Traffic Prediction and Fast Uplink for Hidden Markov IoT Models

TL;DR

This paper introduces a traffic prediction and uplink scheduling framework for IoT networks using hidden Markov models, improving resource efficiency and information freshness over traditional methods.

Contribution

It presents a novel real-time traffic prediction scheme with an online learning algorithm for IoT uplink scheduling based on hidden Markov models.

Findings

Outperforms TDMA and grant-free access in regret minimization.

Reduces age of information and improves system efficiency.

Demonstrates effectiveness through simulation results.

Abstract

In this work, we present a novel traffic prediction and fast uplink framework for IoT networks controlled by binary Markovian events. First, we apply the forward algorithm with hidden Markov models (HMM) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via fast uplink grant. In addition, we evaluate the regret metric as the number of wasted transmission slots to evaluate the performance of the prediction. Next, we formulate a fairness optimization problem to minimize the age of information while keeping the regret as minimum as possible. Finally, we propose an iterative algorithm to estimate the model hyperparameters (activation probabilities) in a real-time application and apply an online-learning version of the proposed traffic prediction scheme. Simulation results show that the proposed algorithms outperform baseline models such as time division multiple access (TDMA) and grant-free (GF) random-access in terms of regret, the efficiency of system usage, and age of information.