Skip to main content
Open Access Publications from the University of California


UCLA Electronic Theses and Dissertations bannerUCLA

Applying Distributed Learning of Deep Neural Networks to Improve Their Classification Accuracy on Radio-Frequency Datasets


This thesis aims to improve on the current classification capabilities of deep neural networks on two types of radio-frequency data: radar and OFDM packets. In radar, applying neu- ral networks to Automatic Target Recognition problems is a well-developed field, especially using the MSTAR database. However, existing state-of-the-art methods require precise pre- conditioning of radar data and are unsuited to applications with a large number of radar target classes. Therefore, we asked whether distributed learning can increase the generaliz- ability and scalability of neural networks in these tasks. To test this, we applied distributed learning via Multi-Stage Training and a new network architecture, the Convolutional Multi- Stage Network, to provide a scalable, generalized treatment of radar data for more practical applications. This method was shown to outperform traditional neural network architectures on a new radar dataset. A similar approach was applied to the OFDM data with the goal of identifying specific radio-frequency transmitters for network security purposes. The task of identifying OFDM packet transmitters has previously been performed successfully, though with precise data collection methods. Data collection methods on a live network will likely include imperfect recording times, so we sought to improve network robustness to time- shifted OFDM packets. It was shown that the Convolutional Multi-Stage Network improved robustness to time-shifting of the radio-frequency data over the Multi-Stage Network, which was the previous-best method. Simple preconditioning of the data using variations of the dis- crete wavelet transform further improved robustness to time-shifting of the radio-frequency data using both network architectures. These results are significant, as they provide a new avenue for applying neural networks to radio-frequency in difficult, real-world applications.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View