  Massive MIMO systems can greatly increase spectral and energy efficiency over
traditional MIMO systems by exploiting large antenna arrays. However,
increasing the number of antennas at the base station (BS) makes the uplink
non-coherent data detection very challenging in massive MIMO systems. In this
paper we consider the joint maximum likelihood (ML) channel estimation and data
detection problem for massive SIMO (single input multiple output) wireless
systems, which is a special case of wireless systems with large antenna arrays.
We propose exact ML non-coherent data detection algorithms for both
constant-modulus and nonconstant-modulus constellations, with a low expected
complexity. Despite the large number of unknown channel coefficients for
massive SIMO systems, we show that the expected computational complexity of
these algorithms is linear in the number of receive antennas and polynomial in
channel coherence time. Simulation results show the performance gains (up to 5
dB improvement) of the optimal non-coherent data detection with a low
computational complexity.
