<ul><li>Traffic pattern prediction in massive machine-type communication (mMTC) networks is challenging due to the inherent randomness of events and bursty traffic.</li><li>A machine learning-based framework using long-term short-term memory (LSTM) and DenseNet with feed-forward neural network (FFNN) layers is proposed for forecasting bursty traffic in multi-channel slotted ALOHA networks.</li><li>The framework includes a low-complexity online prediction algorithm that updates the states of the LSTM network using frequently collected data from the mMTC network.</li><li>Simulation results show that the proposed framework achieves a 52% higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load.</li></ul>

RACH Traffic Prediction in Massive Machine Type Communications

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