Classified Index: TN929.5
U.D.C: 654
Dissertation for the Master Degree in Engineering
THE RESEARCH ON INTERFERENCE ALIGNMENT FOR MULTIUSERS MIMO NETWORK VIA MAX-MIN SINR
Candidate：Li Yang
Supervisor：Prof. Zhilu Wu
Academic Degree Applied for：Master of Engineering Speciality：Information and Communication
Engineering日产总统型房车
Affiliation：School of Electronics and Information
绝世好简历Engineering
Date of Defence：July, 2015
Degree-Conferring-Institution：Harbin Institute of Technology
哈尔滨工业大学工学硕士学位论文
摘要
热处理手册多用户多输入多输出(Multiple Input Multiple Output, MIMO)无线网络中一个棘手的问题就是存在多用户干扰，干扰对齐(Interference Alignment, IA)技术是一个高效的干扰抑制新措施。然而，多用户MIMO网络中存在用户间资源分配不均匀的现象，导致某些用户的通信质量差，甚至达不到通信要求。本文将利用最大化最小信干噪比(Maximize the Minimum Signal-to-Interference-plus-Noise Ratio，Max-Min SINR)解决这种资源分配不均匀的问题。首先，本文对IA进行分析，并建立了一般的基于Max-Min SINR IA模型。对干扰对齐中的预编码矩阵和干扰抑制矩阵的设计做了说明，并研究了功率分配的原理，为后文的基于Max-Min SINR 的干扰对齐方法提供基础。 其次，对多用户MIMO对称网络进行研究。分析了MIMO对称网络中典型SINR优化的IA方法，包括最小干扰泄漏算法(Minimum Leakage Interference，MLI)和精确循环协调上升算法(Exact Cyclic Coordinate Ascent Algorithm，ECCAA)。其中MLI算法是抑制干扰泄漏的典型IA方法，ECCAA算法是Max-Min SINR优化的典型IA方法。针对MIMO对称网络中运用IA进行干扰抑制时会产生用户间资源分配不均匀的问题，结合功率分配策略，采用了一种基于Max-Min SINR联合优化的IA方法。并根据算法 特点，设计了一种效用函数，来评价各用户分得资源的公平性。并对以上MIMO对称网络中三种IA方法进行实验仿真，仿真结果表明，相比于MLI和ECCAA算法，基于Max-Min SINR联合优化的IA方法随着SNR 的增大不仅能更好的提高系统中最小SINR的值，其算法收敛程度还更快，并且效用率更高，但其和速率仍有待提高。
最后，本文对接近实际通信网络的多用户MIMO非对称网络进行了分析。研究了分组IA算法，解决用户数超过IA可行性上限时的干扰抑制问题。本文分析了软分组算法，并将MLI算法改进后，与之相结合。结合博弈论给出了严格位势博弈模型，建立了基于Max-Min SINR博弈准则的IA方法。仿真结果表明，相比于软分组算法和改进的MLI算法结合后的IA方法，基于Max-Min SINR博弈准则的干扰对齐方法能够更好的提高系统中最小SINR的值，并获得较高的和速率，但当分组后，用户数接近IA可行性上限时，效用率较低。
关键词：MIMO；干扰对齐；最大化最小信干噪比
哈尔滨工业大学工学硕士学位论文
Multiusers interference is the difficult problem in multiusers Multiple Input Multiple Output wireless network. Interference alignment (IA) is a novel method for interference suppression. While there exist the phenomenon of unfairness of resources, the quality of service of multiusers is poor, which even c
an’t meet the requirements o f communication. We will apply the Maximize the Minimum Signal to Interference plus Noise Ratio (Max-Min SINR) strategy to solve this problem. Firstly，we study interference alignment, then establish the Max-Min SINR optimization model for IA to improve the unfairness of the allocation of resources between multiusers. The design of precoding and interference suppression matrix is explained, and the power allocation is analysed, which provide the basis of Max-Min SINR optimization for IA.
The multiusers MIMO symmetrical network is studied with perfect channel state information. We analyse the typical SINR optimization IA algorithm in MIMO symmetrical network, include MLI algorithm and ECCAA. MLI algorithm is a typical interference leakage suppression method for IA, while ECCAA is a representative Max-Min SINR optimized means for IA. A joint optimization combining power allocation for IA using Max-Min SINR is proposed to improve the unfairness of the allocation of resources between multiusers. A utility function is also presented to evaluate the fairness of resources allocation every users. The simulations are conducted and indicate that the joint optimization combining power allocation for IA using Max-Min SINR can not only increase the minimum SINR of the system, but also obtain fast convergence rate and receive highest utility ratio, at the cost of sum rate.
Then, we study the multiusers MIMO asymmetrical network which is get close to the practical communication system. The clustering for IA is discussed to solve the interference suppression problem when the number of users exceeds the feasibility of IA. The soft cluster algorithm is provided and next combined with the modified MLI. According with the game theory, strict potential game model is raised, and a method for IA based on the Max-Min SINR game rules is proposed in MIMO asymmetrical network. Simulations is presented and show that the IA method based on the Max-Min SINR game rules can better improve the minimum SINR of the MIMO system, and achieve higher sum rate in comparison with the combination of soft cluster and modified MLI. When the number of users approximate the feasibility of IA after clustering, the utility ratio of IA method based on the Max-Min SINR game rules is lower.
Keywords:multiple input and multiple output communication system, interference alignment, Maximize the Minimum Signal to Interference plus Noise Ratio
哈尔滨工业大学工学硕士学位论文
目录
摘要 .................................................................................................................. I ABSTRACT ................
........................................................................................ II
第1章绪论 (1)
1.1 课题背景及研究的目的和意义 (1)rvr
1.2 国内外研究现状及分析 (2)
1.2.1 多用户MIMO通信技术的研究现状 (2)
1.2.2 干扰对齐的研究现状 (4)
1.2.3 最大化最小SINR的研究现状 (5)
1.3 论文主要研究内容 (7)
第2章多用户MIMO网络中基于SINR的干扰对齐 (9)
2.1干扰对齐技术及最大最小公平性问题 (9)
2.1.1 系统模型 (9)
2.1.2 干扰对齐技术 (10)
2.1.3 最大化最小SINR (11)
2.2 预编码矩阵和干扰抑制矩阵设计 (11)
2.3 功率分配 (12)
2.4 本章小结 (12)
第3章MIMO对称网络中MAX-MIN SINR优化 (14)
3.1MIMO对称网络中基于Max-Min SINR的干扰对齐研究 (14)
3.1.1 MIMO对称干扰网络特点 (15)
3.1.2 对称信道的互易性 (17)
3.2MIMO对称网络中典型SINR优化的干扰对齐方法 (18)
3.2.1 最小干扰泄漏算法 (18)
3.2.2 精确循环协调上升算法 (20)
3.3 MIMO对称网络中Max-Min SINR联合优化的干扰对齐方法 (21)
3.4 效用函数 (25)
3.5仿真结果及分析 (26)
3.5.1 最小信干噪比平均值的分析 (26)
3.5.2 和速率分析 (29)
哈尔滨工业大学工学硕士学位论文
3.5.3 收敛速度分析 (31)
3.5.4 效用率分析 (34)
矩阵干扰3.6本章小结 (36)
第4章MIMO非对称网络中MAX-MIN SINR优化 (37)
4.1 MIMO非对称网络中基于Max-Min SINR的干扰对齐研究 (37)
4.1.1 MIMO非对称干扰网络的特点 (38)
4.1.2 分组干扰对齐 (38)
4.2 MIMO非对称网络中典型SINR优化的干扰对齐方法 (40)
4.2.1 软分组算法 (40)
4.2.2 改进的MLI算法 (42)
4.3基于Max-Min SINR严格位势博弈准则的干扰对齐方法 (43)
4.3.1 严格位势博弈模型 (43)
4.3.2基于Max-Min SINR博弈准则的干扰对齐 (45)
4.4仿真结果及分析 (46)
4.4.1 最小信干噪比的平均值分析 (47)
4.4.2和速率分析 (51)
4.4.3 效用率分析 (54)
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4.5 本章小结 (55)
结论 (56)
参考文献 (58)
攻读硕士学位期间取得的研究成果 (63)
哈尔滨工业大学学位论文原创性声明和使用权限 (64)
致谢 (65)
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