  \textit{Why does the literature consider the channel-state-information (CSI)
as a 2/3-D image? What are the pros-and-cons of this consideration for
accuracy-complexity trade-off?} Next generations of wireless communications
require innumerable disciplines according to which a low-latency, low-traffic,
high-throughput, high spectral-efficiency and low energy-consumption are
guaranteed. Towards this end, the principle of massive multi-input multi-output
(MaMIMO) is emerging which is conveniently deployed for millimeter wave
(mmWave) bands. However, practical and realistic MaMIMO transceivers suffer
from a huge range of challenging bottlenecks in design the majority of which
belong to the issue of channel-estimation. Channel modeling and prediction in
MaMIMO particularly suffer from computational complexity due to a high number
of antenna sets and supported users. This complexity lies dominantly upon the
feedback-overhead which even degrades the pilot-data trade-off in the uplink
(UL)/downlink (DL) design. This comprehensive survey studies the novel
deep-learning (DLg) driven techniques recently proposed in the literature which
tackle the challenges discussed-above - which is for the first time. In
addition, we consequently propose 7 open trends e.g. in the context of the lack
of Q-learning in MaMIMO detection - for which we talk about a possible solution
to the saddle-point in the 2-D pilot-data axis for a \textit{Stackelberg game}
based scenario.
