宏-飞蜂窝双层网络中基于毫微微基站分组的资源分配

针对宏-飞蜂窝双层网络模型中宏小区（Macrocell）用户层和毫微微小区（Femtocell）用户层之间的跨层干扰和Femtocell之间的同层干扰，提出了一种基于毫微微基站分组的资源分配算法。该算法包括两个部分：一部分是宏基站先利用改进的差额法，设置虚拟的宏用户（MUE），将之变为平衡的指派问题再为宏小区用户分配信道，然后用注水算法分配功率，保证宏小区用户的正常传输。另一部分是在保证宏小区用户的服务质量基础上，采用一种增强型的蚁群优化（EACO）算法，设定信息素浓度范围后对毫微微小区进行分组，避免了原始的蚁群算法有可能陷入局部最优的现象；再利用一种启发式算法和分布式功率分配算法分别对毫微微用户（FUE）进行信道和功率分配，在满足毫微微小区用户的数据速率需求下，最大化频谱效率。仿真结果表明，EACO有效地抑制了跨层干扰和同层干扰，既能保证用户的数据速率需求，又能有效提升网络频谱效率。

Resource allocation based on femto base station in Macrocell-Femtocell networks

Aiming at the cross-layer interference between the macrocell user layer and the femtocell user layer and the same layer interference between femtocells in the macrocell-femtocell dual-layer network model, a resource allocation algorithm based on femtocell base stations was proposed. The algorithm consists of two parts:the one is that, the Macrocell base station firstly used the improved difference method, set the virtual Macro User Equipment (MUE), and turned it into a balanced assignment problem and allocated the channel to the macrocell user, and then used water-filling algorithm for power allocation to ensure the transmission of macrocell users. The other part is that, on the basis of guaranteeing the service quality of the macrocell users, an Enhanced Ant Colony Optimization (EACO) algorithm was adopted to group the femtocells after setting the pheromone concentration range, which avoided the possibility that the original ant colony algorithm may fall into a local optimum. Then, a heuristic algorithm and a distributed power allocation algorithm were used to allocate the channel and power to the Femto User Equipment (FUE) respectively. The spectral efficiency was maximized under the data rate requirement of the femtocell users. The simulation results show that EACO effectively suppresses cross-layer interference and same-level interference, which can guarantee the data rate requirement of users and improve the efficiency of network spectrum effectively.