  In 5G and beyond communication systems, the notion of latency gets great
momentum in wireless connectivity as a metric for serving real-time
communications requirements. However, in many applications, research has
pointed out that latency could be inefficient to handle applications with data
freshness requirements. Recently, Age of Information (AoI) metric, which can
capture the freshness of the data, has attracted a lot of attention. In this
work, we consider mixed traffic with time-sensitive users; a
deadline-constrained user, and an AoI-oriented user. To develop an efficient
scheduling policy, we cast a novel optimization problem formulation for
minimizing the average AoI while satisfying the timely throughput constraints.
The formulated problem is cast as a Constrained Markov Decision Process (CMDP).
We relax the constrained problem to an unconstrained Markov Decision Process
(MDP) problem by utilizing the Lyapunov optimization theory and it can be
proved that it is solved per frame by applying backward dynamic programming
algorithms with optimality guarantees. In addition, we provide a low-complexity
algorithm guaranteeing that the timely-throughput constraint is satisfied.
Simulation results show that the timely throughput constraints are satisfied
while minimizing the average AoI. Simulation results show the convergence of
the algorithms for different values of the weighted factor and the trade-off
between the AoI and the timely throughput.
