Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks

Energy Efficient User Association, Resource Allocation and Caching Deployment in Fog Radio Access Networks
复制标题

雾无线接入网络中的节能用户关联、资源分配和缓存部署

DOI:
10.1109/tvt.2021.3131720
复制
发表时间:
2022-02
影响因子:
6.8
通讯作者:
Victor CM Leung
Victor CM Leung
中科院分区:
计算机科学2区
文献类型:
--
作者:
Xiangnan Liu;Haijun Zhang;Keping Long;Arumugam Nallanathan;Victor CM Leung

文献摘要

参考文献

被引文献

相似文献

通过下一代无线通信网络可以实现异构雾无线接入网(Fog-RAN)、雾计算与传统异构无线接入网的融合。然而,大多数解决方案都局限于频谱效率优化,而雾接入点(F-AP)存在的跨层干扰会严重影响网络性能。在本文中,用户关联,资源分配(包括带宽和功率),并在异构Fog-RAN缓存部署进行了研究,考虑能源效率和跨层干扰缓解。具体而言,用户关联,资源分配和缓存策略制定为一个非凸优化问题,然后转化为凸问题,这可以通过提出的算法的交替方向的乘法器(ADMM)的概念的基础上解决。然后提出了一种基于ADMM的算法来提高Fog-RAN的能量效率。仿真结果表明,与现有算法相比,该算法具有较好的收敛性和有效性。
The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness.
通过虚拟化实现以信息为中心的无线网络中的虚拟资源分配
DOI: 10.1109/tvt.2016.2530716
发表时间: 2016-02
影响因子: 6.8
作者:
Liang Chengchao;Yu F. Richard;Yao Haipeng;Han Zhu
通讯作者: Han Zhu
通过深度强化学习实现 Fog-RAN 切片
DOI: 10.1109/twc.2020.2965927
发表时间: 2020-01
影响因子: 10.4
作者:
Xiang Hongyu;Yan Shi;Peng Mugen
通讯作者: Peng Mugen
基于博弈论的小型视频缓存系统的定价和资源分配
DOI: 10.1109/jsac.2016.2577278
发表时间: 2016-02
影响因子: 16.4
作者:
Youjia Chen;Zihuai Lin;Branka Vucetic;Lajos Hanzo
通讯作者: Lajos Hanzo
DOI: 10.1109/jiot.2017.2786639
发表时间: 2018-06-01
影响因子: 10.6
作者:
Chernyshev, Maxim;Baig, Zubair;Zeadally, Sherali
通讯作者: Zeadally, Sherali
DOI: 10.1109/tccn.2019.2957457
发表时间: 2020-03
影响因子: 8.6
作者:
Yujie Tang;Peng Yang;Wen Wu;J. Mark;X. Shen
通讯作者: Yujie Tang;Peng Yang;Wen Wu;J. Mark;X. Shen