Low-Complexity Stackelberg Game Approach for Energy-Efficient Resource Allocation in Heterogeneous Networks

Abstract

In this letter, we propose a low-complexity game-theoretic approach for energy-efficient resource allocation in a two-tier heterogeneous network. We measure the energy efficiency (EE) by "Revenue per Cost" in the utility domain considering both power allocation and interference coordination. Since global optimization for maximizing the overall EE is computationally expensive, we transform this nonconvex problem to a two-stage Stackelberg game inspired by fractional programming. We then employ the backward induction method and the Lagrange dual decomposition method to solve this game. An efficient iterative algorithm is designed to achieve Stackelberg equilibrium. Simulation results validate the effectiveness of the proposed approach.