Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Abstract

Abstract 5G technologies is known for its beneficial characteristic of supporting largely connected network and high-speed data transmission. With increasing number of services and application meant for hosting over 5G network, there is also an increasing concern towards accomplishing better quality of service and quality of experience in global telecommunication sector. In this line of communication advancement, it is also noted that machine learning is one of the contributories and enabling technology towards boosting the performance of value-added services and applications running on 5G networks. Existing review of literature exhibited multiple variants of methodologies meant for performing predictive performance towards leveraging quality of data delivery services in 5G. However, there are quite many research challenges too that is directly associated with deploying a cost-effective learning scheme in 5G. Therefore, the proposed scheme contributes towards developing a novel and yet simplified baseline architecture which targets to accomplishing seamless and reliable data dissemination services in 5G. The proposed model constructs a novel deployment scenario where a user handheld device is considered as a mobile node with an agenda to considered routing in allocated multiple paths to reach its destination. Further, RFC 8822 is used for deployment 5G standard along with a specific mobility model sync with a real-time server via access point and gateway node in large deployment scenario. Finally, an enhanced Long Short-Term Memory is implemented towards performing identification of predictive routes that are shared to users directly over 5G network. The study outcome is benchmarked with existing learning schemes to exhibit that proposed scheme offers approximately 35% of reduced losses, 19% of higher throughput, 23% of reduced delay, 37% of reduced memory, and 41% of reduced processing time.