  Perfect channel estimation is very hard, time/ power consuming, and
expensive; so it is not preferred (e.g. in mobile) communication systems. This
paper seeks for new, cheap, low complexity, deep learning based solution.
Several new combinations of deep learning and conventional structures (in
different parts such as constellation shaper, channel estimator, and detector)
are presented investigated, and compared over all atmospheric turbulence
regimes from weak to strong (considering Gamma-Gamma atmospheric turbulence
model). Results indicate that deep learning could provide close enough
performance to the perfect channel estimation scheme, and it is immune to the
atmospheric turbulence variation. The proposed deep learning based solutions
are low cost, low complexity, with favorable performance. Accordingly, they are
recommended for channel estimation in mobile communication systems. Because
these system should deliver favorable, and cheap services to the costumers,
which use a small mobile as transceiver that needs to be cheap, low complexity
and low power consuming.
