Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks

TL;DR

This paper introduces an LSTM-based deep learning method for event-driven source traffic prediction in machine-type communications, improving resource efficiency and prediction accuracy over existing solutions.

Contribution

The paper presents a novel LSTM approach that models causal relationships in device transmission data for improved event-driven traffic prediction in MTC.

Findings

Model outperforms baselines with ~9% accuracy improvement.

Significantly reduces Random Access requests.

Demonstrates low signaling overhead.

Abstract

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.