Age Optimal Sampling Under Unknown Delay Statistics

This paper revisits the problem of sampling and transmitting status updates through a channel with random delay under a sampling frequency constraint. We use the Age of Information (AoI) to characterize the status information freshness at the receiver. The goal is to design a sampling policy that can minimize the average AoI when the statistics of delay is unknown. We reformulate the problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. We prove that the proposed algorithm satisfies the sampling frequency constraint. Moreover, when the transmission delay is bounded and its distribution is absolutely continuous, the average AoI obtained by the proposed algorithm converges to the minimum AoI when the number of samples $K$ goes to infinity with probability 1. We show that the optimality gap decays with rate $\mathcal {O}\left ({\ln K/K}\right)$ , and the proposed algorithm is minimax rate optimal. Simulation results validate the performance of our proposed algorithm.