Nonorthogonal Multiple Access With Guessing Random Additive Noise Decoding-Aided Macrosymbol (GRAND-AM)

We propose guessing random additive noise decoding-aided macrosymbols (GRAND-AMs) as a nonorthogonal multiple access (NOMA) method that can detect, error correct, and decode multiple users with imperfect channel estimation, asynchronous transmission, and interference, which are all topics of concern for Internet of Things. GRAND-AM is a NOMA method that uses both joint multiuser detection and joint error correction decoding to handle multiple access interference (MAI). For the joint multiuser detector, we introduce the concept of a macrosymbol, which is constructed from the combination of all user symbols. For the error correction decoding component, we introduce multiple access channel (MAC) codes, which are codes that are used to split the channel rate between users and correct errors due to MAI. In this scheme, each user has their information bits encoded with independent MAC codes. We use a soft detection variant of GRAND, an efficient and practical decoding method that inverts noise effect sequences from a sequence of symbols to arrive at a codeword, to correct a sequence of macrosymbols, ensuring that all user codebooks are simultaneously satisfied. The joint detection and decoding of GRAND-AM can outperform time division multiple access (TDMA) by 10 dB with perfect channel estimation, and by 6 dB with imperfect channel estimation. Considering a more complete communication chain, when additional forward error correction is used along with the MAC code, the GRAND-AM method performs similarly to a same rate low-density parity-check-coded TDMA system.