  In this letter, we introduce a new syndrome-based decoder where a deep neural
network (DNN) estimates the error pattern from the reliability and syndrome of
the received vector. The proposed algorithm works by iteratively selecting the
most confident positions to be the error bits of the error pattern, updating
the vector received when a new position of the error pattern is selected.
Simulation results for the (63,45) and (63,36) BCH codes show that the proposed
approach outperforms existing neural network decoders. In addition, the new
decoder is flexible in that it can be applied on top of any existing
syndrome-based DNN decoder without retraining.
