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We consider a multi-source relaying system where independent sources randomlygenerate status update packets which are sent to the destination with the aidof a relay through unreliable links. We develop transmission schedulingpolicies to minimize the weighted sum average age of information (AoI) subjectto transmission capacity and long-run average resource constraints. Weformulate a stochastic control optimization problem and solve it using aconstrained Markov decision process (CMDP) approach and a drift-plus-penaltymethod. The CMDP problem is solved by transforming it into an MDP problem usingthe Lagrangian relaxation method. We theoretically analyze the structure ofoptimal policies for the MDP problem and subsequently propose a structure-awarealgorithm that returns a practical near-optimal policy. Using thedrift-plus-penalty method, we devise a near-optimal low-complexity policy thatperforms the scheduling decisions dynamically. We also develop a model-freedeep reinforcement learning policy for which the Lyapunov optimization theoryand a dueling double deep Q-network are employed. The complexities of theproposed policies are analyzed. Simulation results are provided to assess theperformance of our policies and validate the theoretical results. The resultsshow up to 91% performance improvement compared to a baseline policy.

Minimizing the AoI in Resource-Constrained Multi-Source Relaying Systems: Dynamic and Learning-based Scheduling