Advanced Mathematical Models for Predicting Complex System Behavior in 6G Networks

Abstract

The research indicates that the rapid evolution of wireless communication technologies has prepared the way for the creation of 6G networks that are likely to introduce revolutionary features, including ultra-low Latency, enormous connectivity, and AI-controlled networks. Also, artificial intelligence (AI), machine learning, and Internet of things (IoT) are the technologies which will be employed in such networks due to the dynamics and complexity nature of such networks. Nevertheless, it is not easy to make any predictions regarding such networks owing to the many factors that determine their performance. This study seeks to address the above issues through the formulation of complex mathematical models that make it easy to predict the behavior of the networks. The suggested mathematical models will incorporate machine learning algorithms together with traditional mathematical modeling to ensure precise predictions. The suggested models turn out to operate better with respect to the performance criteria due to their more accurate performance (95% vs. 88%), a reduced delay (10.5 ms vs. 15.2 ms) and increased throughput (1.2 Mbps vs. 0.85 Mbps). It is clear from this data that the suggested models will prove to be useful in terms of prediction and optimization of the 6G network and thus they can be utilized in the real-time management of the network. As a result of this research, more efficient, resilient and intelligent 6G networks have been developed and this research contains many important aspects that can be applied in the future.