LSTM-based Framework for 5G Resource Allocation Prediction

During the rapid evolution of 5G networks, the efficient allocation of bandwidth, frequency spectrum, and computing power is critical to maintaining a high standard of service and performance. A methodology for predicting resource allocation percentages based on Long-Short-Term Memory (LSTM) is presented in this paper. The ability to predict resource allocation percentages enhances network operators’ efficiency in terms of bandwidth, frequency spectrum, and computing power utilization. Network operators can maintain high service levels by accurately predicting resource needs and allocating resources dynamically. In high-latency and high-reliability applications such as VoIP calls, video streaming, and emergency services, this is particularly critical. From Iot sensor data to high-definition video calls and emergency services, our proposed model explores the specific resource requirements of a 5 G network. Our experimental results show that the LSTM model achieves a considerable improvement in performance when compared to the best-performing machine learning model, with an R2 score of 0.976 compared to 0.937 for the best-performing model. Predictive accuracy is improved by approximately 4.16 percent. This highlights the superiority of our proposed framework to recognizing patterns in time series data.