LAUSR

In knowledge-freshness-sensitive wireless networking scenarios, a major identified gap lies in overlooking the notable impact of physical link interference and the validity duration (i.e. deadline) of newly acquired knowledge on the network-wide achieved Age of Information (AoI). This paper aims at closing this gap not only through accounting for deadline and interference constraints but also through proposing efficient mechanisms leveraging inter-nodal cooperativeness to work around them and, indeed, migrate the achieved network-wide average AoI to its next lower level. First, a Deadline-constrained Interference-aware Cooperative data transmission Scheduling (DICS) framework is presented. This framework encloses a mathematical formulation of the cooperative data transmission scheduling optimization problem as an Integer Linear Program (ILP) that accounts for deadline and interference constraints and has the two-fold objective of: i) investigating the impact of integrating auxiliary Secondary Nodes (SNs) in the network to assist Primary Nodes (PNs) in improving their overall achieved AoI, and ii) evaluating the effect of altering various network-wide parameter values on the network's AoI performance. Owing to the ILP's remarkable complexity, a GReedy Algorithm (GRA) is proposed, which generates acceptable sub-optimal schedules (error of 5.222%) but is found to be non-scalable. To address GRA's scalability issue, a Reinforcement Learning Actor-Critic Algorithm (RL-ACA) is developed, which, is found to outperform GRA by 2.849% in terms of accuracy. Thorough simulations and numerical analyses are conducted to gauge the merit of the proposed DICS's RL-ACA and highlight its AoI performance improvement as compared to existing benchmarks.