  Many channel decoders rely on parallel decoding attempts to achieve good
performance with acceptable latency. However, most of the time fewer attempts
than the foreseen maximum are sufficient for successful decoding.
Input-distribution-aware (IDA) decoding allows to determine the parallelism of
polar code list decoders by observing the distribution of channel information.
In this work, IDA decoding is proven to be effective with different codes and
decoding algorithms as well. Two techniques, M-IDA and MD-IDA, are proposed:
they exploit the sampling of the input distribution inherent to particular
decoding algorithms to perform low-cost IDA decoding. Simulation results on the
decoding of BCH codes via the Chase and ORBGRAND algorithms show that they
perform at least as well as the original IDA decoding, allowing to reduce
run-time complexity down to 17% and 67\% with minimal error correction
degradation.
