Load-aware Dynamic Spectrum Access for Small Cell Networks: A Graphical Game Approach

TL;DR

This paper presents a graph-based game-theoretic approach for dynamic spectrum access in small cell networks, considering load and interference, with proven convergence and near-optimal performance.

Contribution

It introduces a novel load-aware graphical game model for spectrum access, proving it as an exact potential game and proposing a fast converging learning algorithm.

Findings

The game is an exact potential game with interference as the potential.

The proposed algorithm converges rapidly to Nash equilibrium.

Performance is close to the optimal solution.

Abstract

In this letter, we investigate the problem of dynamic spectrum access for small cell networks, using a graphical game approach. Compared with existing studies, we take the features of different cell loads and local interference relationship into account. It is proved that the formulated spectrum access game is an exact potential game with the aggregate interference level as the potential function, and Nash equilibrium (NE) of the game corresponds to the global or local optima of the original optimization problem. A lower bound of the achievable aggregate interference level is rigorously derived. Finally, we propose an autonomous best response learning algorithm to converge towards its NE. It is shown that the proposed game-theoretic solution converges rapidly and its achievable performance is close to the optimum solution.