Performance and Convergence of Multi-user Online Learning

  • Cem Tekin
  • Mingyan Liu
Conference paper

DOI: 10.1007/978-3-642-30373-9_23

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 75)
Cite this paper as:
Tekin C., Liu M. (2012) Performance and Convergence of Multi-user Online Learning. In: Jain R., Kannan R. (eds) Game Theory for Networks. GameNets 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 75. Springer, Berlin, Heidelberg

Abstract

We 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 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 optimal allocation.

Keywords

multi-user learning multi-armed bandits spectrum sharing congestion games 

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Cem Tekin
    • 1
  • Mingyan Liu
    • 1
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborUSA

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