Distributed Learning Algorithms for Spectrum Sharing in Spatial Random Access Wireless Networks

Kobi Cohen, Angelia Nedich, R. Srikant

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


We consider distributed optimization over orthogonal collision channels in spatial random access networks. Users are spatially distributed and each user is in the interference range of a few other users. Each user is allowed to transmit over a subset of the shared channels with a certain attempt probability. We study both the non-cooperative and cooperative settings. In the former, the goal of each user is to maximize its own rate irrespective of the utilities of other users. In the latter, the goal is to achieve proportionally fair rates among users. Simple distributed learning algorithms are developed to solve these problems. The efficiencies of the proposed algorithms are demonstrated via both theoretical analysis and simulation results.

Original languageEnglish (US)
Article number7738417
Pages (from-to)2854-2869
Number of pages16
JournalIEEE Transactions on Automatic Control
Issue number6
StatePublished - Jun 2017


  • Collision channel
  • Nash equilibrium
  • distributed optimization
  • proportional fairness
  • slotted-ALOHA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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