3 research outputs found
Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks
The growing number of devices using the wireless spectrum makes it important
to find ways to minimize interference and optimize the use of the spectrum.
Deep learning models, such as convolutional neural networks (CNNs), have been
widely utilized to identify, classify, or mitigate interference due to their
ability to learn from the data directly. However, there have been limited
research on the complexity of such deep learning models. The major focus of
deep learning-based wireless classification literature has been on improving
classification accuracy, often at the expense of model complexity. This may not
be practical for many wireless devices, such as, internet of things (IoT)
devices, which usually have very limited computational resources and cannot
handle very complex models. Thus, it becomes important to account for model
complexity when designing deep learning-based models for interference
classification. To address this, we conduct an analysis of CNN based wireless
classification that explores the trade-off amongst dataset size, CNN model
complexity, and classification accuracy under various levels of classification
difficulty: namely, interference classification, heterogeneous transmitter
classification, and homogeneous transmitter classification. Our study, based on
three wireless datasets, shows that a simpler CNN model with fewer parameters
can perform just as well as a more complex model, providing important insights
into the use of CNNs in computationally constrained applications.Comment: 6 pages, 7 figures, 3 table