Abstract:Guessing random additive noise decoding (GRAND) is a universal maximum-likelihood decoder that recovers codewords by guessing rank-ordered putative noise sequences and inverting their effect until one or more valid code-words are obtained. This work explores how GRAND can leverage additivenoise statistics and channel-state information in fading channels. Instead of computing per-bit reliability information in detectors and passing this information to the decoder, we propose leveraging the colored noise statist… Show more
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