NeTS: Small: Dynamic Spectrum Access by Learning Primary Network Topology

NeTS：小型：通过学习主网络拓扑进行动态频谱访问

• 批准号: 1527026
• 负责人: Danijela Cabric
• 金额: $ 20万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527026&HistoricalAwards=false
• 关键词: NeTS; Small; Dynamic; Spectrum; Access

Primary user networks provide wireless coverage over a large area by using multiple geographically separated transmitters operating on licensed frequency bands. On the other hand, cognitive radios are opportunistic users of unoccupied spectrum. In order to coexist with the transmitters in its vicinity, they need to sense the spectrum, i.e., detect the primary users' transmissions, and avoid causing them interference. This project focuses on the dynamic spectrum access in the presence of primary networks that are cellular, have anisotropic antennas, or employ frequency reuse, and aims to significantly increase the range of spectrum that cognitive radios can use. Through higher-layer radio scene analysis we aim to maximize the cognitive radio network throughput by increasing the detected spatial resolution and utilization of spectrum holes. This combination of increased spectrum and geographical spread makes large-scale cognitive radio networks a viable candidate for implementing smart grids, environmental networks, and traffic sensors. For defense purposes, the learned primary network topology provides unprecedented information about commonly deployed communication networks.This project research is based on development of cooperative algorithms for the identification of spectrum holes and classification of primary network activity by geographically large-scale cognitive radio networks. A novelty of the proposed methods is that they will not rely on knowledge of the channel propagation models or the location of the radios. This blind nature of the algorithms allows for more diverse applications. Instead of geographical vicinity, correlations in the received signals will be used for distinguishing between primary transmitters and learning the shape of their footprints. For the case that footprints of two or more transmitters overlap, message passing based cooperative spectrum sensing methods will be formulated for the joint estimation of the multiple transmitters spectrum occupancy. The learned footprint and the detected spectrum occupancy will be used to analyze the primary user activity over time. Primary users that are part of the same infrastructure-based primary networks will be identified. Further, learning their traffic statistics will enable the classification of their channel access methods and protocols.

主用户网络通过使用在许可频带上操作的多个地理上分离的发射机来提供大区域上的无线覆盖。另一方面，认知无线电是空闲频谱的机会主义用户。为了与其附近的发射机共存，它们需要感测频谱，即，检测主用户的传输，避免对主用户造成干扰。该项目的重点是动态频谱接入的主要网络是蜂窝，具有各向异性天线，或采用频率复用的存在下，并旨在显着增加频谱的范围，认知无线电可以使用。通过更高层的无线电场景分析，我们的目标是通过增加检测到的空间分辨率和频谱空洞的利用率，最大限度地提高认知无线电网络的吞吐量。这种增加的频谱和地理分布的组合使得大规模认知无线电网络成为实现智能电网，环境网络和交通传感器的可行候选者。为了防御的目的，学习的主要网络拓扑结构提供了前所未有的信息，通常部署的communicationnetwork.This项目研究的基础上开发的合作算法的识别频谱空洞和分类的主要网络活动的地理大规模认知无线电网络。所提出的方法的新奇在于，它们将不依赖于信道传播模型或无线电的位置的知识。算法的这种盲目性质允许更多样化的应用。代替地理邻近，接收信号中的相关性将用于区分主发射器并学习其足迹的形状。对于两个或多个发射机的足迹重叠的情况下，基于消息传递的协作频谱感知方法将被制定用于多个发射机频谱占用的联合估计。学习的足迹和检测到的频谱占用将用于分析主用户随时间的活动。将确定属于相同的基于基础设施的主要网络的主要用户。此外，学习它们的业务统计将使得能够对其信道接入方法和协议进行分类。