Design of Communication Systems using Deep Learning: A Variational Inference Perspective

An approach to design end to end communication system using deep learning leveraging the generative modeling capabilities of autoencoders is presented. The system models are designed using Deep Neural Networks (DNNs) and the objective function for optimizing these models are derived using variational inference. Through experimental validation, the proposed method is shown to produce better models consistently in terms of error rate performance as well as constellation packing density as compared to previous works.