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

在对高数据速率移动服务需求暴涨的推动下，在监管和技术进步的推动下，蜂窝服务提供商正在通过各种补充频段（包括未授权频段、少量授权频段、二级频段和高频频段）增加或正在增加自己的许可频谱。本项目研究如何有效利用这些频段，特别是如何了解不同频段上的业务可用性和质量，以及如何在预算约束下有效利用这些频段。为了应对这一挑战，研究人员提出了一个具有背景和预算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大多独立研究的研究领域，信息学习和最优决策。该研究为理解、设计和分析联合信息学习和决策算法提供了重要的见解。此外，由于这些问题的普遍性和重要性，所提出的方法可以应用于其他领域，如无线网络控制、众包和在线广告分配。虽然是一种直观的方法，但由于预算约束引入了上下文和时间之间的耦合，因此设计联合学习和决策算法并分析其性能具有挑战性；信息学习和决策是一个紧密耦合、共同演进的过程。本项目包括三个主要重点：1)总体框架：研究人员不仅要对联合学习和决策有基本的理解，而且要寻求具有实际简单性和理论性能保证的算法。这种算法能够在蜂窝网络中有效地利用补充频谱；2)稀疏性和结构：上下文信息，如时间、位置和应用类型，为选择合适的补充波段提供了有用的信息。然而，学习曲线（成本）随着上下文-动作对数量的增加而增加。研究人员开发了利用领域知识、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的动态频谱接入时，会出现实际问题。特别是，研究人员研究了系统统计随时间变化的非平稳系统，以及某些先验信息可用的热启动系统。

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.