  Source traffic prediction is one of the main challenges of enabling
predictive resource allocation in machine type communications (MTC). In this
paper, a Long Short-Term Memory (LSTM) based deep learning approach is proposed
for event-driven source traffic prediction. The source traffic prediction
problem can be formulated as a sequence generation task where the main focus is
predicting the transmission states of machine-type devices (MTDs) based on
their past transmission data. This is done by restructuring the transmission
data in a way that the LSTM network can identify the causal relationship
between the devices. Knowledge of such a causal relationship can enable
event-driven traffic prediction. The performance of the proposed approach is
studied using data regarding events from MTDs with different ranges of entropy.
Our model outperforms existing baseline solutions in saving resources and
accuracy with a margin of around 9%. Reduction in Random Access (RA) requests
by our model is also analyzed to demonstrate the low amount of signaling
required as a result of our proposed LSTM based source traffic prediction
approach.
