  Massive MIMO OFDM waveforms help support a large number of users in the same
time-frequency resource and also provide significant array gain for uplink
reception in cellular systems. However, channel estimation in such large
antenna systems can be tricky as pilot assignment for multiple users becomes
more challenging with increasing number of users. Additionally, the pilot
overhead especially for wideband rapidly changing channels can diminish the
system throughput quite significantly. In this paper, we propose an iterative
matrix decomposition algorithm for the blind demodulation of massive MIMO OFDM
signals without using any pilots. This new decomposition technique provides
estimates of both the user symbols and the user channel in the frequency domain
simultaneously (to a scaling factor) without any pilots. We discuss methods for
finding the appropriate initial points for the algorithm that ensure its
convergence in different types of wireless channels. We also propose new
methods for resolving the scaling factor in the estimated signal that do not
increase pilot overhead. We show how the method can be adapted to both
single-user and multi-user systems. Simulation results demonstrate that the
lack of pilots does not affect the error performance of the proposed algorithm
when compared to the conventional pilot-based channel estimation and
equalization methods across a wide range of channels for both single and
multi-user cases. We also demonstrate techniques to reduce the complexity of
the estimation algorithm over multiple OFDM symbols in a 5G MIMO system by
leveraging the temporal correlations in the channel.
