Energy-Efficient Resource Management in Ultra Dense Small Cell Networks: A Mean-Field Approach

TL;DR

This paper introduces a mean-field game approach for energy-efficient resource management in ultra dense small cell networks, optimizing power control and user scheduling to improve energy efficiency and reduce outages.

Contribution

It presents a novel combination of Lyapunov optimization and mean field theory to derive low-complexity control policies for dense small cell networks.

Findings

Achieves up to 18.1% energy efficiency gains.

Reduces network outage probability by 98.2%.

Provides a scalable solution for large UDN deployments.

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 power, in ultra dense small cell networks (UDNs). To address this problem, a dynamic stochastic game (DSG) is formulated between small cell base stations (SBSs). This game enables to capture the dynamics of both 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 two low-complexity tractable partial differential equations. 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 18:1% gains in EE and 98.2% reductions in the network's outage probability compared to a baseline model.