Machine Learning-Based Predictive Resource Allocation for Real-Time IoT Traffic in 6G Networks

Abstract

The advent of the Sixth Generation (6G) networks promises an unprecedented change in the wireless telecommunication technologies and go beyond the capabilities of 5G in terms of having ultralow latency, ultrahigh throughput, and ultra-reliable connectivity. These attributes are needed to complement the exploding growth of the Internet of Things (IoT), especially in real-time applications (e.g. autonomous vehicles, industrial automation, intelligent healthcare systems). The exigencies of real-time network resource management have become increasingly important, as the growing volume and diversity of IoT traffic demand sophisticated, proactive network resource management. Accordingly, this study proposes a novel machine learning based predictive algorithm for the 6G network resource management to support real time IoTs traffic demands. By using predictive analytics, the proposed methodology anticipates the traffic patterns and allocates resources in advance, thus improving the Quality of Service (QoS), and reducing latency and improving the throughput while also ensuring equitable distribution of resources.  The research presents a hybrid model combining Long Short-Term Memory (LSTM) networks to predict traffic dynamics and then reinforcement learning mechanisms to control the decision of the allocation of resources. The system development involves the use of a mixture of synthetic and publicly available IoT datasets to train and validate the model, which illustrates good generalization capabilities for various scenarios.  Performance evaluation is concerned with benchmark measures to determine the predictive accuracy in correlation with the allocation efficiency, latency reduction, and QoS compliance. The findings are part of the growing body of research on smart management of next-generation wireless networks. Looking into the future, 6G empowered IoT ecosystems will be able to be operated in a sustainable manner based on the concept of adaptive resource-allocation strategies according to the insights reported in relation to the subject.