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Joint User Association and Power Allocation for Millimeter-Wave Ultra-Dense Networks

机译:毫米波超密集网络的联合用户协会和功率分配

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To meet the demand of various high-speed data rate services as well as serving an exponential increment of mobile devices, millimeter-Wave (mmWave) communication in ultra-dense networks (UDNs) has been considered as a promising technology for future wireless communication systems. By deploying multiple small-cell base stations (SBSs), the dense topology combining high frequency mmWave is expected to grow not only the users (UEs) throughput but also the energy efficiency (EE) of the whole networks. Exploiting the benefits from mmWave and UDNs, in this paper, we introduce a new approach for jointly optimizing SBS-UE association and power allocation to maximize the system EE while guaranteeing the quality of service (QoS) constraints for each UE. Specifically, the throughput fairness among UEs is also taken into account by formulating UE throughput maxmin problem. Unfortunately, such SBS-UE association problem poses a new challenge since it reflects as a complex mixed-integer non-convex problem. On the other hand, the power allocation problem is in nonconvexity structure, which is impossible to handle with the association problem concurrently. Tackling those issues, an alternating descent method is proposed to separate the primal optimization problem into two subproblems and handle one-by-one at each iteration. In particular, the SBS-UE association problem is reformulated using the penalty approach. Then, successive convex programming is developed to convert non-convex problem into the simple convex quadratic functions at each iteration. Numerical results are provided to demonstrate the convergence and low-complexity of our proposed schemes, where the increment of the objective function is guaranteed at each iteration.
机译:为了满足各种高速数据速率服务的需求以及服务于移动设备的指数增长,超密集网络(UDN)中的毫米波(mmWave)通信已被认为是未来无线通信系统的有希望的技术。通过部署多个小蜂窝基站(SBS),结合了高频mmWave的密集拓扑不仅有望提高用户(UE)吞吐量,还可以提高整个网络的能效(EE)。利用mmWave和UDN的好处,在本文中,我们介绍了一种新方法,用于联合优化SBS-UE关联和功率分配以最大化系统EE,同时保证每个UE的服务质量(QoS)约束。具体地,通过制定UE吞吐量maxmin问题,还考虑了UE之间的吞吐量公平性。不幸的是,这种SBS-UE关联问题提出了一个新的挑战,因为它反映为一个复杂的混合整数非凸问题。另一方面,功率分配问题是非凸性结构,不可能同时解决关联问题。为了解决这些问题,提出了一种交替下降方法,将原始优化问题分为两个子问题,并在每次迭代中一一处理。特别地,使用惩罚方法来重新构造SBS-UE关联问题。然后,开发了连续凸规划,以在每次迭代时将非凸问题转换为简单凸二次函数。数值结果提供了证明我们提出的方案的收敛性和低复杂度的方法,其中每次迭代都保证了目标函数的增量。

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