Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

TL;DR

This paper introduces a distributed, network-assisted channel selection method for D2D communication using a novel combination of no-regret learning and calibrated forecasting, ensuring efficient spectrum use and convergence to equilibrium.

Contribution

It proposes a new distributed algorithm for D2D channel selection based on multi-player bandit learning with theoretical guarantees of regret minimization and equilibrium convergence.

Findings

The approach achieves vanishing regret compared to the optimal solution.

The empirical joint frequencies converge to the set of correlated equilibria.

The method effectively minimizes interference with cellular networks.

Abstract

We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources construct a distributed system, where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Analytically, it is established that this approach not only yields vanishing regret (in comparison to the global optimal solution), but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.