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

合作研究：EARS：基于大规模统计学习的频谱感知和认知网络

• 批准号: 1659962
• 负责人: Tao Shu
• 金额: $ 13.74万
• 依托单位: Auburn University
• 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: 2016-10-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659962&HistoricalAwards=false
• 关键词: Collaborative; Research; EARS; Large; Scale

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.

随着认知无线电（CR）研究向大地理区域的多跳和复杂系统的发展，频谱利用率的提高应被推广到充分利用时间、频率和空间三个维度（3D）的频谱使用多样性，并将重点放在空间维度上。因此，本项目主要有以下三个研究目标。第一个是利用大数据统计学习的最新进展来开发有效的3D频谱感知方案，其中采用分层方法来开发新的有限位和单位学习技术，以有效地探索三维空间的相关性结构，还开发了先进的分布式方法。第二个是开发基于3D频谱感测的大规模CR联网中的两个关键构建块：1）新颖的多尺度路由方案，以提高整体频谱利用率，重点在于利用空间维度中的分层频谱使用相关结构;以及2）可靠的分层公共控制信道识别方案。最后一个研究目标是通过密集的模拟和概念验证测试平台来验证所提出的传感和联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，结合联合收割机的统计学习，信号处理和无线网络的方法，与核心建立在频谱使用统计和CR网络方法的分层处理。该项目为大规模频谱感知和认知网络提供了理论和算法。通过一个连贯的教育计划，研究结果将被纳入课程，并通过期刊论文和会议演讲向社区传播。

项目成果

Multi-Operator Network Sharing for Massive IoT

大规模物联网的多运营商网络共享

• DOI: 10.1109/mcom.2019.1800272
• 发表时间: 2019
• 期刊: IEEE Communications Magazine
• 影响因子: 11.2
• 作者: Xiao, Yong;Krunz, Marwan;Shu, Tao
• 通讯作者: Shu, Tao

Strategic Network Infrastructure Sharing through Backup Reservation in a Competitive Environment

• DOI: 10.1109/sahcn.2019.8824849
• 发表时间: 2019-06
• 期刊: 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
• 作者: Jing Hou;Li Sun;Tao Shu;Yong Xiao;M. Krunz