Artificial Intelligence-Driven Dynamic 5G RAN Slicing for Smart Grid Application Using LSTM and Docker

In this paper, a dynamic Radio Access Network (RAN) slicing architecture with the help of Artificial intelligence(AI) is proposed that is specifically intended to meet the communication requirements of the 5G-powered smart grid in the future. With the help of precise prediction of network traffic based on historical data, the given solution makes use of Long Short-Term Memory (LSTM) neural networks that make it possible to allocate resources proactively and efficiently. These smart grid services distributed energy monitoring, fault detection and demand response are encoder-ed by independent network slice allowing performance isolation and meeting specific Quality of Service (QoS) requirements. These slices are realized with the help of Docker containers that offer a lightweight and scalable deployment, quick reconfiguration, and the isolation of faults. Prometheus metrics collection to be inspected visually can be used to monitor the real-time activity, so that the functioning of the system is responsive and transparent. According to the experimental results, the framework offers high accuracy in predicting traffic, low latency in critical applications of the smart grid, and high efficiency of resource utilisation. Overall, this dynamic RAN slicing architecture with the help of AI provides a versatile and robust solution to upcoming smart grid communication needs with the capabilities of 5G devices.