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首页> 外文期刊>IEEE transactions on mobile computing >Energy-Efficient Resource Allocation in C-RANs with Capacity-Limited Fronthaul
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Energy-Efficient Resource Allocation in C-RANs with Capacity-Limited Fronthaul

机译:C-RAN中的节能资源配置,具有有限的Fronthaul

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Cloud Radio Access Network (C-RAN) is a key architecture for 5G cellular wireless network that aims at improving spectral and energy efficiency of the network by uniting traditional RAN with cloud computing. In this paper, a novel resource allocation scheme that optimizes the network energy efficiency of a C-RAN is designed. First, an energy consumption model that characterizes the computation energy of the BaseBand Unit (BBU) is introduced based on empirical results collected from a programmable C-RAN testbed. Then, an optimization problem is formulated to maximize the energy efficiency of the network, subject to practical constraints including Quality of Service (QoS) requirement, radio remote head transmit power, and fronthaul capacity limits. The formulated Network Energy Efficiency Maximization (NEEM) problem jointly considers the tradeoff among the network accumulated data rate, BBU power consumption, fronthaul cost, and beamforming design. To deal with the non-convexity and mixed-integer nature of the problem, we utilize successive convex approximation methods to transform the original problem into the equivalent Weighted Sum-Rate (WSR) maximization problem. We then propose a provably-convergent iterative method to solve the resulting WSR problem. Extensive simulation results coupled with real-time experiments on a small-scale C-RAN testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.
机译:云无线电接入网络(C-RAN)是5G蜂窝无线网络的关键架构,其旨在通过使用云计算的传统运行来提高网络的光谱和能量效率。本文设计了一种新的资源分配方案,用于优化C-RAN的网络能效。首先,基于从可编程C-RAN测试的验证结果引入了表征基带单元(BBU)的计算能量的能量消耗模型。然后,制定优化问题以最大化网络的能效,但经过实际限制,包括服务质量(QoS)要求,无线电远程头传递功率和Fronthaul容量限制。制定的网络能效最大化(NEEM)问题共同考虑了网络累计数据速率,BBU功耗,FRONTHAUL成本和波束成形设计之间的权衡。为了处理问题的非凸性和混合整数性质,我们利用连续的凸近似方法将原始问题转换为等同的加权和速率(WSR)最大化问题。然后,我们提出了一种可透明的会聚迭代方法来解决所产生的WSR问题。广泛的模拟结果与小型C-RAN测试的实时实验相结合,展示了我们提出的资源分配方案的有效性及其优于现有方法。

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