Optimal Preemption Policy for Age of Information Minimization with Random Arrival and Known Packet Length

An optimal preemption framework is proposed to minimize Age of Information (AoI) in single-link systems with stochastic packet arrivals and known packet lengths. The problem is formulated as a Markov Decision Process (MDP) solved through relative value iteration (RVI), demonstrating that packet-length-dependent threshold policies achieve minimal average AoI. For scenarios with unknown traffic statistics, a Deep Q-Network (DQN) algorithm learns these thresholds adaptively through real-time interactions without prior distribution knowledge. Numerical evaluations under exponential and bounded Pareto packet length distributions reveal 15.5 percent average AoI reduction compared to non-preemptive baselines using the RVI method, while the DQN method achieves 14.4 percent improvement with less than 1.1 percent performance gap from the optimal policy. Both methods exhibit threshold-driven decision structures that balance immediate AoI reduction against long-term scheduling efficiency. Comparative analysis reveals the framework’s robustness across stationary and dynamic environments, with the DQN maintaining near-optimal performance through dimension-reduced state representation. These results establish a unified solution for AoI minimization that transitions seamlessly between model-aware and model-agnostic configurations, addressing critical challenges in real-time Internet of Things (IoT) networks and status update systems requiring freshness guarantees.