面向5G的下行多天线传输关键技术研究

发布时间：2018-03-10 19:19

本文选题：大规模MIMO　切入点：量化预编码　出处：《电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：为了满足未来无线数据传输爆炸式的增长,5G(第五代移动通信技术)移动通信系统对频谱效率和能耗效率提出了更高的要求。在5G关键技术探索中,大规模MIMO(Massive MIMO)技术能够深度挖掘空间维度无线资源,大幅度提升无线通信系统的频谱效率和能耗效率,脱颖而出,成为支撑未来新一代宽带绿色移动通信最具潜力的研究方向之一。而有关研究工作尚处于起步阶段,存在着具有挑战性的基础理论和关键技术问题有待深入系统的研究。因此需要在此领域展开大规模MIMO无线通信理论和技术研究,包括复杂无线环境中大规模MIMO信道模型和信道容量分析、信道状态信息获取理论和技术等。本文首先介绍了多天线技术的演进及下行多用户MIMO关键技术,探讨了新型的多天线技术在信息理论容量、下行预编码技术、空间信道模型、信道估计、信道检测等方面的研究现状,以及大规模MIMO在这些关键技术问题上面临的挑战。然后对大规模MIMO下行有限反馈预编码技术展开研究,提出了优化信道状态信息获取的方法和有效的用户选择算法。所提的优化信道状态信息获取的方法通过对码本进行子采样和预编码索引值重排,能够有效地减少反馈的开销,降低反馈信道传输发生差错所带来的性能损失。同时针对大规模MIMO发射天线数达到一定数量级,传统的用户选择算法复杂度呈指数性增加的问题,提出了基于量化预编码的用户选择算法。该算法利用凸优化的方法获取了一组最佳的用户集,充分考虑了用户选择算法复杂度问题和系统性能等问题,适用于大规模MIMO。考虑到搭建更接近实际的信道模型对大规模MIMO传输技术的研究具有重要影响。因为只有对MIMO信道进行了足够精确的建模,建立精确的无线信道模型来描述MIMO信道特性,才能更准确的对新技术进行链路级或系统级的仿真。因此本文除了从下行预编码方面的优化来适应大规模MIMO的发展,本文还从信道建模方面着手,搭建了更能描述实际信道情况的新信道模型。该信道模型充分考虑了垂直维度的信息,扩展了当前2D信道模型,完成了3D信道建模,并对该信道模型从下列指标进行了校验,包括用户分布、耦合损耗,宽带信干噪比以及特定天线配置下的信道特征值分布情况。这种新的信道模型,实现了3维空间的信道建模,能够更精确的描述实际信道,有利于用来评估大规模MIMO技术。
[Abstract]:In order to meet the future wireless data transmission explosive growth of 5G (fifth generation mobile communication technology) mobile communication system put forward higher requirements for spectrum efficiency and energy efficiency. Large-scale MIMO(Massive Mimo technology can deeply mine the space dimension wireless resources, greatly improve the spectrum efficiency and energy efficiency of wireless communication system, stand out from the crowd. It has become one of the most promising research directions to support a new generation of broadband green mobile communication in the future. However, the related research work is still in its infancy. There are some challenging basic theories and key technical problems to be deeply and systematically studied, so it is necessary to carry out large-scale MIMO wireless communication theory and technology research in this field. It includes large-scale MIMO channel model and channel capacity analysis in complex wireless environment, channel state information acquisition theory and technology. Firstly, this paper introduces the evolution of multi-antenna technology and the key technologies of downlink multi-user MIMO. The research status of new multi-antenna technology in information theoretical capacity, downlink precoding, spatial channel model, channel estimation, channel detection and so on is discussed. And the challenges faced by large-scale MIMO in these key technical issues. Then, the limited feedback precoding technology for large-scale MIMO downlink is studied. This paper presents a method of optimizing channel state information acquisition and an effective user selection algorithm. The proposed method can effectively reduce the cost of feedback by subsampling the codebook and rearranging the precoded index value. In order to reduce the performance loss caused by the error in the feedback channel transmission, the traditional user selection algorithm complexity increases exponentially in view of the fact that the number of large-scale MIMO transmit antennas reaches a certain order of magnitude. A user selection algorithm based on quantization precoding is proposed, which uses convex optimization method to obtain a set of optimal user sets, taking into account the complexity of user selection algorithm and system performance. Considering that building a more realistic channel model has an important impact on the research of large-scale MIMO transmission technology, because only the MIMO channel is modeled accurately enough, Only by establishing an accurate wireless channel model to describe the characteristics of MIMO channel, can the new technology be simulated at link or system level more accurately. Therefore, in addition to optimizing downlink precoding, this paper adapts to the development of large-scale MIMO. From the aspect of channel modeling, a new channel model is built to describe the actual channel situation. The channel model takes full account of the information of vertical dimension, extends the current 2D channel model, and completes the 3D channel modeling. The channel model is verified from the following parameters, including user distribution, coupling loss, wideband signal-to-noise ratio and channel eigenvalue distribution under a particular antenna configuration. The channel modeling in 3D space is realized, which can describe the actual channel more accurately and is helpful to evaluate the large-scale MIMO technology.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5

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