Conclusion and Future Work
The primary focus of the presented work in this thesis is to address the hybrid transceiver design for next
generation of mobile cellular networks. Keeping this perspective in view, an endeavor was made to propose
the low-...
Conclusion and Future Work
The primary focus of the presented work in this thesis is to address the hybrid transceiver design for next
generation of mobile cellular networks. Keeping this perspective in view, an endeavor was made to propose
the low-complexity solution of hybrid precoding design for a single-user mmWave MIMO system by taking
into account the frequency selective channels. The concept of hybrid beamforming was applied on different
relay-assisted mmWave MIMO communication networks to extend the transmission range, network
coverage and the enhancement of spectral efficiency. The first hybrid beamforming design was proposed
for multi-user MIMO relay system under frequency selective channels. The second hybrid precoding design
was proposed in an attempt to combine the hybrid processing and relay selection strategy to maximize the
performance of system, when channel conditions are not favorable for reliable data transmission. The third
relay-assisted hybrid beamforming was designed by considering a single-user multi-relay MIMO system
under wideband assumption. These relay based architectures enable the mmWave MIMO network efficient
and reliable in outdoor environments. Computer simulations reveal that the proposed hybrid beamforming
algorithms show near-optimal performance under different system configuration parameters.
There is a definite need to address hybrid transceiver design problems by taking into account the estimation
of mmWave channel with acceptable level of accuracy while minimizing the computational burden
associated with this process. Deep learning models may have the potential to address this problem but they
require huge amount of data for training purpose and these algorithms shift the computational complexity
from real-time to off-line training. By exploiting the potential of deep learning frameworks, the
performance of the proposed algorithms for different relay-assisted MIMO networks may be improved and,
it may be considered as a promising future direction to extend the research work in this thesis.