Neural Network-based Forecasting of Decodability for Early ARQ

Forecasting the decodability of a received packet of a given decoder is a hard task as many State-of-the-Art (SoTA) decoders are of high complexity and not easy to analyse in an analytical fashion. Gathering this forecast on the other hand would enable to save computational complexity and latency as a decoder execution can be saved if it is unlikely that the received packet is decoded correctly. On top, we can provide early feedback for Automatic Repeat Request (ARQ) schemes before actually running the decoding chain. Guided by this motivation, several approaches of classifying the received packet before the actual decoding process have been discussed. We propose to use neural networks (NN) in the context of forecasting received packets for a given receiver chain. We evaluate the performance of the NN by evaluating different performance metrics and perform an efficiency analysis of ARQ.