Distributed Learning for Optimal Spectrum Access in Dense Device-To-Device Ad-Hoc Networks

Tomer Boyarski, Wenbo Wang, Amir Leshem*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In 5G networks, Device-To-Device (D2D) communications aim to provide dense coverage without relying on the cellular network infrastructure. To achieve this goal, the D2D links are expected to be capable of self-organizing and allocating finite, interfering resources with limited inter-link coordination. We consider a dense ad-hoc D2D network and propose a decentralized time-frequency allocation mechanism that achieves sub-linear social regret toward optimal spectrum efficiency. The proposed mechanism is constructed in the framework of multi-Agent multi-Armed bandits, which employs the carrier-sensing-based distributed auction to learn the optimal allocation of time-frequency blocks with different channel state dynamics from scratch. Our theoretical analysis shows that the proposed fully distributed mechanism achieves a logarithmic regret bound by adopting an epoch-based strategy-learning scheme when the length of the strategy-exploitation window is exponentially growing. We further propose an implementation-friendly protocol featuring a fixed exploitation window, which guarantees a good tradeoff between performance optimality and protocol efficiency. Numerical simulations demonstrate that the proposed protocol achieves higher efficiency than the prevalent reference algorithms in both static and dynamic wireless environments.

Original languageAmerican English
Pages (from-to)3149-3163
Number of pages15
JournalIEEE Transactions on Signal Processing
StatePublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1991-2012 IEEE.


  • D2D networks
  • Multi-Agent multi-Armed bandit
  • distributed network management
  • resource allocation


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