In millimeter wave (mmW) dense femto-networks, major challenges are overcoming the performance loss imposed by the channel and managing the co-channel interference. The former is due to the mmW susceptibility to pathloss and shadowing and the latter is due to density of the network. We cope with both challenges by a clustering method designed for mmW environment. In our approach, the femto access points (FAP) and femto users (FU) are clustered based on having the most line of sight connectivity. We modify this binary optimization problem into a continuous problem using deductive penalty functions and solve it by difference of two convex functions (D.C.) programming. Our clustering algorithm achieves higher data rate compared to the foremost clustering method. We also propose a technique to assign FUs to FAPs in each cluster which has near-optimal performance and polynomial time complexity. We solve mixed integer nonlinear programming of power and sub-channel allocation by D.C. programming. Instead of using the deductive penalty terms in D.C. programming, we penalize the objective function in a multiplicative manner. Thus, the penalty term depends on both constraint violation and objective function. Our scheme achieves around 10% higher data rate compared to the method using deductive penalty terms.

中文翻译：

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毫米波 Femtocell 网络中基于集群的资源分配和用户关联

在毫米波 (mmW) 密集毫微微网络中，主要挑战是克服信道带来的性能损失和管理同信道干扰。前者是由于毫米波对路径损耗和阴影的敏感性，后者是由于网络密度。我们通过为毫米波环境设计的聚类方法来应对这两个挑战。在我们的方法中，毫微微接入点 (FAP) 和毫微微用户 (FU) 基于具有最多的视线连接进行聚类。我们使用演绎惩罚函数将这个二元优化问题修改为一个连续问题，并通过两个凸函数 (DC) 编程的差异来解决它。与最重要的聚类方法相比，我们的聚类算法实现了更高的数据速率。我们还提出了一种将 FU 分配给每个集群中的 FAP 的技术，该技术具有接近最佳的性能和多项式时间复杂度。我们通过直流编程求解功率和子信道分配的混合整数非线性规划。我们没有在 DC 编程中使用演绎惩罚项，而是以乘法方式惩罚目标函数。因此，惩罚项取决于约束违反和目标函数。与使用演绎惩罚项的方法相比，我们的方案实现了大约 10% 的数据速率。惩罚项取决于约束违反和目标函数。与使用演绎惩罚项的方法相比，我们的方案实现了大约 10% 的数据速率。惩罚项取决于约束违反和目标函数。与使用演绎惩罚项的方法相比，我们的方案实现了大约 10% 的数据速率。