基于带缓存的云接入网络最优能效设计

在云接入网络(Cloud-RAN)中，现有工作大多假定射频拉远头(RRH)不具备缓存功能。然而下一代通信网络具有以内容为中心的特性，因此在Cloud-RAN中考虑带缓存的RRHs也变得有必要。该文考虑在Cloud-RAN中有效设计缓存方案，并通过资源分配有效减轻前程链路负担。假设系统采用正交频分多址接入(OFDMA)技术，通过联合优化子载波(SC)分配，RRH选择与传输功率，最小化系统下行总功耗，并通过拉格朗日对偶分解转化非凸问题，获得最优分配方案。仿真结果表明，比起其它缓存方案，该文提出的优化算法可以有效地提升系统能效，满足未来通信需求。

Optimal Energy-efficient Design for Cache-based Cloud Radio Access Network

In Cloud Radio Access Network (Cloud-RAN), most of the existing work assumes Remote Radio Heads (RRHs) could not cache content. To better adapt the content-centric feature of next-generation communication networks, it is necessary to consider caching function for RRHs in Cloud-RAN. Motivated by this, this paper intends to design the suitable caching schemes and reduce the burden of fronthaul link burden through resource allocation. It is assumed the system utilizes Orthogonal Frequency Division Multiple Access (OFDMA) technique. A jointly optimization scheme of SubCarrier (SC) allocation, RRH selection, and transmission power is proposed to minimize the total downlink power consumption. To transform the original non-convex problem, a Lagrange dual decomposition is utilized to design the optimal allocation scheme. The experimental results show that the proposed algorithms can effectively improve the energy efficiency of the system, which meets the requirements of green communication in the future.