超密集异构网中基于配对理论的用户关联算法

超密集异构网络作为提升网络数据流量和用户速率的有效手段,成为5G关键技术中的一员。但超密集部署的小小区基站势必会导致系统总能耗的增加,以及用户接入公平性等问题。解决给定目标速率条件下基于能效优化的用户关联(也称为用户接入)问题。改进了原有的功耗优化模型,使之更准确地反映基站的实际发射功率和总功耗。在此基础上,利用拉格朗日对偶和二分法对关联过程中用户分配到的资源比例和基站为每个关联用户提供的发射功率进行优化和调节;同时结合配对理论,提出一种综合考虑能效和接入公平度的用户关联算法。性能仿真和对比结果表明,算法在保持良好的用户接入公平性的基础上有效提升了系统能效。

A User Association Algorithm Based on Matching Theory in Ultra-dense Heterogeneous Networks

Ultra-dense Heterogeneous network, as an effective means to meet the needs of exponential growth in data traffic and high user rate, has become a member of 5G''s key technologies. While ultra-densely deployed small cell base stations will inevitably result in increased power consumption and access fairness problem. The problem of user association (also called user access) with energy efficiency(EE) optimization under given rate demands was investigated in this paper. The original power consumption model was improved to make it more accurately reflect the actual transmit power and whole power consumption of the base station. And then, Lagrange duality and bisection method were used to optimize and adjust the proportion of resource allocated to users and the transmit power provided by the base station for each associated user. Combined with matching theory, a user association algorithm considering both EE and access fairness was proposed. Performance simulation and comparison results show that the proposed approach performs well in access fairness and effectively improves EE of the system.