Traffic Prediction and Fast Uplink for Hidden Markov IoT Models

In this work we present a novel traffic prediction and fast uplink (FU) framework for IoT networks controlled by binary Markovian events. First we apply the forward algorithm with hidden Markov models (HMMs) in order to schedule the available resources to the devices with maximum likelihood activation probabilities via the FU 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 (AoI) 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 AoI.