基于MF-SIC算法改进的大规模MIMO检测方法研究

发布时间：2018-04-19 05:20

本文选题：大规模MIMO + 信号检测　；参考：《安徽大学》2017年硕士论文

【摘要】：大规模多输入多输出(Massive Multi-Input Multi-Output)技术是5G中的关键技术之一,已经受到了人们的广泛关注,成为当前无线通信领域的重要研究课题之一。相较于传统的MIMO系统,大规模MIMO系统配置了大量的接收天线,其数量远大于MIMO系统,提高了系统的容量和频谱效率。接收端的检测技术作为大规模MIMO系统中的一个重要的组成部分,对大规模MIMO的发展有重要的意义。因此,论文对大规模MIMO中信号的检测算法进行研究,以探求适用于大规模MIMO系统的信号检测算法。论文首先研究了大规模MIMO的系统模型及信号检测技术的基本原理,然后分别研究了线性信号检测算法中的常用的最大比合并检测算法(Maximum Ratio Combining,MRC)、迫零检测算法(Zero Forcing,ZF)、最小均方误差检测算法(Minimum Mean Square Error,MMSE),以及非线性信号检测算法中的串行干扰消除算法(Successive Interference Cancellation,SIC)和 QR 分解算法。并在瑞利衰落信道模型下,对这几种检测算法进行误码率性能仿真对比及分析。论文在研究最大比合并排序串行干扰消除(MRC-OSIC)算法的基础上,从提高性能的角度考虑,利用多反馈串行干扰消除(Multiple Feedback Successive Interference Cancellation,MF-SIC)算法中的多反馈策略对MRC-OSIC检测算法进行改进,即MF-MRC-OSIC算法,并给出了最佳反馈点的选择方法。通过实验仿真验证MF-MRC-OSIC算法的误码性能,并与MRC-OSIC算法进行仿真对比,对仿真结果进行分析。通过实验仿真验证,可以看出,改进后的MF-MRC-OSIC算法在相同信噪比的条件下,误码率性能比MRC-OSIC算法有所提升。在计算复杂度低于MF-SIC算法的情况下,MF-MRC-OSIC算法的检测性能也接近MF-SIC算法。最后,在研究改进多反馈串行干扰消除(Improved Multiple Feedback Successive Interference Cancellation,IMF-SIC)算法的基础上,从提升性能角度考虑,结合信干噪比排序的方法,对IMF-SIC进行进一步的改进。在瑞利衰落信道模型下,对改进后的算法进行误码率性能仿真,与改进之前的IMF-SIC算法进行对比及分析。仿真结果表明,改进后的算法在大规模MIMO系统中,误码率表现较原算法有一定的提升。在计算复杂度方面,IMF-OSIC算法的复杂度要高于IMF-SIC算法。
[Abstract]:Large-scale multi-input and multi-output massive Multi-Input Multi-Output technology is one of the key technologies in 5G. It has been paid more and more attention and become one of the most important research topics in the field of wireless communication.Compared with the traditional MIMO system, the large scale MIMO system is equipped with a large number of receiving antennas, which is much larger than the MIMO system, and improves the system capacity and spectral efficiency.As an important part of large scale MIMO system, receiver detection technology is of great significance to the development of large scale MIMO.Therefore, this paper studies the signal detection algorithm in large-scale MIMO, in order to explore the signal detection algorithm suitable for large-scale MIMO system.Firstly, the paper studies the system model of large-scale MIMO and the basic principle of signal detection technology.Then, the maximum Ratio combining detection algorithm, the zero forcing detection algorithm, the minimum Mean Square error detection algorithm, the minimum mean square error detection algorithm, and the serial dry in the nonlinear signal detection algorithm are studied, respectively, in the linear signal detection algorithms, such as Maximum Ratio combining detection algorithm, zero-forcing detection algorithm, minimum mean square error detection algorithm, minimum mean square error detection algorithm, and nonlinear signal detection algorithm.The scrambling cancellation algorithm (SC) and the QR decomposition algorithm (SICs) are presented.In the Rayleigh fading channel model, the BER performance of these detection algorithms is compared and analyzed.In this paper, based on the research of MRC-OS ICs algorithm, considering the improvement of performance, the multi-feedback strategy of multiple Feedback Successive Interference cancellation MF-SICs algorithm is used to improve the MRC-OSIC detection algorithm.That is, MF-MRC-OSIC algorithm, and the method of selecting the best feedback point is given.The error performance of MF-MRC-OSIC algorithm is verified by experimental simulation, and compared with MRC-OSIC algorithm, the simulation results are analyzed.The experimental results show that the BER performance of the improved MF-MRC-OSIC algorithm is better than that of the MRC-OSIC algorithm under the same SNR.When the computational complexity is lower than that of MF-SIC algorithm, the detection performance of MF-MRC-OSIC algorithm is similar to that of MF-SIC algorithm.Finally, on the basis of the improved Multiple Feedback Successive Interference IMF-SICs algorithm, the IMF-SIC is further improved from the point of view of lifting performance and combining with the method of signal-to-noise ratio ranking.In the Rayleigh fading channel model, the BER performance of the improved algorithm is simulated and compared with the improved IMF-SIC algorithm.The simulation results show that the BER performance of the improved algorithm is better than that of the original algorithm in large-scale MIMO systems.The computational complexity of IMF-OS IC algorithm is higher than that of IMF-SIC algorithm.
【学位授予单位】：安徽大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3

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