Molina-Tenorio Yanqueleth, Prieto-Guerrero Alfonso, Aguilar-Gonzalez Rafael, Lopez-Benitez Miguel
Information Science and Technology Ph.D., Metropolitan Autonomous University, Mexico City 09360, Mexico.
Electrical Engineering Department, Metropolitan Autonomous University, Mexico City 09360, Mexico.
Sensors (Basel). 2023 May 30;23(11):5209. doi: 10.3390/s23115209.
Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.
认知无线电网络（CRNs）需要高容量和高精度来检测感知频谱中授权用户或主用户（PUs）的存在。此外，它们必须正确定位频谱机会（空洞），以便非授权用户或次用户（SUs）能够使用。在本研究中，提出了一种用于实时监测多频段频谱的集中式认知无线电网络，并通过诸如软件定义无线电（SDRs）等通用通信设备在实际无线通信环境中实现。在本地，每个次用户使用基于样本熵的监测技术来确定频谱占用情况。检测到的主用户的确定特征（功率、带宽和中心频率）被上传到数据库。然后由一个中央实体处理上传的数据。这项工作的目标是通过构建无线电环境地图（REMs）来确定特定区域内感知频谱中的主用户数量、其载波频率、带宽以及频谱间隙。为此，我们比较了中央实体执行的经典数字信号处理方法和神经网络的结果。结果表明，两种提出的认知网络（一种与使用典型信号处理的中央实体协同工作，另一种使用神经网络执行）都能准确地定位主用户，并向次用户提供传输信息，避免隐藏终端问题。然而，性能最佳的认知无线电网络是使用神经网络在载波频率和带宽上准确检测主用户的网络。
