To relay or not to relay: Learning device-to-device relaying strategies in cellular networks

Nicholas Mastronarde, Viral Patel, Jie Xu, Lingjia Liu, Mihaela Van Der Schaar

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


We consider a cellular network where mobile transceiver devices that are owned by self-interested users are incentivized to cooperate with each other using tokens, which they exchange electronically to 'buy' and 'sell' downlink relay services, thereby increasing the network's capacity compared to a network that only supports base station-to-device (B2D) communications. We investigate how an individual device in the network can learn its optimal cooperation policy online, which it uses to decide whether or not to provide downlink relay services for other devices in exchange for tokens. We propose a supervised learning algorithm that devices can deploy to learn their optimal cooperation strategies online given their experienced network environment. We then systematically evaluate the learning algorithm in various deployment scenarios. Our simulation results suggest that devices have the greatest incentive to cooperate when the network contains (i) many devices with high energy budgets for relaying, (ii) many highly mobile users (e.g., users in motor vehicles), and (iii) neither too few nor too many tokens. Additionally, within the token system, self-interested devices can effectively learn to cooperate online, and achieve up to 20 percent throughput gains on average compared to B2D communications alone, all while selfishly maximizing their own utilities.

Original languageEnglish (US)
Article number7181721
Pages (from-to)1569-1585
Number of pages17
JournalIEEE Transactions on Mobile Computing
Issue number6
StatePublished - Jun 1 2016


  • Emerging technologies
  • learning
  • mobile communications systems
  • mobile environments
  • network management
  • wireless communications

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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