Resource Management in Cloud Radio Networks

云无线电网络中的资源管理

• 批准号: RGPIN-2019-04819
• 负责人: Ghaderi, Majid
• 金额: $ 2.48万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715149
• 关键词: Resource; Management; Cloud; Radio; Networks

The explosive growth in creation and consumption of content on mobile devices has led to a massive increase in mobile data traffic in recent years. In order to satisfy growing user demands, mobile network operators are increasingly moving toward denser deployment of base stations. However, deploying a large number of base stations results in significant increases in capital and operational costs of the network. Cloud radio access network (CRAN) is an emerging mobile network architecture in which signal processing functions are moved to a datacenter, turning base stations into simple low-cost remote radio units. Not only the cloud-based architecture reduces the cost and complexity of deploying more base stations, but also allows signal processing functions to be virtualized in software modules that can be dynamically scaled to adapt to varying user demands, improving network scalability and performance. While CRAN is conceptually simple, several technological and intellectual challenges need to be addressed before it can be realized. A key challenge is the efficient and effective management of intertwined radio (e.g., radio frequency and transmit power) and datacenter (e.g., servers and interconnection links) resources. Our research view is that separate management of these resources is not optimal, even if each one is managed based on state-of-the-art techniques. The goal of this Discovery Program is to introduce and study, in a unified way, optimal or close to optimal algorithms for resource management in CRAN. One of the major concepts we pursue in our research, which has direct practical implications, is proactive resource management, where it is guaranteed that our algorithms perform well under dynamic demands without requiring costly and disruptive reconfigurations. We aim to make foundational contributions toward proactive resource management in cloud radio access networks by developing frameworks to study: i) online resource management, when no information about future demands is available, ii) robust resource management, when only partial information about future demands is available, and iii) autonomic resource management, when the optimal resource management algorithm is learnt autonomously. We focus on CRAN, having future mobile technologies in mind, but the basic tools and approaches to be built and researched are relevant to other cloud-based systems as well. The proposed research will produce new algorithms and theoretical frameworks for resource management in cloud-centric mobile networks. It will provide other researchers with an innovative framework to design autonomic resource management algorithms as well as a suite of efficient algorithms based on conventional optimization techniques, whose performance characteristics and trade-offs are well quantified. Canadian mobile operators and cloud service providers will be able to use our results to better inform their decisions when planning new services and applications.

近年来，移动设备上的内容创作和消费的爆炸式增长导致了移动数据流量的大幅增长。为了满足日益增长的用户需求，移动网络运营商正日益朝着基站密集部署的方向发展。然而，部署大量基站会导致网络的资本和运营成本显著增加。云无线接入网（CRAN）是一种新兴的移动网络架构，其中信号处理功能转移到数据中心，将基站转变为简单的低成本远程无线电单元。基于云的架构不仅降低了部署更多基站的成本和复杂性，而且还允许在软件模块中虚拟化信号处理功能，这些模块可以动态扩展以适应不同的用户需求，从而提高网络的可扩展性和性能。