Matching while learning: Wireless scheduling for age of information optimization at the edge

In this paper, we investigate the minimization of age of information (AoI), a metric that measures the information freshness, at the network edge with unreliable wireless communications. Particularly, we consider a set of users transmitting status updates, which are collected by the user randomly over time, to an edge server through unreliable orthogonal channels. It begs a natural question: with random status update arrivals and obscure channel conditions, can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI? To give an adequate answer, we define a bipartite graph and formulate a dynamic edge activation problem with stability constraints. Then, we propose an online matching while learning algorithm (MatL) and discuss its implementation for wireless scheduling. Finally, simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users' buffers for fresher information at the edge.