  Massive MIMO systems have made significant progress in increasing spectral
and energy efficiency over traditional MIMO systems by exploiting large antenna
arrays. 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. Despite the large number of unknown channel
coefficients for massive SIMO systems, we improve an algorithm to achieve the
exact ML non-coherent data detection with a low expected complexity. We show
that the expected computational complexity of this algorithm is linear in the
number of receive antennas and polynomial in channel coherence time. Simulation
results show the performance gain of the optimal non-coherent data detection
with a low computational complexity.
