  We study the problem of allocating multiple users to a set of wireless
channels in a decentralized manner when the channel quali- ties are
time-varying and unknown to the users, and accessing the same channel by
multiple users leads to reduced quality 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 as to maximize the social
welfare. Assuming that the users adopt a certain online learning algorithm, we
investigate under what conditions the socially optimal allocation is
achievable. In particular we examine the effect of different levels of
knowledge the users may have and the amount of communications and cooperation.
The general conclusion is that when the cooperation of users decreases and the
uncertainty about channel payoffs increases it becomes harder to achieve the
socially opti- mal allocation.
