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.
Bibliographical notePublisher Copyright:
© 1991-2012 IEEE.
- D2D networks
- Multi-Agent multi-Armed bandit
- distributed network management
- resource allocation