At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Getting network traffic right isn't just a nice-to-have for 6G wireless networks-it's essential if you want smart resource use and good service.In this study, I built a time-series forecasting system that blends old-school stats with deep learning, so you get a better handle on what's happening in the network.First, I used an ARIMA model to pick up on the simple, linear patterns in the traffic.But linear models can only take you so far.That's where the LSTM neural network comes in; it's great at spotting the trickier, nonlinear trends and keeping track of what's happened over longer stretches of time.Before feeding the LSTM, I normalized the real traffic data with Min-Max scaling and set it up as supervised sequences.To see how well each model worked, I looked at their Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) scores.The results?LSTM outperformed ARIMA, giving more accurate predictions and making it possible to forecast multiple steps ahead.This means network managers can make smarter choices and stay ahead of changes in 6G traffic.