EARS: Collaborative Research: Maximizing Spatio-Temporal Spectrum Efficiency in the Cloud

EARS：协作研究：最大化云中的时空频谱效率

• 批准号: 1547353
• 负责人: Raghupathy Sivakumar
• 金额: $ 45万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547353&HistoricalAwards=false
• 关键词: EARS; Collaborative; Research; Maximizing; Spatio

Over the last few years wireless data traffic has drastically increased. To encounter this trend, spatio-temporal spectrum utilization has to be dramatically improved. Achieving this goal, however, need to address several fundamental challenges including discovering more TV white spaces (TVWS) in urban areas where geo-location databases generally fail, increasing spectrum efficiency through network densification with excessive intercell interference, and enabling the shift from one-dimensional spectrum sharing to multidimensional infrastructure sharing. The objective of this project is to address these important challenges by systematically exploiting the potential of the evolutionary cloud radio access network (Cloud-RAN) architecture. The research solutions of this project are expected to fundamentally address the spectrum inefficiency of current closed and distributed radio access networks and to meet the demands of fast-growing mobile traffic along with the rapidly-evolving and diverse network applications, thus providing uniform, ubiquitous network services for network users.This project develops a new and holistic spectrum management framework, which maximizes spatio-temporal spectrum efficiency through innovative cloud-augmented spectrum mapping, cloud-based spectral resource orchestrating, and virtualization-enabled dynamic infrastructure sharing. The project consists of four highly interrelated thrusts: (1) an iterative Bayesian decision framework, which coherently combines Bayesian spatial prediction and Bayesian experimental design, which optimally selects a small number of mobile users as well as their locations to enable metropolitan-scale geo-location databases with high spatial-resolution and high TVWS detection accuracy; (2) utilizing such spectrum map, a throughput-optimal joint clustering and scheduling framework is developed, which jointly selects the clustering patterns of remote radio heads (RRHs) and the transmission schedules of network users, such that each user has bounded average queueing delay and the sum-rate of the formed virtual base stations (VBSs) through RRH clustering is maximized; (3) novel wireless virtualization tools are developed, which can abstract, slice, and instantiate multiple virtual networks on a common wireless physical infrastructure; (4) the proposed solutions are demonstrated with an experiment testbed based on commercial software-defined radio frontends and high-performance servers.

在过去几年中，无线数据流量急剧增加。为了应对这一趋势，必须大幅提高时空频谱的利用率。然而，要实现这一目标，需要解决几个根本性的挑战，包括在地理位置数据库通常失效的城市地区发现更多的电视空白空间（TVWS），通过过度蜂窝间干扰的网络密度提高频谱效率，并实现从一维频谱共享到多维基础设施共享的转变。该项目的目标是通过系统地开发演进云无线接入网络（cloud - ran）架构的潜力来解决这些重要挑战。本项目的研究解决方案有望从根本上解决当前封闭、分布式无线接入网频谱效率低的问题，满足快速增长的移动流量和快速演进、多样化的网络应用需求，从而为网络用户提供统一、无处不在的网络服务。该项目开发了一个全新的整体频谱管理框架，通过创新的云增强频谱映射、基于云的频谱资源编排和虚拟化的动态基础设施共享，最大限度地提高了时空频谱效率。该项目包括四个高度相关的重点：(1)迭代贝叶斯决策框架，该框架将贝叶斯空间预测和贝叶斯实验设计相结合，优化选择少量移动用户及其位置，使大都市规模的地理位置数据库具有高空间分辨率和高TVWS检测精度；(2)利用该频谱图，开发了一种吞吐量最优的联合聚类调度框架，该框架联合选择远程无线电头（RRHs）的聚类模式和网络用户的传输调度，使每个用户的平均排队延迟有界，通过RRH聚类形成的虚拟基站（VBSs）的总和速率最大；(3)开发了新颖的无线虚拟化工具，该工具可以在公共无线物理基础设施上抽象、切片和实例化多个虚拟网络；(4)通过基于商用软件定义无线电前端和高性能服务器的实验测试平台对所提出的解决方案进行了验证。