超密集异构无线网络中的联合干扰管理方法研究

To satisfy the requirement of enormous wireless data service, increasing the efficiency of spectrum reuse by the interference management in the ultra-dense heterogeneous wireless network is one of the important research areas. In the interference management, resource allocation and interference alignment (IA) have its own strengths and limitations, respectively. Therefore, the joint interference management to mitigate interference further in ultra-dense heterogeneous wireless network has been shown as a promising mechanism. However, the diversity and the time variation of the large-scale network topology, the feasible constraint of IA and the consistency of resource occupation of participant users in IA have become the key bottleneck for the techniques in the joint interference management, including the selection of IA participant users, the scheduling of IA and the resource allocation. Therefore, the joint interference management for the ultra-dense heterogeneous wireless network is studied in this project. First, the combination representation model of the decision and interference based on the multi-scheme cooccurrence interference graph is constructed, which can represent the diversity of interference and the correlation between resource allocation and IA. Second, the techniques including the selection of IA participant users, the scheduling of IA and the resource allocation are proposed, which are based on the decomposition in chordal graph and the analysis of the characteristic network topology with the efficient decision making. At last, the cognitive joint interference management is designed to improve the network performance according to the analysis on the time variation on the interference environment.

为满足激增的业务需求，运用超密集异构无线组网技术并通过干扰管理增强有效频谱复用是重要研究领域之一。资源分配与干扰对齐（Interference alignment, IA）作为两种干扰管理方法，各自具有优势与限制，因而通过联合干扰管理进一步充分消除干扰成为重要的研究方向。然而，网络拓扑的大规模异构时变特性、IA可行性条件与IA用户资源分配的一致性约束成为了联合干扰管理中IA组用户选择、IA组调度与资源分配等问题需要突破的关键瓶颈。为此，本项目将研究超密集异构无线网络中的联合干扰管理机制。首先，揭示异构干扰中资源分配与IA的关联机理，构造基于多策略共现干扰图的决策-干扰联合表征模型。其次，分析不同干扰管理方案高效决策对应的网络拓扑特征，利用弦图理论进行降耦分解，解决IA组用户选择、IA组调度与资源分配等技术挑战。最后，进一步探索时变性对干扰环境的影响，设计基于认知的联合干扰管理机制。

为满足激增的业务需求，运用超密集异构无线组网技术并通过干扰管理增强有效频谱复用是重要研究领域之一。本项目将干扰对齐、管理负荷优化、连通性保障以及稳定拓扑分析等多种技术联合干扰管理进行深入研究。主要研究内容包含：超密集网络中联合干扰对齐与资源分配的干扰管理研究，设计基于多策略共现干扰图的决策-干扰联合表征模型，并进行优化资源分配；超密集网络中联合网络重配置负荷优化的干扰管理研究，对大规模密集网络中针对复杂网络进行统计学建模，使用随机几何理论实现了对网络中的层内及层间干扰的定量分析，并进行减少网络负荷的资源配置；动态密集网络中联合连通性保障的干扰管理研究，针对动态的网络，使用图论构造了融合干扰与资源分配问题的网络连通模型，提出了一种基于最小生成树的资源分配方法，以消除网络中的干扰，保障动态网络连通性；动态大规模超密集网络中联合稳定子图分析的干扰管理研究，针对动态网络，设计基于图相似性的干扰拓扑最大稳定子图提取方法，从而实现减少干扰冲突的资源分配。作为基础研究，本项目将多种技术联合干扰管理的方法应用到密集网络中，设计出高效可靠的干扰管控方案，使其能够在有限的资源下增强网络的连通性、满意用户数、可靠性等服务性能，从而保证密集网络的高效运行。随着各种信息业务的飞速发展，资源的有限性成为制约网络性能发展的重要因素之一。为了通过发掘复用潜能，控制干扰带来的信息损耗，本项目研究成果可以为解决密集网络的部署问题，为智能交通、智能安防、智能医疗、可穿戴设备等物联网应用的做出理论铺垫。