大规模MIMO信道估计与绿色能效研究
发布时间：2018-11-23 07:32
【摘要】：在移动互联网和物联网两大趋势的推动下,移动数据业务急剧增加、频谱资源日趋紧缺、能源消耗快速增长对下一代移动通信系统,即5G系统,提出了更高的要求。作为5G的一种关键技术,大规模MIMO系统通过在基站侧配置大规模天线阵列可以显著地提升系统的频谱效率和功率效率。本论文针对大规模MIMO系统传输技术中存在的一些问题进行了研究,具体研究内容如下:首先,大规模MIMO基站侧庞大的天线数导致信道瞬时状态信息提取复杂度较高。本文利用无线信道状态信息的稀疏性,在传统DFT信道估计算法的基础上加以改进,提出了一种适用于大规模MIMO系统的低复杂度稀疏信道估计算法,仿真结果表明该算法可获得接近MMSE信道估计的性能。其次,在大规模MIMO系统中,随着基站侧天线数的增加,系统容量几乎完全受限于相邻小区的导频复用,这是系统设计最严峻的挑战。本文通过研究多小区多用户系统的导频污染特性,得出最严重的导频污染来源于采用相同导频的小区边缘处用户,利用不同用户统计状态信息非正交的特点,提出了一种基于用户与基站之间距离的智能导频分配方案。理论分析表明该方案可以使系统下行链路的信号噪声干扰比收敛于最优,仿真结果也表明该方案可以有效地提升大规模MIMO系统下行链路的性能。最后,大规模MIMO基站侧天线使用大量的A/D转换器对信号进行量化,其量化精度严重地影响了系统的能量效率。本文根据上行链路接收端A/D转换器的量化精度与系统能量损耗以及信息损失之间的关系,建立大规模MIMO系统量化模型,推导了A/D转换器的量化比特数和基站侧天线数与系统频谱效率以及能量效率之间的关系表达式。在保证能量效率最大的基础上,提出了一种基于PSO算法的最佳天线选择分组量化方案。仿真结果表明,低精度量的A/D转换器可以使系统能量效率达到最大,并且当系统采用PSO算法优化出来的最佳组合值时,无论相邻小区的大尺度衰落系数如何变化,系统的鲁棒性最好。
[Abstract]:Driven by the trends of mobile Internet and Internet of things, mobile data services increase rapidly, spectrum resources become increasingly scarce, and the rapid growth of energy consumption puts forward higher requirements for the next generation mobile communication system, that is, 5G system. As a key technology of 5G large scale MIMO systems can significantly improve the spectral efficiency and power efficiency of the system by configuring large scale antenna arrays on the base station side. In this paper, some problems in large-scale MIMO transmission technology are studied. The main contents are as follows: first, the large number of antennas in the large scale MIMO base station results in high complexity of extracting the instantaneous state information of the channel. In this paper, a low complexity sparse channel estimation algorithm for large scale MIMO systems is proposed, which is improved on the basis of the traditional DFT channel estimation algorithm by using the sparsity of wireless channel state information. Simulation results show that the proposed algorithm can achieve performance close to MMSE channel estimation. Secondly, in large-scale MIMO systems, with the increase of the number of antennas on the base station side, the system capacity is almost completely limited by the pilot multiplexing of adjacent cells, which is the most serious challenge in system design. By studying the pilot pollution characteristics of multi-cell multi-user system, it is concluded that the most serious pilot pollution comes from the users at the edge of the cell using the same pilot frequency, and makes use of the non-orthogonal characteristics of different users' statistical state information. An intelligent pilot allocation scheme based on the distance between the user and the base station is proposed. Theoretical analysis shows that the proposed scheme can converge to the optimal signal-to-noise ratio of the downlink, and the simulation results show that the scheme can effectively improve the downlink performance of large-scale MIMO systems. Finally, a large number of A / D converters are used to quantify the signal in the MIMO base station side antenna. The quantization accuracy seriously affects the energy efficiency of the system. Based on the relationship between the quantization accuracy of the uplink receiver and the energy loss and information loss of the system, a large scale quantization model of MIMO system is established in this paper. The relationship between the quantization bit number of A / D converter and the number of antennas on the base station and the spectral efficiency and energy efficiency of the system is derived. Based on the maximum energy efficiency, an optimal antenna selection grouping quantization scheme based on PSO algorithm is proposed. The simulation results show that the low precision A / D converter can maximize the energy efficiency of the system, and when the optimal combination value of PSO algorithm is adopted, no matter how the large-scale fading coefficient of adjacent cells changes, The system has the best robustness.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3
本文编号：2350782
[Abstract]:Driven by the trends of mobile Internet and Internet of things, mobile data services increase rapidly, spectrum resources become increasingly scarce, and the rapid growth of energy consumption puts forward higher requirements for the next generation mobile communication system, that is, 5G system. As a key technology of 5G large scale MIMO systems can significantly improve the spectral efficiency and power efficiency of the system by configuring large scale antenna arrays on the base station side. In this paper, some problems in large-scale MIMO transmission technology are studied. The main contents are as follows: first, the large number of antennas in the large scale MIMO base station results in high complexity of extracting the instantaneous state information of the channel. In this paper, a low complexity sparse channel estimation algorithm for large scale MIMO systems is proposed, which is improved on the basis of the traditional DFT channel estimation algorithm by using the sparsity of wireless channel state information. Simulation results show that the proposed algorithm can achieve performance close to MMSE channel estimation. Secondly, in large-scale MIMO systems, with the increase of the number of antennas on the base station side, the system capacity is almost completely limited by the pilot multiplexing of adjacent cells, which is the most serious challenge in system design. By studying the pilot pollution characteristics of multi-cell multi-user system, it is concluded that the most serious pilot pollution comes from the users at the edge of the cell using the same pilot frequency, and makes use of the non-orthogonal characteristics of different users' statistical state information. An intelligent pilot allocation scheme based on the distance between the user and the base station is proposed. Theoretical analysis shows that the proposed scheme can converge to the optimal signal-to-noise ratio of the downlink, and the simulation results show that the scheme can effectively improve the downlink performance of large-scale MIMO systems. Finally, a large number of A / D converters are used to quantify the signal in the MIMO base station side antenna. The quantization accuracy seriously affects the energy efficiency of the system. Based on the relationship between the quantization accuracy of the uplink receiver and the energy loss and information loss of the system, a large scale quantization model of MIMO system is established in this paper. The relationship between the quantization bit number of A / D converter and the number of antennas on the base station and the spectral efficiency and energy efficiency of the system is derived. Based on the maximum energy efficiency, an optimal antenna selection grouping quantization scheme based on PSO algorithm is proposed. The simulation results show that the low precision A / D converter can maximize the energy efficiency of the system, and when the optimal combination value of PSO algorithm is adopted, no matter how the large-scale fading coefficient of adjacent cells changes, The system has the best robustness.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3
【参考文献】
相关期刊论文 前1条
1 吴昌友;王福林;马力;;一种新的改进粒子群优化算法[J];控制工程;2010年03期
，本文编号：2350782
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