الفهرس 
Millimeter WavesOnly 14 pages are availabe for public view
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Abstract
The increasing demands for a high data rate wireless connectivity necessitate
the search for suitable spectrum regions to meet anticipated future requirements.
The research community has shown considerable interest in millimeter-wave (mmwave) communication.
This thesis presents a survey of different precoding or beamforming techniques that
have been proposed in the literature. Hybrid beamforming techniques that combine
analog and digital precoding can be adopted for mm-wave massive mimo wireless
systems to minimize the power consumption and hardware complexity. The
performance of the most common hybrid precoding algorithms has been investigated
in this thesis. A convolution neural network (CNN) structure based on deep learning
technique are suggested in this thesis. It can be trained to optimize and maximize
spectral efficiency and it is compared with the traditional techniques. Also, a
comparison has been made between the proposed technique and many deep learning
techniques currently in use for detecting hybrid precoding and combining. It has
been found that, DarkNet-19 gives the best performance for detecting hybrid
precoding and combining.