超密集异构网络中的一种混合资源分配方法

针对在宏小区覆盖范围内高密度部署小小区所带来的跨层干扰和同层干扰，研究了超密集异构蜂窝网络中的资源分配问题，采用了一种共享频谱与分离频谱共存的混合频谱分配方案。依据簇中小基站与宏基站的干扰是否超过设定的阈值，将簇进行分类并以不同的方式进行资源分配，超过阈值的簇采用分离频谱的方式分配资源，反之，可共享整段频谱。而在每个小基站簇内，在相应的约束条件下采用对偶分解法求出规划的优化目标，为每个小基站分配子信道和功率。基于联合分配方案提出算法1和算法2(次梯度算法)，算法1最接近最优解决策略，而算法2可以解决高复杂度问题，具有更高的实用性。仿真结果表明，所提方案能够有效地抑制干扰、提升系统容量，同时兼顾用户间公平性。

Ahybrid resource allocation method in ultra-dense heterogeneous networks

Deploying small cells underlaying macro-cell is a promising way to improve the capacity and the coverage of cellular systems. However, the two-tier network also brings the cross-tier and intra-tier interference which should be properly addressed so as to obtain a potential performance gain. The resource allocation problem is studied and developed in an ultra-dense heterogeneous cellular network. Small cells are divided into disjoint groups according to the number of small cells which depends on whether the interference between the small cell and the microcell exceeds a predefined interference threshold. In a cluster, if the number is more than half, then the small cells will get the spectrum resource in a separating spectrum manner with microcell, otherwise in a share spectrum manner. The decomposition method is used to solve the resource allocation optimization problem subject to corresponding constraints in order to allocate the sub-channels and power among small cells in each cluster. Algorithm 1 and algorithm 2 are proposed based on the joint allocation scheme. Algorithm 1 is closest to the optimal solution strategy, while algorithm 2 (sub-gradient algorithm) can solve the high complexity problem and has higher practicability. The simulation results show that the proposed algorithm can effectively suppress the interference, improve the system capacity, and also take into account the fairness among users.