  5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the
important applications of 5G wireless network which requires minimum latency
and higher reliability to support mission-critical applications. Therefore,
lossless reliable communication with a high data rate is the key requirement in
modern wireless communication systems. These all factors highly depend upon
channel conditions. In this work, a channel model is proposed for
air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime
unmanned aerial vehicle (UAV) communication. Firstly, we will present the
formulated channel estimation method which directly aims to adopt channel state
information (CSI) of mmWave from the channel model inculcated by UAV operating
within the Long Short Term Memory (LSTM)-Distributed Conditional generative
adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction.
Secondly, to enhance the applications for the proposed trained channel model
for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where
each one will learn mmWave CSI for all the distributions. Lastly, we have
categorized the most favorable LSTM-DCGAN training method and emanated certain
conditions for our UAV network to increase the channel model learning rate.
Simulation results have shown that the proposed LSTM-DCGAN based network is
vigorous to the error generated through local training. A detailed comparison
has been done with the other available state-of-the-art CGAN network
architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with
CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN.
Simulation results have shown that the proposed LSTM-DCGAN structure
demonstrates higher accuracy during the learning process and attained more data
rate for downlink transmission as compared to the previous state of artworks.
