  Massive MIMO is one of the main features of 5G mobile radio systems. However,
it often leads to high cost, size and power consumption. To overcome these
issues, the use of constrained radio frequency (RF) frontends has been
proposed, as well as novel precoders, e.g., a multi-antenna, greedy, iterative
and quantized precoding algorithm (MAGIQ). Nevertheless, the best performance
of MAGIQ assumes accurate channel knowledge per antenna element, for example,
from uplink sounding reference signals. In this context, we propose an
efficient uplink channel estimator by applying machine learning (ML)
algorithms. In a first step a conditional generative adversarial network (cGAN)
predicts the radio channels from a limited set of full resolution RF chains to
the rest of the low resolution RF chain antenna elements. A long-short term
memory (LSTM) neural network extracts further phase information from the low
resolution RF chain antenna elements. Our results indicate that our proposed
approach is competitive with traditional Unitary tensor-ESPRIT in scenarios
with various closely spaced multipath components (MPCs).
