  In this paper, we introduce a neural-augmented decoder for Turbo codes called
TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP
algorithm but has much better reliability than the max-log-MAP baseline and
performs close to the MAP algorithm. We show that TINYTURBO exhibits strong
robustness on a variety of practical channels of interest, such as EPA and EVA
channels, which are included in the LTE standards. We also show that TINYTURBO
strongly generalizes across different rate, blocklengths, and trellises. We
verify the reliability and efficiency of TINYTURBO via over-the-air
experiments.
