Abstract
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.