Separating Interferers from Multiple Users in Interference Aware Guessing Random Additive Noise Decoding Aided Macrosymbol
Kathleen Yang, Muriel Médard, Ken R. Duffy · 2023
We differentiate between multiuser effects and interference in multiuser detection (MUD), and propose an approach that handles both of these. Users that are in a non-orthogonal multiuser access (NOMA) group employ symbol level multiple access channel codes, using the previously introduced method, guessing random additive noise decoding aided macrosymbol (GRAND-AM). Interferers outside the NOMA group, on the other hand, do not employ this symbol level multiple access coding technique. However, when the modulations and channel gains of the interferers are known at the receiver, we show how to modify the MUD in GRAND-AM in order to mitigate the effect of the interferers. We show that this interference aware GRAND-AM can greatly outperform MUD where the interference is assumed to be additive white Gaussian noise and subsumed into the signal and interference noise ratio.