Collaborative Research: EARS: Large-Scale Statistical Learning based Spectrum Sensing and Cognitive Networking

随着认知无线电研究向大范围的多跳和复杂系统发展，频谱利用率的提高应该得到推广，以充分利用时间、频率和空间三个维度(3D)的频谱使用多样性，并将重点放在未被探索的空间维度上。因此，本项目主要围绕以下三个研究目标展开。第一种是利用大数据统计学习的最新进展来开发高效的3D频谱感知方案，其中采用分层的方法来开发新的有限比特和单比特学习技术来有效地探索三维相关性结构，并开发了先进的分布式方法。二是开发基于3D频谱感知的大规模认知无线电组网的两个关键模块：1)一种新的多尺度路由方案，以提高整体频谱利用率，重点是在空间维度上利用分层的频谱使用相关结构；以及2)一种可靠的分层公共控制信道识别方案。最后的研究目标是通过密集的模拟和概念验证试验台来验证所提出的传感和组网方案中的一些关键方面。在整个项目中，采取了跨学科的方法，将统计学习、信号处理和无线联网的方法结合在一起，核心建立在频谱使用统计和CR联网方法的分层处理基础上。该项目为大规模频谱感知和认知组网提供了理论和算法。通过一个连贯的教育计划，研究成果将被纳入课程，并通过期刊论文和会议报告向社会传播。

As cognitive radio (CR) research advances to multihop and complex systems over large geographic regions, the spectrum utilization enhancement should be generalized to fully exploit the spectrum usage diversity in three dimensions (3D): time, frequency, and space, with new emphasis on the under-explored spatial dimension. Accordingly, this project focuses on the following three research objectives. The first one is to utilize the recent advancements in statistical learning over big data to develop efficient 3D spectrum sensing schemes, where a hierarchical approach is taken in developing novel finite-bit and single-bit learning techniques to efficiently explore the correlation structure across the three dimensions, with an advanced distributed approach also developed. The second one is to develop two key building blocks in large-scale CR networking based on the 3D spectrum sensing: 1) a novel multi-scale routing scheme to enhance the overall spectrum utilization, with a focus on exploiting the layered spectrum usage correlation structure in the spatial dimension; and 2) a reliable hierarchical common control channel identification scheme. The last research objective is to validate some key aspects in the proposed sensing and networking schemes via both intensive simulations and a concept-proving testbed. Throughout the project, an interdisciplinary approach is taken to combine the methods of statistical learning, signal processing, and wireless networking, with the core built upon the hierarchical treatment of both spectrum usage statistics and CR networking methodologies. The project provides both theories and algorithms for large-scale spectrum sensing and cognitive networking. Through a coherent education plan, the research findings will be incorporated into courses, and disseminated to the community via journal papers and conference presentations.