Automated deep learning-based wide-band receiver

We propose a modular and full-fledged physical layer receiver design for Orthogonal Frequency Division Multiplexing (OFDM) wireless systems leveraging the advances of deep neural networks (DNN). We adopt a detailed modular design that includes proper and utmost domain knowledge in each element and train it using data collected both via simulations as well as over-the-air and emulated wireless transmissions. We then unify all the modules into an end-to-end automated deep learning-based wide-band receiver and fine-tune it to further improve its accuracy. Our combined pipeline analysis exhibits superior performance by showing bit error rate values up to 8 times lower if compared to the traditional approaches for wireless communications.