多小区大规模MU-MIMO能效优化方案

发布时间：2018-02-25 05:17

本文关键词： Massive MIMO 能效优化方案下行链路交替迭代　出处：《河北大学》2017年硕士论文　论文类型：学位论文

【摘要】：大规模多输入多输出(Multiple-Input Multiple-Output,Massive MIMO)技术作为未来移动通信中的一项潜在的关键技术而备受人们关注;该技术是传统MIMO技术的延伸,它通过在基站侧配置超大规模的天线阵列,较传统MIMO利用天线数量上的优势,进一步挖掘了空间维度上的资源,并在同一时频资源块内服务更多用户,显著提高了系统的能量效率和频率效率。但是,在Massive MIMO系统中,需要在基站侧配置与发射天线相同的射频链路,当开启所有天线进行数据传输时,不仅电路功耗大,并且有很高的传输复杂度,从能效角度考虑,也不能达到系统最优能效。激活基站侧的部分天线并调节发射功率,能够降低功耗与传输复杂度的同时实现较高能效,满足当今社会节能环保的需求。本文也以此为依据,针对Massive MIMO的能效优化问题做了进一步的研究与分析。首先,本文对Massive MIMO的相关知识进行了介绍,包括Massive MIMO的基本应用、相关预编码技术;随后对单用户与多用户两种系统模型进行了分析,验证了Massive MIMO系统的优势及线性预编码的的可行性,并详细介绍了这两种系统模型下的一些现有能效优化方案。然后,具体分析并验证了一种针对单小区多用户Massive MIMO系统下行链路的能效优化方案,该方案基于天线选择算法,求出使能效最大化的最优天线数,并说明当天线数很大的情况下随机天线选择算法性能接近最优。经过仿真分析,与基站侧天线全部开启相比,该方案能够显著提高系统能效,但通过参数变化对比看出,该方案仅考虑了发射天线数目的优化,而没有考虑基站侧发射功率调节等问题。最后,提出了一种基于多小区多用户Massive MIMO下行链路的能效优化方案,本方案在采用随机天线选择算法的前提下,结合分数规划法,推导出发射功率、天线数与能效的关系式,并给出了一种发射功率与激活天线数交替迭代的能效优化方案。最后仿真结果表明,与基站侧所有天线进行传输相比,本方案在能效方面具有很大的提升,并且与原方案相比,也具有一定的优势。
[Abstract]:Large-scale Multiple-Input Multiple-Output massive MIMO (Multiple-Input Multiple-Output massive MIMO) technology, as a potential key technology in future mobile communications, is an extension of traditional MIMO technology, which is extended by the deployment of large antenna arrays on the base station side. Compared with the traditional MIMO, the advantages of antenna number are used to further excavate the resources in the spatial dimension, and to serve more users in the same time-frequency resource block, which significantly improves the energy efficiency and frequency efficiency of the system. However, in the Massive MIMO system, the energy efficiency and frequency efficiency of the system are greatly improved. It is necessary to configure the same RF link on the base station side as the transmitting antenna. When all the antennas are turned on for data transmission, not only the power consumption of the circuit is high, but also the transmission complexity is very high, which is considered from the point of view of energy efficiency. It can not achieve the optimal energy efficiency of the system. By activating some antennas on the base station side and adjusting the transmission power, it can reduce the power consumption and the transmission complexity and achieve higher energy efficiency, which can meet the needs of energy saving and environmental protection in today's society. The energy efficiency optimization of Massive MIMO is further studied and analyzed. Firstly, the related knowledge of Massive MIMO is introduced, including the basic application of Massive MIMO and the related precoding technology. The advantages of Massive MIMO system and the feasibility of linear precoding are verified, and some existing energy efficiency optimization schemes under these two system models are introduced in detail. An energy efficiency optimization scheme for single cell multi-user Massive MIMO downlink is analyzed and verified. The scheme is based on antenna selection algorithm to obtain the optimal number of antennas to maximize energy efficiency. When the number of antennas is very large, the performance of the random antenna selection algorithm is close to the optimum. Through simulation analysis, compared with the base station antenna open, the scheme can significantly improve the energy efficiency of the system, but through the comparison of the parameters, it can be seen that, The scheme only considers the optimization of the number of transmit antennas, but not the power regulation of the base station side. Finally, an energy efficiency optimization scheme based on multi-cell and multi-user Massive MIMO downlink is proposed. Based on the random antenna selection algorithm and the fractional programming method, the relationship between transmit power, antenna number and energy efficiency is derived. Finally, the simulation results show that compared with the transmission of all antennas on the base station side, the scheme has a great improvement in energy efficiency, and compared with the original scheme. Also has certain superiority.
【学位授予单位】：河北大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3

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