Power allocation with stackelberg game in femtocell networks: a self-learning approach

TL;DR

This paper proposes a self-learning power allocation strategy in femtocell networks modeled as a Stackelberg game, enabling energy-efficient and QoS-guaranteed operation with limited information exchange.

Contribution

It introduces a novel self-learning mechanism for hierarchical power control in femtocell networks under both continuous and discrete power profiles.

Findings

The proposed schemes converge to equilibrium strategies.

Simulation results validate the effectiveness of the learning algorithms.

The approach improves energy efficiency while maintaining QoS.

Abstract

This paper investigates the energy-efficient power allocation for a two-tier, underlaid femtocell network. The behaviors of the Macrocell Base Station (MBS) and the Femtocell Users (FUs) are modeled hierarchically as a Stackelberg game. The MBS guarantees its own QoS requirement by charging the FUs individually according to the cross-tier interference, and the FUs responds by controlling the local transmit power non-cooperatively. Due to the limit of information exchange in intra- and inter-tiers, a self-learning based strategy-updating mechanism is proposed for each user to learn the equilibrium strategies. In the same Stackelberg-game framework, two different scenarios based on the continuous and discrete power profiles for the FUs are studied, respectively. The self-learning schemes in the two scenarios are designed based on the local best response. By studying the properties of the proposed game in the two situations, the convergence property of the learning schemes is provided. The simulation results are provided to support the theoretical finding in different situations of the proposed game, and the efficiency of the learning schemes is validated.