大规模MIMO容量优化算法

发布时间：2018-07-06 15:44

本文选题：大规模MIMO + 空间自由度　；参考：《西安电子科技大学》2014年硕士论文

【摘要】：相对传统多输入多输出（MIMO）系统而言，大规模MIMO系统可以通过采用数目庞大的多天线阵列获得较大的分集增益，使系统的容量和能效获得很大提升，是第5代（5G）移动通信系统的核心技术之一。本文围绕大规模MIMO技术展开研究，首先介绍了大规模MIMO系统的关键技术，包括大规模MIMO下线性多用户预编码，接收端信号检测算法和采用2D天线阵列的3D波束赋形。接着，，重点分析了大规模MIMO多用户场景的系统容量，引入了空间自由度（DOF），推导了一定空间自由度下的近似系统遍历容量解析表达式。接着，在此基础上，研究了基于容量最大化的用户数优化问题，提出一种基于二分法的大规模MIMO用户数优化算法，并进行仿真分析。最后，针对大规模MIMO采用线阵尺寸过于庞大的问题，给出了采用2D天线阵的3D波束赋形技术方法，通过粒子群算法（PSO）优化波束下倾角并进行波束的功率分配，在保证低复杂度的前提下优化了系统容量。
[Abstract]:Compared with traditional multiple-input multiple-output (MIMO) systems, large scale MIMO systems can obtain large diversity gains by using a large number of multi-antenna arrays, which can greatly improve the capacity and energy efficiency of the systems. It is one of the core technologies of the 5 th generation (5 G) mobile communication system. This paper focuses on large-scale MIMO technology. Firstly, the key technologies of large-scale MIMO system are introduced, including linear multiuser precoding, receiver signal detection algorithm and 3D beamforming using 2D antenna array. Then, the system capacity of large scale MIMO multi-user scenarios is analyzed, and the spatial degree of freedom (DOF) is introduced, and the analytical expression of the approximate system ergodic capacity under a certain degree of spatial freedom is derived. Then, on the basis of this, the problem of optimizing the number of users based on capacity maximization is studied, and a large scale MIMO user number optimization algorithm based on dichotomy is proposed, and the simulation analysis is carried out. Finally, a 3D beamforming method based on 2D antenna array is proposed to solve the problem that the large scale MIMO adopts linear array. The particle swarm optimization (PSO) algorithm is used to optimize the beam-inclination angle and the power distribution of the beam-beam. The system capacity is optimized on the premise of low complexity.
【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN919.3

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