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We consider a single-hop wireless network with sources transmittingtime-sensitive information to the destination over multiple unreliablechannels. Packets from each source are generated according to a stochasticprocess with known statistics and the state of each wireless channel (ON/OFF)varies according to a stochastic process with unknown statistics. Thereliability of the wireless channels is to be learned through observation. Atevery time slot, the learning algorithm selects a single pair (source, channel)and the selected source attempts to transmit its packet via the selectedchannel. The probability of a successful transmission to the destinationdepends on the reliability of the selected channel. The goal of the learningalgorithm is to minimize the Age-of-Information (AoI) in the network over $T$time slots. To analyze the performance of the learning algorithm, we introducethe notion of AoI regret, which is the difference between the expectedcumulative AoI of the learning algorithm under consideration and the expectedcumulative AoI of a genie algorithm that knows the reliability of the channelsa priori. The AoI regret captures the penalty incurred by having to learn thestatistics of the channels over the $T$ time slots. The results are two-fold:first, we consider learning algorithms that employ well-known solutions to thestochastic multi-armed bandit problem (such as $\epsilon$-Greedy, UpperConfidence Bound, and Thompson Sampling) and show that their AoI regret scalesas $\Theta(\log T)$; second, we develop a novel learning algorithm and showthat it has $O(1)$ regret. To the best of our knowledge, this is the firstlearning algorithm with bounded AoI regret.

Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals