间的相互干扰，极大地满足用户的服务需求，同时提升了系统吞吐量和频谱效率。并且基于最大功率和最低速率的公平性准则能够动态地调整子信道功率，进一步提升了FUEs间的公平性。

Clustering-based resource allocation algorithm in ultra dense network

In ultra-dense network(UDN), the serious inter-cell interference has restricted the data rate of users. In view of this problem, a new coloring-based clustering resource allocation scheme is proposed in this paper. The scheme is divided into three steps: the first step uses graph-based coloring algorithm for femtocell access points (FAPs). The second step uses the amount of data to be transmitted, the queuing delay and the interference intensity of each femtocell user equipment (FUEs) in the cluster to build the corresponding priority, and then calculate the priority of each cluster. Such clusters owning high priority can received sub-channel of good channel gain first. The third step allocates power for FUEs by Karush-Kuhn-Tucker(KKT) and water-filling algorithm. Simulation results show that the proposed scheme can effectively reduce the interference among femtocells and also greatly satisfy the needs of users, while improving the throughput and spectrum efficiency. In addition, the power of each sub-channel is dynamically adjusted based on the fairness criterion of maximum power and minimum rate which further improves the fairness degree among FUEs.