Massive MIMO系统能效优化关键技术研究

发布时间：2018-02-21 09:44

本文关键词： Massive MIMO 能效功率分配技术天线选择技术凸优化　出处：《郑州大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着无线通信的发展,移动用户对通信网络的数据传输速率要求越来越高。Massive MIMO系统通过在基站配置大规模天线,可在不增加额外系统带宽的情况下,显著的提高系统的速率,因而成为下一代移动通信系统的关键技术。但是Massive MIMO系统的射频模块成本和能源损耗随基站天线数的增加而增加。为了满足人们对绿色通信的需求,研究兼顾信号传输速率和系统功耗的能效优化技术具有更加重要的实际意义。天线选择和功率分配技术是Massive MIMO系统提高能效的两种重要技术,但在以往的研究中,能效优化以找到最优的发射天线子集为目的,忽略了发射天线数和发射功率对能效的影响,而功率分配时,通常假设基站为每个用户平均分配功率,没有对用户不等功率分配时的能效进行分析。针对这些问题,本文做了更深一步的研究,论文的主要工作如下:1、在Massive MIMO系统中,联合研究发射天线数和发射功率对能效的影响,证明了系统存在最优的发射功率和发射天线数使能效最大,进而提出了一种功率分配和天线选择联合优化算法求解最优值。主要思想为,文中建立了同时考虑发射功率和电路功率的系统功耗模型,在此模型上,讨论基站发射天线数和用户数对功耗的影响。然后以能效最大化为优化目标,证明了能效分别是关于发射天线数和发射功率的凸函数,并利用凸优化理论提出了联合优化算法求解最优的发射功率和发射天线数。仿真结果证明了最优解的存在性以及该联合优化算法提高能效的有效性。2、针对Massive MIMO下行链路系统能效优化问题,研究用户不等功率分配时的能效优化方案,并提出了新的用户功率分配算法。文中首先建立了在用户不等功率分配时的能效优化模型,并证明了能效是关于用户功率的凸函数。然后讨论了无功率限制和有功率限制两种场景下的功率分配问题,在无功率限制下提出了功率迭代分配算法求解用户功率的最优值。在有功率限制场景下,利用拉格朗日优化得到用户功率最优值的近似表达式。仿真结果说明,与等功率分配算法相比,本文提出的优化算法能更大程度的提高系统的能效。
[Abstract]:With the development of wireless communication, mobile users require more and more data transmission rate of the communication network. Massive antennas can be deployed in the base station, which can significantly improve the system speed without increasing the additional system bandwidth. So it becomes the key technology of the next generation mobile communication system, but the cost of RF module and energy loss of Massive MIMO system increase with the increase of the number of base station antennas. It is of great practical significance to study the energy efficiency optimization technology which takes both signal transmission rate and system power consumption into account. Antenna selection and power allocation are two important technologies for improving energy efficiency in Massive MIMO systems. Energy efficiency optimization aims to find the optimal subset of transmit antennas, ignoring the effect of the number of transmit antennas and the transmission power on energy efficiency, and when power allocation is performed, it is usually assumed that the base station allocates power to each user equally. In view of these problems, this paper has done a further study. The main work of this paper is as follows: 1. In Massive MIMO system, the effects of transmit antenna number and transmit power on energy efficiency are studied jointly. It is proved that the energy efficiency is maximized by the existence of optimal transmission power and number of transmit antennas, and a joint optimization algorithm of power allocation and antenna selection is proposed to solve the optimal value. In this paper, a power consumption model considering both transmit power and circuit power is established. In this model, the effects of the number of transmit antennas and the number of users on power consumption are discussed, and the optimization goal is to maximize energy efficiency. It is proved that energy efficiency is a convex function of the number of transmitting antennas and the transmitting power, respectively. Using convex optimization theory, a joint optimization algorithm is proposed to solve the optimal transmit power and the number of transmit antennas. The simulation results prove the existence of the optimal solution and the effectiveness of the joint optimization algorithm to improve energy efficiency. Under Massive MIMO, the proposed algorithm is effective in improving energy efficiency. Energy efficiency optimization of line link systems, The energy efficiency optimization scheme for users with unequal power allocation is studied, and a new user power allocation algorithm is proposed. In this paper, the energy efficiency optimization model for users with unequal power allocation is first established. It is proved that energy efficiency is a convex function of user power. An iterative power allocation algorithm is proposed to solve the optimal value of user power without power constraints. In the scenario with power limitation, the approximate expression of the optimal value of user power is obtained by Lagrangian optimization. The simulation results show that, Compared with the equal-power allocation algorithm, the proposed optimization algorithm can improve the energy efficiency of the system to a greater extent.
【学位授予单位】：郑州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3

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