2013/03/13 by Xianfu Chen, Chen, Xianfu, Honggang Zhang +7
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Computer Science and Game Theory (cs.GT) #Cooperative Communication and Network Coding #FOS: Computer and information sciences #ICT Impact and Policies #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.1303.4638
openalex publication_date 2013/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the efficient solutions of improving coverage and increasing capacity in cellular networks is the deployment of femtocells. As the cellular networks are becoming more complex, energy consumption of whole network infrastructure is becoming important in terms of both operational costs and environmental impacts. This paper investigates energy efficiency of two-tier femtocell networks through combining game theory and stochastic learning. With the Stackelberg game formulation, a hierarchical reinforcement learning framework is applied for studying the joint expected utility maximization of macrocells and femtocells subject to the minimum signal-to-interference-plus-noise-ratio requirements. In the learning procedure, the macrocells act as leaders and the femtocells are followers. At each time step, the leaders commit to dynamic strategies based on the best responses of the followers, while the followers compete against each other with no further information but the leaders' transmission parameters. In this paper, we propose two reinforcement learning based intelligent algorithms to schedule each cell's stochastic power levels. Numerical experiments are presented to validate the investigations. The results show that the two learning algorithms substantially improve the energy efficiency of the femtocell networks.