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We argue for giving users the ability to lease bandwidth temporarily from anymobile network operator. We propose, prototype, and evaluate a spectrum marketfor mobile network access, where multiple network operators offer blocks ofbandwidth at specified prices for short-term leases to users, with autonomousagents on user devices making purchase decisions by trading off price,performance, and budget constraints.  We show that the problem of provider selection can be formulated as aso-called Bandit problem. For the case where providers change pricessynchronously, we approach the problem through contextual multi-armed banditsand Reinforcement Learning methods like Q-learning either applied directly tothe bandit maximization problem or indirectly to approximate the Gittinsindices that are known to yield the optimal provider selection policy. For asimulated scenario corresponding to a practical use case, our agent shows a$20-41\%$ QoE improvement over random provider selection under various demand,price and mobility conditions.  We implemented a prototype spectrum market using LTE networks and eSIMtechology and deployed it on a testbed, using a blockchain to implement theledger where bandwidth purchase transactions are recorded. Experiments showedthat we can learn both user behavior and network performance efficiently, andrecorded $25-74\%$ improvements in QoE under various competing agent scenarios.

A Multi-Armed Bandit-based Approach to Mobile Network Provider Selection