  Guessing random additive noise decoding (GRAND) is a noise-centric decoding
method, which is suitable for ultra-reliable low-latency communications, as it
supports high-rate error correction codes that generate short-length codewords.
GRAND estimates transmitted codewords by guessing the error patterns that
altered them during transmission. The guessing process requires the generation
and testing of error patterns that are arranged in increasing order of Hamming
weight. This approach is fitting for binary transmission over additive white
Gaussian noise channels. This letter considers transmission of coded and
modulated data over flat fading channels and proposes a variant of GRAND, which
leverages information on the modulation scheme and the fading channel. In the
core of the proposed variant, referred to as symbol-level GRAND, is an
analytical expression that computes the probability of occurrence of an error
pattern and determines the order with which error patterns are tested.
Simulation results demonstrate that symbol-level GRAND produces estimates of
the transmitted codewords notably faster than the original GRAND at the cost of
a small increase in memory requirements.
