基于平均场博弈的超密集5G小蜂窝网络下行功率管理

研究了一个拥有大量小型基站(Small Base Stations,SBSs)和用户设备(User Equipments,UEs)的 超密集 5G 小蜂窝网络(Ultra-Dense 5G Small Cellular Networks,UDN)的下行通信系统,并考虑了功率控制策 略和队列状态信息(Queue State Information,QSI)的影响,研究了 SBSs 与其服务用户之间数据成功传输的概 率和 SBSs 传输能量消耗问题。 考虑 SBSs 之间的交互关系,将该问题建模为一个动态随机博弈(Dynamic Stochastic Game,DSG)。 通过利用 SBSs 的超密集特性,DSG 被进一步近似为平均场博弈(Mean Filed Game, MFG)。 然后基于 Lax-Friedrichs 方案和拉格朗日松弛法开发有限差分方法来求解相应的 MFG。 在数据传 输之前,通过所求解,每个小型基站可以独立地优化其控制策略从而最小化成本,而无需与其他 SBS 进行任 何信息交换。 仿真结果表明:所提出的控制策略能有效地提高数据传输成功的概率并减小相应成本。

Downlink Power Management of Ultra-dense 5G Small Cellular Networks Based on Mean Field Game

In this paper we study a downlink communication system of ultra-dense 5G small cellular networks UDN with a large number of small base stations SBSs and user equipment UEs and consider the effects of power control strategy and queue state information QSI . The probability of successful data transmission between SBSs and its service users and the energy consumption of SBSs transmission are studied. Considering the interaction between SBSs we model the problem as a dynamic stochastic game DSG . By using the super dense characteristics of SBSs DSG is further approximated as mean file game MFG . Then based on Lax-Friedrichs scheme and Lagrange relaxation method a finite difference method is developed to solve the corresponding MFG. Before data transmission through the solution each small base station can independently optimize its control strategy to minimize the cost without any information exchange with other SBS. Simulation results show that the proposed control strategy can effectively improve the successful probability of transmission and reduce the corresponding cost.