In cloud radio access network (Cloud-RAN), dense remote radio heads (RRHs) connect to baseband unit (BBU) pool through optical transport link, causing significant interference and power consumption in multicast downlinks. Coordinated beamforming with RRHs clustering is expected to be a key element towards improving network performance with more consideration of energy efficiency and interference management. This letter proposes an energy-aware joint clustering and scheduling (EA-JCS) scheme for multicast beamforming in Cloud-RAN downlink, which is formulated as a network utility maximization problem with fractional formulation and low-rank constraints. To efficiently solve this problem, we first develop a heuristic algorithm to provide sub-optimal performance by Dinkelbach’s transformation. To handle non-convex formulation, we propose an iterative approximation algorithm with linear approximation and constraints relaxation. The effectiveness of the proposed scheme is confirmed by simulations.

中文翻译：

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Cloud-RAN下行链路中多播波束成形的能量感知联合集群和调度

在云无线接入网 (Cloud-RAN) 中，密集的远程射频头 (RRH) 通过光传输链路连接到基带单元 (BBU) 池，在组播下行链路中造成显着的干扰和功耗。具有 RRH 集群的协调波束成形有望成为提高网络性能的关键要素，更多地考虑能源效率和干扰管理。这封信提出了一种能量感知联合集群和调度 (EA-JCS) 方案，用于 Cloud-RAN 下行链路中的多播波束成形，该方案被表述为具有分数公式和低秩约束的网络效用最大化问题。为了有效地解决这个问题，我们首先开发了一种启发式算法，通过丁克尔巴赫变换提供次优性能。为了处理非凸公式，我们提出了一种具有线性逼近和约束松弛的迭代逼近算法。通过仿真证实了所提出方案的有效性。