Low-complexity Decoding is Asymptotically Optimal in the SIMO MAC
  A single input multiple output (SIMO) multiple access channel, with a large number of transmitters sending symbols from a constellation to the receiver of a multi-antenna base station, is considered. The fundamental limits of joint decoding of the signals from all the users using a low complexity convex relaxation of the maximum likelihood decoder (ML, constellation search) is investigated. It has been shown that in a rich scattering environment, and in the asymptotic limit of a large number of transmitters, reliable communication is possible even without employing coding at the transmitters. This holds even when the number of receiver antennas per transmitter is arbitrarily small, with scaling behaviour arbitrarily close to what is achievable with coding. Thus, the diversity of a large system not only makes the scaling law for coded systems similar to that of uncoded systems, but, as we show, also allows efficient decoders to realize close to the optimal performance of maximum-likelihood decoding. However, while there is no performance loss relative to the scaling laws of the optimal decoder, our proposed low-complexity decoder exhibits a loss of the exponential or near-exponential rates of decay of error probability relative to the optimal ML decoder. 
Mainak Chowdhury and Andrea Goldsmith
cs.IT math.IT