Latency based Re-Enforcement Learning over Cognitive Software Defined 5G Networks
Baburao Kodavati, Madhu Ramarakula · 2021
In present days, in software defined networks, cognitive network (CN) is the key control to enable Internet of Things (IoT) services, whenever CN plays a important task in future network internet applications used in different types of real time applications such as agriculture, monitoring healthcare and smart metering with different scenario's. Because of increasing popularity of variety software defined crowed applications; it follow on low transmission rate in communication, major challenge behind this is to improve efficiency of packet transmission in software defined network data transmission. So that in this paper, we propose A Novel Re-Enforcement Learning Approach (NRELA) to improve the transmission efficiency using cognitive radio software defined networks through multiple channel to increase network throughput. To increase the system ability in between nodes with respect to re-enforcement learning approach to find optimal data communication in software defined networks. Establish the connection between different nodes to accelerate the solution for efficient data transmission in software defined communication. An experimental result of proposed approach gives better and efficient latency and energy results in data transmission in software defined networks.