Improving High data rates in Milli meter Wave Communication networks via Long short term memory Technique

In present scenarios the mobile users except for Seamless communication, mobile esports gaming, hybrid classrooms, and playground parks, massive mobile performance, no lag video with best 5G Connections. This huge volume of data transmission is supported by big data architecture as a backbone of the internet. Big data architecture is modelled with handling processing, recovery, breakdown and analysis of information that is high volume or with complex design pattern when compared with traditional database systems, with this existing design in this paper we are integrating the mixed-mode millimeter -wave data transmission design for better network capacity with high data rates, throughput, coverage, and performance in both indoor and outdoor environments. Machine Learning has the potential to resolve the problems in the design of communication networks, In particular, we create a network design with our theoretical approach that evaluates the download speed of the user equipment by modeling the connection between a big data system and mm-wave antenna system. Long Short-term memory algorithm is used to predict the mm-wave antenna group in a time-series manner for data transmission to improve the performance of a network. The result proves the better throughput when compared with non-big data architecture and with this proposed