Can Basic ML Techniques Illuminate Rateless Erasure Codes?

Abstract

The recently developed rateless erasure codes are a near-optimal channel coding technique that guaranteeslow overhead and fast decoding. The underlying theory, and current implementations, of thesecodes assume that a network transmitter encodes according to a pre-specified probability distribution.In this report, we use basic Machine Learning techniques to try to understand what happens when thisassumption is false. We train several classes of models using certain features that describe the empiricaldistribution realized at a network receiver, and we investigate whether these models can  learn topredict whether a given encoding will require extra overhead. Our results are mixed.