Practical Radio Frequency Learning for Future Wireless Communication Systems

Abstract

In future wireless systems, intelligent capabilities are of utmost importance. To efficiently utilize resources, communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is critical for practitioners to select the right parameters in building learning models, use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of some deep learning models compared to other machine learning methods, explore the different scenarios in which deep learning can be used for radio frequency (RF) monitoring, and evaluate performance in the various scenarios. Our work looks at the best practices and procedures for developing intelligent RF Learning. Specifically, we analysed over-the-air RF dataset collected from a USRP-based testbed to identify the number of interfering devices as a case study. From the obtained results, we discuss how Signal-to-Noise Ratio (SNR) selection for training affects the model performance as it relates to practical implementation of Deep Learning in communications systems.