Performance and Convergence of Multi-User Online Learning
Source: University of Michigan
The authors study the problem of allocating multiple users to a set of wireless channels in a decentralized manner when the channel qualities are time-varying and unknown to the users, and accessing the same channel by multiple users leads to reduced quality (e.g., data rates) received by the users due to interference. In such a setting the users not only need to learn the inherent channel quality and at the same time the best allocations of users to channels so at to maximize the social welfare. Assuming that the users adopt a certain online learning algorithm, they investigate under what conditions the socially optimal allocation is achievable.