In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multipoint transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs’ capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., CoMP selection, and resource allocation. We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at scaling up the capacity maximally for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.

中文翻译：

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在 C-RAN 中通过远程无线电头端协作优化以用户为中心的性能

在云无线接入网络 (C-RAN) 中，分布式远程射频头 (RRH) 由云中的基带单元 (BBU) 协调。信号处理的集中化为 RRH 的协作多点传输 (CoMP) 提供了灵活性，以协作服务于用户设备 (UE)。我们的目标是通过联合优化服务 UE 的 RRH 选择，即 CoMP 选择和资源分配来提高 UE 的容量性能。我们分析了问题的计算复杂度。接下来，我们证明在固定 CoMP 选择下，最优资源分配相当于求解一个所谓的迭代函数。针对以用户为中心的网络优化，我们提出了一种联合优化问题的算法，旨在最大程度地扩展任何感兴趣的目标 UE 组的容量。