WiFiUS: Collaborative Research: Sequential Inference and Learning for Agile Spectrum Use

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A key imperative to expanding future wireless services is to overcome the spectral crunch. At present, static allocation and rigid regulation lead to underutilization of available spectral resources. Flexible spectrum use aims at exploiting under-utilized spectrum. Available spectrum opportunities may be non-contiguous, scattered over a large bandwidth, and are available locally and for a limited period of time due to the highly dynamic nature of wireless transmissions. This fuels the need to understand how to discover, assess and utilize the time-frequency-location varying spectral resources efficiently and with minimal delay. Moreover, it is critical to access identified idle spectrum in an agile manner.This project will design sequential inference and learning algorithms for agile spectrum access when the state of the spectrum varies rapidly. The key advantage of sequential algorithms, as compared to block-wise algorithms, is that they typically lead to significantly reduced decision delays. The overarching goal of this project is to design sequential inference and learning algorithms for agile spectrum utilization. In particular, this project will employ advanced sequential inference and learning methods for the following three interconnected yet increasingly sophisticated and demanding tasks: 1) to employ sequential reinforcement learning and sequential inference algorithms to design sensing policies for rapid spectrum opportunities discovery; 2) to design sequential algorithms for fast and accurate spectrum quality assessment; and 3) to build, maintain and exploit an interference map of the area where our network operates and represent it as a spatial potential field. The proposed research is expected to make substantial contributions to both applications and theory. On the application level, the proposed research has the potential to substantially improve spectral efficiency by introducing novel tools from sequential analysis, machine learning and statistical inference for the design of spectrum discovery, assessment and exploitation policies. On the theoretical level, the proposed project will advance the state of the art in sequential analysis and contribute new approaches to the general methodological base for optimal stopping, control and machine learning problems. Furthermore, new methods and theory of modeling and exploiting knowledge of interference using spatial potential fields, sequential statistics and advanced propagation modeling will be developed.