Abstract
Intelligent capabilities are of utmost importance in future wireless communication systems. For optimum resource utilization, wireless communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is imperative for practitioners to select the right parameters for building robust data-driven learning models as well as use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of deep learning models against the performance of other machine learning methods for wireless communication systems. We explore the different wireless communication scenarios in which deep learning can be used given Radio Frequency (RF) data, and evaluate its performance in various scenarios. Furthermore, we express it as a distribution alignment problem in which deep learning models do not perform well when learning from RF data of a particular distribution and evaluating on RF data from a different distribution. We also discuss our results in the light of how signal quality affects deep learning model leveraging on the knowledge from computer vision domain. The effect of Signal-to-Noise Ratio (SNR) selection for training on the model performance as it relates to practical implementation of deep learning in communications systems is also discussed. From our analysis, we conclude that the design and use of RF spectrum learning must be tailored to each specific scenario being considered in practice.
Highlights
There has been a lot of interest in the development of artificial intelligence for wireless communication systems using radio frequency (RF) data
We identified three scenarios where deep learning can be used for Radio Frequency (RF) spectrum learning, investigated the deep learning training strategies for these scenarios and came up with performance evaluation strategies for deep learning models in practical RF learning, focusing on the effect of signal-tonoise ratio (SNR)
OF DEEP RF LEARNING Supervised learning using deep neural network for RF dataset is referred to as deep RF learning. It is expressed as a function f () that models the mapping from the in-phase component (I) and the quadrature (Q) component of the RF front-end denoted as the input data X to the class label Y
Summary
There has been a lot of interest in the development of artificial intelligence for wireless communication systems using radio frequency (RF) data. It becomes important to explore how to develop robust deep learning models that generalize well on unseen data for different wireless communications scenarios in practice using RF data. We evaluate the effect of signal-to-noise ratio in the development of deep learning models using radio frequency data This practical study is motivated by the need to develop DL models that are robust and will generalize well given the stochastic nature of wireless communication systems. It can be deduced that to achieve robust and practicable RF learning in different scenarios, there must be unique problem formulation with special consideration for SNR selection and performance evaluation methods Factors such as SNR step-size for training as well as testing dataset selection must be carefully studied in all cases for RF learning.
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