  Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding
technique for short-length and high-rate channel codes. GRAND tries to guess
the channel noise by generating test error patterns (TEPs), and the sequence of
the TEPs is the main difference between different GRAND variants. In this work,
we extend the application of GRAND to multipath frequency non-selective
Rayleigh fading communication channels, and we refer to this GRAND variant as
Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading
conditions of the underlying communication channel, outperforming traditional
channel code decoders in scenarios with $L$ spatial diversity branches as well
as scenarios with no diversity. Numerical simulation results show that the
Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for
decoding BCH code $(127,106)$ and BCH code $(127,113)$ by $\mathbf{0.5\sim6.5}$
dB at a target FER of $10^{-7}$. Similarly, Fading-GRAND outperforms GRANDAB,
the hard-input variation of GRAND, by $0.2\sim8$ dB at a target FER of
$10^{-7}$ with CRC $(128,104)$ code and RLC $(128,104)$. Furthermore the
average complexity of Fading-GRAND, at $\frac{E_b}{N_0}$ corresponding to
target FER of $10^{-7}$, is $\frac{1}{2}\times\sim \frac{1}{46}\times$ the
complexity of GRANDAB.
