Distributed Spectrum Access for Cognitive Small Cell Networks: A Robust Graphical Game Approach

TL;DR

This paper proposes a robust graphical game approach for distributed spectrum access in cognitive small cell networks, addressing local interference and time-varying channels, with proven equilibrium existence and a distributed learning algorithm validated by simulations.

Contribution

It introduces a novel robust graphical game model for spectrum access considering local interference and channel variability, with a distributed algorithm ensuring convergence.

Findings

The game is an ordinal potential game with at least one pure strategy Nash equilibrium.

The lower throughput bound of Nash equilibria is analytically derived.

Simulation results confirm the effectiveness of the distributed learning algorithm in dynamic environments.

Abstract

This letter investigates the problem of distributed spectrum access for cognitive small cell networks. Compared with existing work, two inherent features are considered: i) the transmission of a cognitive small cell base station only interferes with its neighbors due to the low power, i.e., the interference is local, and ii) the channel state is time-varying due to fading. We formulate the problem as a robust graphical game, and prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium (NE). Also, the lower throughput bound of NE solutions is analytically obtained. To cope with the dynamic and incomplete information constraints, we propose a distribute spectrum access algorithm to converge to some stable results. Simulation results validate the effectiveness of the proposed game-theoretic distributed learning solution in time-varying spectrum environment.