Traffic Prediction and Fast Uplink for Hidden Markov IoT Models

The road to extreme low latency urges the invention of intelligent access schemes that depart from the shortcomings associated with the classic ones. In this work, we present a novel traffic prediction and fast uplink framework for IoT devices activated by binary Markovian events. First, we apply the forward algorithm in the context of hidden Markov models (HMM) to predict the hidden states, which are used 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 missed resource allocation opportunities 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.