  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.
