EARS: Utilizing Diverse Spectrum Bands in Cellular Networks - A Unified Information Learning and Decision Making Approach

EARS：在蜂窝网络中利用不同的频段 - 一种统一的信息学习和决策方法

• 批准号: 1547461
• 负责人: Xin Liu
• 金额: $ 35.38万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547461&HistoricalAwards=false
• 关键词: EARS; Utilizing; Diverse; Spectrum; Bands

Driven by the skyrocketing demand for high data-rate mobile services and enabled by regulatory and technology advances, cellular service providers are augmenting or in the processing of augmenting their own licensed spectrum with a variety of supplemental bands, including unlicensed bands, lightly-licensed bands, secondary bands, and high frequency bands. This project studies how to effectively utilize such bands, in particular, how to learn the service availability and quality on different spectrum bands, and how to effectively use these bands under budget constraints. To address this challenge, the researchers propose a joint information learning and decision making framework with context and under budget. This mathematical framework connects two important yet mostly independently studied research areas, information learning and optimal decision making. The investigation provides important insights in understanding, designing, and analyzing joint information learning and decision making algorithms. In addition, because the generality and importance of such problems, the proposed approaches can be applied in other areas, such as wireless network control, crowd-sourcing, and online-ad allocation.While an intuitive approach, it is challenging to design the joint learning and decision algorithms and to analyze their performance because budget constraints introduce coupling among contexts and across time; and the information learning and decision making are closely coupled and jointly evolving processes. This project consists of three main thrusts: 1) General framework: The researchers strive for not only the fundamental understanding of joint learning and decision, but also algorithms with practical simplicity and theoretical performance guarantees. Such algorithms enable efficient supplemental spectrum utilization in cellular networks; 2) Sparsity and Structure: Context information, such as time, location, and application type, provides useful information in selecting the appropriate supplemental bands. However, the learning curve (cost) increases as the number of context-action pairs increases. The researchers develop algorithms that can accelerate the learning by exploiting the domain knowledge, system structure, and compressing techniques; and 3) Practical Issues: When considering the dynamic spectrum accessing in real wireless networks, practical issues arise. In particular, the researchers study non-stationary systems where the system statistics vary over time, and warm-start systems where certain prior information is available.

在对高数据率移动服务的需求飙升的驱动下，并由监管和技术进步启用，蜂窝服务提供商正在增强或处理以各种补充频带（包括无执照的频段），包括无效的频段，轻微固定的频段，二级频段，高频频段和高频频段来增强自己的许可光谱。该项目研究了如何有效利用此类频段，特别是如何在不同频谱频段上学习服务的可用性和质量，以及如何在预算限制下有效使用这些频段。为了应对这一挑战，研究人员提出了一个与上下文和预算下的联合信息学习和决策框架。这个数学框架连接了两个重要但主要研究的研究领域，信息学习和最佳决策。调查提供了有关理解，设计和分析联合信息学习和决策算法的重要见解。此外，由于此类问题的普遍性和重要性，因此可以在其他领域（例如无线网络控制，众筹和在线分配）应用所提出的方法。虽然直觉方法，因此设计联合学习和决策算法并分析其绩效，因为预算约束在上下文和跨时间之间引入耦合，这是一项挑战。信息学习和决策是紧密耦合并共同发展的过程。该项目由三个主要推力组成：1）一般框架：研究人员不仅努力建立对联合学习和决策的基本理解，而且努力具有实践上的简单性和理论性能保证的算法。这种算法可以在细胞网络中有效的补充频谱利用； 2）稀疏性和结构：上下文信息，例如时间，位置和应用程序类型，在选择适当的补充频段时提供了有用的信息。但是，随着上下文对的数量增加，学习曲线（成本）会增加。研究人员开发了可以通过利用领域知识，系统结构和压缩技术来加速学习的算法。 3）实际问题：在考虑实际无线网络中访问的动态频谱时，会出现实际问题。特别是，研究人员研究了系统统计信息随着时间而变化的非平稳系统，以及可以使用某些先前信息的温暖启动系统。

项目成果

Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits

• 发表时间: 2017-09
• 作者: Huasen Wu;Xueying Guo;Xin Liu

Cellular Network Configuration via Online Learning and Joint Optimization

通过在线学习和联合优化进行蜂窝网络配置

• 发表时间: 2017
• 期刊: IEEE Big Data Conference
• 作者: Guo, Xueying;Trimponiasy, George;Wang, Xiaoxiao;Chen, Zhitang;Geng, Yanhui;Liu, Xin
• 通讯作者: Liu, Xin