Kułacz Łukasz, Kliks Adrian
Institute of Radiocommunications, Poznan University of Technology, 60-965 Poznan, Poland.
Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Lulea, Sweden.
Sensors (Basel). 2023 Jul 16;23(14):6436. doi: 10.3390/s23146436.
Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection's effectiveness. Given the recent trend of using federated learning, we present a federated learning algorithm for distributed spectrum occupancy detection. This idea improves overall spectrum-detection effectiveness, simultaneously keeping a low amount of data that needs to be exchanged between sensors. The proposed solution achieves a higher accuracy score than separate and autonomous models used without federated learning. Additionally, the proposed solution shows some sort of resistance to faulty sensors encountered in the system. The results of the work presented in the article are based on actual signal samples collected in the laboratory. The proposed algorithm is effective (in terms of spectrum occupancy detection and amount of exchanged data), especially in the context of a set of sensors in which there are faulty sensors.
动态接入频谱对于无线电通信及其有限的频谱资源至关重要。动态频谱接入系统的关键要素通常是有效的频谱占用检测。在许多情况下，机器学习算法可提高这种检测的有效性。鉴于最近使用联邦学习的趋势，我们提出了一种用于分布式频谱占用检测的联邦学习算法。这一想法提高了整体频谱检测的有效性，同时保持传感器之间需要交换的少量数据。所提出的解决方案比未使用联邦学习的单独自主模型具有更高的准确率得分。此外，所提出的解决方案对系统中遇到的故障传感器具有一定的抗性。本文所展示的工作结果基于在实验室收集的实际信号样本。所提出的算法是有效的（在频谱占用检测和交换数据量方面），特别是在存在故障传感器的一组传感器的情况下。
