Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznań, Poland.
Sensors (Basel). 2021 Dec 28;22(1):198. doi: 10.3390/s22010198.
Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users' (primary users' (PUs')) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.
频谱感知（SS）是寻找频谱共享新机会的重要工具。没有许可证就无法无障碍传输的用户，即次用户（SU），需要使用 SS 来检测和使用频谱，而不会干扰有许可证的用户（主用户（PU））的传输。深度学习（DL）已被证明是一种很好的智能 SS 算法选择，它在决策过程中考虑了无线电环境因素。然而，SU 无法收集所需的数据并训练复杂的 DL 模型。在本文中，我们提出使用联邦学习（FL）算法来分布数据收集和模型训练过程到多个设备上。所提出的方法通过其平均信噪比（SNR）将 FL 设备分为组，并在迭代过程中为每个组创建一个共同的 DL 模型。结果表明，通过 FL 算法获得的检测精度与专门针对给定平均 SNR 值的 PU 信号进行频谱检测的卷积神经网络（CNN）等几种 DL 模型获得的检测精度相似。同时，还实现了网络中 SS 过程简化的主要目标。
