arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1607.06231cs.ITmath.IT
收藏

绿云无线接入网

Green Cloud Radio Access Networks

Alaa Alameer, Aydin Sezgin

摘要:

云无线电接入网络(C-RAN)是实现第五代(5G)通信网络的宏伟愿景的有前途的技术。尽管C-RAN具有潜在的好处,但运营成本仍然是一个具有挑战性的问题,主要是由于集中式处理方案和大量连接到云的操作远程无线电头端(RRH)。在这项工作中,我们考虑的设置,其中CRAN部分供电的一组可再生能源(RES),我们的目标是最大限度地减少处理/回程成本在云中心以及在RRH的传输功率,同时满足一些用户的服务质量(QoS)的服务质量。该问题首先被表述为一个具有大量优化变量的混合整数非线性规划(MINLP)。底层的NLP是非凸的,尽管我们通过使用均方误差(MSE)-速率关系重新表述问题来解决这个问题。考虑到问题的大规模,我们引入松弛变量来分解重新制定的(MINLP),并通过使用交替方向乘子法(ADMM)算法实现分布式优化框架的应用。

英文摘要:

Cloud radio access networks (C-RAN) are a promising technology to enable the ambitious vision of the fifth generation (5G) communication networks. In spite of the potential benefits of C-RAN, the operational costs are still a challenging issue, mainly due to the centralized processing scheme and the large number of operating remote radio head (RRH) connecting to the cloud. In this work we consider a setup in which a CRAN is powered partially with a set of renewable energy sources (RESs), our aim is to minimize the processing/backhauling costs at the cloud centre as well as the transmission power at the RRHs, while satisfying some user quality of service (QoS). This problem is first formulated as a mixed integer non linear program (MINLP) with a large number of optimization variables. The underlying NLP is non-convex, though we address this issue through reformulating the problem using the mean squared error (MSE)-rate relation. To account to the large-scale of the problem, we introduce slack variables to decompose the reformulated (MINLP) and enable the application of a distributed optimization framework by using the alternating direction method of multipliers (ADMM) algorithm.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑