Smart Congestion Control in 5G/6G Networks Using Hybrid Deep Learning Techniques

Abstract

With the mobility and ease of connection, wireless sensor networks have played a significant role in communication over the last few years, making them a significant data carrier across networks. Additional security, lower latency, and dependable standards and communication capability are required for future-generation systems such as millimeter-wave LANs, broadband wireless access schemes, and 5G/6G networks, among other things. Effectual congestion control is regarded as of the essential aspects of 5G/6G technology. It permits operators to run many network illustrations on a single organization while maintaining higher service quality. A sophisticated decision-making system for arriving network traffic is necessary to confirm load balancing, limit network slice letdown, and supply alternative slices in slice letdown or congestion. Because of the massive amount of data being generated, artificial intelligence (AI) and machine learning (ML) play a vital role in reconfiguring and improving a 5G/6G wireless network. In this research work, a hybrid deep learning method is being applied to forecast optimal congestion improvement in the wireless sensors of 5G/6G IoT networks. This proposed model is applied to a training dataset to govern the congestion in a 5G/6G network. The proposed approach provided promising results, with 0.933 accuracy, and 0.067 miss rate.

AI-Driven Intelligent Transportation Systems in the Age of 5G/6G Networks

“…The input gate, forget gate, and output gate are the three basic parts of these cells. Each of these gates is responsible for controlling the flow of information in and out of the cell [6].…”

A comprehensive survey of performance, security and privacy issues in the network interface layer of the TCP/IP

“…The network interface layer plays a vital role in today's interconnected world, impacting various aspects of society and culture, e-commerce and online services needs, secure and reliable network interface layer for the smooth functioning of online activities [27]. Technological advancements are shaping the future of the NIL, Software-defined networking (SDN), provides greater flexibility and control over network traffic management, potentially improving performance and security in the network interface layer.…”

An efficient routing protocol to reduce traffic and congestion control in cloud edge networks of wireless sensor networks

“…In the meantime, none of the packets in cwnd can be lost; hence, the problem is communication disruption rather than congestion. 18 As a result, the projected decline ratio is given by Equation (5).…”

“…The priority methods employed in WSNs optimize resource utilization by considering the resource‐constrained nature. The sensor nodes conveyed the traffic situation and influenced the energy proficiency of administration boundaries 5 . At the initial stage, simple routing algorithms are created that perform the data transmission within WSNs.…”

“…The sensor nodes conveyed the traffic situation and influenced the energy proficiency of administration boundaries. 5 At the initial stage, simple routing algorithms are created that perform the data transmission within WSNs. The congestion control is determined by wide applications such as transmission control protocol (TCP) and stream control transmission protocol (SCTP).…”