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

主用户网络通过使用在许可频段上运行的多个地理上分离的发射机来提供大面积的无线覆盖。另一方面，认知无线电是空闲频谱的机会主义用户。为了与附近的发射机共存，它们需要侦听频谱，即检测主用户的传输，并避免对它们造成干扰。该项目专注于在存在蜂窝、具有各向异性天线或采用频率重用的主要网络的情况下的动态频谱接入，旨在显著增加认知无线电可以使用的频谱范围。通过高层无线电场景分析，我们的目标是通过提高检测到的空间分辨率和频谱空洞的利用率来最大化认知无线电网络的吞吐量。频谱和地理分布的增加使大规模认知无线电网络成为实施智能电网、环境网络和交通传感器的可行候选。为了防御目的，学习的主网络拓扑提供了关于通常部署的通信网络的前所未有的信息。本项目的研究基于地理上大规模认知无线电网络的频谱空洞识别和主网络活动分类的协作算法的开发。所提出的方法的一个新奇之处在于，它们不依赖于信道传播模型或无线电位置的知识。算法的这种盲目性允许更多样化的应用。接收到的信号中的相关性将被用来区分主要发射机和了解它们的足迹形状，而不是地理上的邻近。对于两个或多个发射机的足迹重叠的情况，提出了基于消息传递的协作频谱感知方法，用于联合估计多个发射机的频谱占用。学习到的占用空间和检测到的频谱占用情况将用于分析主要用户随时间的活动。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，了解他们的通信量统计将能够对他们的信道接入方法和协议进行分类。

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.