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We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling status update transmissions over a set of channels. The freshness of information at the destination node is measured in terms of the Age of Information (AoI) metric. In this setting, a natural tradeoff exists between the transmission cost (or equivalently, energy consumption) of the source and the achievable AoI performance at the destination. This tradeoff has been optimized in the existing literature under the assumption of having a complete knowledge of the channel statistics. In this work, we develop online learning-based algorithms with finite-time guarantees that optimize this tradeoff in the practical scenario where the channel statistics are unknown to the scheduler. In particular, when the channel statistics are known, the optimal scheduling policy is first proven to have a threshold-based structure with respect to the value of AoI (i.e., it is optimal to drop updates when the AoI value is below some threshold). This key insight was then utilized to develop the proposed learning algorithms that surprisingly achieve an order-optimal regret (i.e., O(1)) with respect to the time horizon length. 

Abstract Materials that blend physical properties that are usually mutually exclusive could facilitate devices with novel functionalities. For example, the doped perovskite alkaline earth stannates BaSnO 3 and SrSnO 3 show the intriguing combination of high light transparency and high electrical conductivity. Understanding such emergent physics requires deep insight into the materials’ electronic structures. Moreover, the band structure at the surfaces of those materials can deviate significantly from their bulk counterparts, thereby unlocking novel physical phenomena. Employing angle-resolved photoemission spectroscopy and ab initio calculations, we reveal the existence of a 2-dimensional metallic state at the SnO 2 -terminated surface of 1% La-doped BaSnO 3 thin films. The observed surface state is characterized by a distinct carrier density and a lower effective mass compared to the bulk conduction band, of about 0.12 m e . These particular surface state properties place BaSnO 3 among the materials suitable for engineering highly conductive transition metal oxide heterostructures.