  The traditional communication model based on chain of multiple independent
processing blocks is constraint to efficiency and introduces artificial
barriers. Thus, each individually optimized block does not guarantee end-to-end
performance of the system. Recently, end-to-end learning of communications
systems through machine learning (ML) have been proposed to optimize the system
metrics jointly over all components. These methods show performance
improvements but has a limitation that it requires a differentiable channel
model. In this study, we have summarized the existing approaches that
alleviates this problem. We believe that this study will provide better
understanding of the topic and an insight into future research in this field.

Machine Learning;Networking and Internet Architecture