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

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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.