Unveiling the Future: A Comparative Analysis of LSTM and SP-LSTM for Network Traffic Prediction in 6G Networks

The emergence of 6G connectivity heralds a transformative era in wireless communication, emphasizing the necessity of network-wide intelligence for fully automated operations. At the heart of this paradigm shift lies the crucial need for an efficient algorithm capable of accurately predicting network traffic dynamics. This paper presents a comprehensive comparative study between two pivotal neural network architectures, LSTM (Long Short-Term Memory), and SP-LSTM (Speed-Optimized LSTM), within the context of network traffic prediction for the burgeoning 6G landscape. While LSTM stands as a widely acknowledged recurrent neural network, our innovative SP-LSTM is meticulously engineered for rapid and efficient decision-making. Through rigorous evaluation in a controlled environment, both models are examined for their efficacy in forecasting network traffic patterns. LSTM's proficiency in capturing long-term dependencies in sequential data is juxtaposed against SP-LSTM's emphasis on delivering swift and precise predictions. Our comparative analysis elucidates the distinctive strengths of these algorithms, assessing LSTM's effectiveness under varying conditions and scrutinizing SP-LSTM's proficiency in providing rapid, accurate forecasts. Additionally, we provide a GitHub link for accessing the project. This study offers vital insights into the relative merits and limitations of LSTM and SP-LSTM in network traffic prediction, essential for the advancement of intelligent 6G networking. These insights inform the selection of the optimal algorithm for realizing a seamless, intelligent 6G future with unparalleled capabilities.