面向IMT-A的femtocell绿色自组织关键问题研究

The main subject of our project is IMT-Advanced femtocell green self-organizing technology, which focused on solving the problem on how to build the model of the allocation of organizational distributed or hybrid (centralized and distributed) resources (sub-channels, power, etc.) of femtocell, and then propose a self-organizing resource allocation algorithm, low-complexity and easy to implement, to maximize the energy efficiency of the system and to ensure the user's quality of service. To implement the above objectives, we have to fulfill the following research: Firstly, we will build the hierarchical energy-efficient Stackelberg Game model for the two-tier network in IMT-Advanced systems, then by using of the distributed processing method to transform the problem of self-organization distribution of the resources into the utility optimization problem for a single base station to reduce the cross-layer interference and increase the energy efficiency; Secondly, we will establish the regional alliance game model based on energy efficiency for the dense deployment Scenarios, through converting the management of resources self-organization to the optimization of the effectiveness of the femtocell network to promote the whole network's energy efficiency; Thirdly, by means of the inherited type Q-learning theory, we will set up the femtocell network learning model to reduce the network signaling overhead and improve the energy efficiency of the whole system; Finally, we can build the interference measurements matrix to achieve the dynamic allocation of the sub-channel by adopting the perception technology to detect the idle or low-interfere sub-channel, to achieve the optimal energy efficiency goals and ensure the user's quality of service.

本课题主要研究如何建立Femtocell分布式自组织资源分配模型，并提出复杂度低、易实施的自组织资源分配算法，为IMT-A系统的射频参数的自配置自优化提供理论依据，实现在保证用户服务质量的同时网络能效最大化。基于以上研究目标，（1）针对Macro-Femto双层网络建立基于凸定价效用的斯塔博克分层博弈的分布式能效模型，将资源自组织分配问题转化为单个基站效用优化问题，实现能效提升的同时降低跨层干扰；（2）针对致密部署区域的Femtocell网络层，建立基于联盟博弈的能效模型，将资源自组织管理问题转化为Femtocell网络的效用优化问题，实现全网总体能效的提升；（3）通过引入Q学习理论，提出建立femtocell网络的继承型学习模型，实现降低网络信令开销，提高系统的能效；（4）通过感知技术检测空闲或干扰小的子信道，建立干扰测量矩阵，实现子信道动态分配，在保证用户QoS的同时实现能效最优。

宏蜂窝网络与small cell网络组成异构无线网络将成为未来移动通信网络的主要形态。随着Smallcell部署数量的增加，整个网络的能量消耗将成为一个巨大的数字。无线资源分配技术会对整个网络的性能产生很大的影响，是目前解决异构无线网络面临问题的一项重要技术。.本课题主要针对建立Femtocell无线资源分配模型，设计低复杂度的自组织无线资源分配算法展开研究，为IMT-A Femtocell/Smallcell系统的无线资源分配参数的自配置自优化提供理论依据，实现在保证用户服务质量的同时实现网络能效最大化。.课题组从Femtocell/Smallcell与Macrocell共用频谱的同频部署、Smallcell独占专用频段的异频部署两个部署场景出发，分别从下行链路的能效优化以及上行链路的能效优化两个方面进行了研究。具体的说，考虑到时延敏感性移动多媒体业务的比重越来越大的趋势，我们引入了新的时延敏感业务效用模型，并基于此模型进行算法设计和优化，实现对于业务的时延QoS保障。同时，考虑到Smallcell/Femtocell网络中干扰严重的问题，针对于不同场景的同层和跨层干扰的问题，我们引入了不同的定价机制，设计了对应的算法，实现了干扰的有效抑制。此外，针对于实现无线资源分配算法的自组织的目标，我们引入了不同的博弈模型，设计了不同的分布式算法。.通过大量的仿真实验发现，本课题中设计的各类算法既可以实现对于网络能效的优化，同时，又可以实现干扰抑制以及用户QoS保障等目标。