Performance Analysis of ML-Based MTC Traffic Pattern Predictors

Abstract

Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-ofthe-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

“…The algorithm choice should be based on the specific problem, network architecture, and available data. A comprehensive/holistic approach that considers all aspects of the network, including HW, SW, and communication protocols, is needed to design energy-efficient solutions [247]- [249]. Moreover, the specific EE problem, the architecture and the characteristics of the network, the available data, and the strengths and weaknesses of each ML algorithm must be considered [247]- [249].…”

“…A comprehensive/holistic approach that considers all aspects of the network, including HW, SW, and communication protocols, is needed to design energy-efficient solutions [247]- [249]. Moreover, the specific EE problem, the architecture and the characteristics of the network, the available data, and the strengths and weaknesses of each ML algorithm must be considered [247]- [249]. For instance, decision trees are suitable for routing optimization, while support vector machines are effective for network anomaly detection and prediction.…”