Distributed Learning for Channel Allocation over a Shared Spectrum

S. M. Zafaruddin*, Ilai Bistritz, Amir Leshem, Dusit Niyato

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Channel allocation is the task of assigning channels to users such that some objective (e.g., sum-rate) is maximized. In centralized networks such as cellular networks, this task is carried by the base station (BS) which gathers the channel state information (CSI) from the users and computes the optimal solution. In distributed networks such as ad-hoc and device-to-device (D2D) networks, no BS exists and conveying global CSI between users is costly or simply impractical. When the CSI is time varying and unknown to the users, the users face the challenge of both learning the channel statistics online and converging to a good channel allocation. This introduces a multi-armed bandit (MAB) scenario with multiple decision makers. If two or more users choose the same channel, a collision occurs and they all receive zero reward. We propose a distributed channel allocation algorithm that each user runs and converges to the optimal allocation while achieving an order optimal regret of O log T , where T denotes the length of time horizon. The algorithm is based on a carrier sensing multiple access (CSMA) implementation of the distributed auction algorithm. It does not require any exchange of information between users. Users need only to observe a single channel at a time and sense if there is a transmission on that channel, without decoding the transmissions or identifying the transmitting users. We demonstrate the performance of our algorithm using simulated LTE and 5G channels.

Original languageAmerican English
Article number8792155
Pages (from-to)2337-2349
Number of pages13
JournalIEEE Journal on Selected Areas in Communications
Volume37
Issue number10
DOIs
StatePublished - Oct 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1983-2012 IEEE.

Keywords

  • Distributed channel allocation
  • dynamic spectrum accesses
  • multiplayer multi-armed bandit
  • online learning
  • resource management

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