Ultra Dense Small Cell Networks: Turning Density into Energy Efficiency

TL;DR

This paper introduces a novel joint power control and user scheduling method for ultra dense small cell networks, leveraging mean-field game theory and Lyapunov optimization to significantly improve energy efficiency and reduce outages.

Contribution

It proposes a new approach combining mean-field game theory and Lyapunov optimization for energy-efficient resource management in ultra dense small cell networks.

Findings

Achieves up to 70.7% energy efficiency gains.

Reduces network outage probabilities by 99.5%.

Provides a low-complexity solution with practical applicability.

Abstract

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables to capture the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network's outage probabilities compared to a baseline model which focuses on improving EE while attempting to satisfy the users' instantaneous quality-of-service requirements.