Massive MIMO系统信道估计与检测技术研究

发布时间：2018-08-31 13:50

【摘要】：Massive MIMO (Multiple-input Multiple-output)在可靠性、频谱效率、能量效率及信道容量等方面均能够带来显著的性能提升,并被公认为未来第5代(5G)移动通信系统的核心技术之一。由于在基站(Base station,BS)端进行前向预编码和反向检测时均需要利用信道状态信息,因此,信道估计结果的质量将直接影响整个通信系统的性能。同时,由于在Massive MIMO系统中,BS端将配置大规模的天线,信号处理算法的计算复杂度也将成为决定其是否具有可行性的关键因素。因此,如何以较低的计算复杂度获取精度较高的信道估计结果及信号检测结果,对Massive MIMO系统而言将是至关重要的。本文主要对Massive MIMO系统中的信道估计技术和信号检测技术进行研究。基于导频的线性信道估计算法需要用户发送大量的导频信号,该类算法具有较低的频谱利用率且其性能受到导频污染的严重限制。文中首先推导出一种对模糊矩阵进行迭代计算的方法,然后利用FRRH (Fast Recursive Row-Householder)子空间跟踪算法降低接收信号自相关矩阵的信号子空间估计过程的计算量,最后给出了一种基于FRRH的半盲信道估计算法。通过将文中所给算法的计算量和基于EVD或SVD的半盲信道估计算法的计算量进行比较,发现文中所给算法显著减少了半盲信道算法的计算量。仿真实验表明,文中所给的基于FRRH的半盲信道估计算法具有较好的收敛性能,在较低的样本数目时即可达到收敛。所给算法能够取得好于LS信道估计算法及基于EVD的半盲信道估计算法的估计性能,并且和基于SVD的半盲信道估计算法基本相当。同时,所给算法能有效地减轻导频污染对估计性能的影响。针对Massive MIMO系统中的信号检测技术,文中主要对MRC、ZF和MMSE这三种线性的检测算法进行研究,并针对每种检测算法给出了小区中每个用户可达上行链路数据速率的下界表达式。在单小区模型和多小区模型中分别对三种算法的误码率性能进行仿真比较。仿真实验结果表明,在单小区模型中,当BS端的天线数目变大时,三种算法的误码率均显著下降,且其误码率性能几乎相当,并均可在较低的SNR下即可达到理想的误码率；在多小区模型中,ZF和MMSE检测算法在不同的SNR下具有近似的误码率,并且都能取得比MRC检测算法更低的误码率。但是,当BS端天线数目较大且SNR的值较高时,MRC算法仍能够取得与ZF和MMSE检测算法近似的误码率性能。
[Abstract]:Massive MIMO (Multiple-input Multiple-output) is recognized as one of the core technologies of the 5th generation (5G) mobile communication system in terms of reliability, spectrum efficiency, energy efficiency and channel capacity. Since both forward precoding and reverse detection at the base station (Base station,BS) require the use of channel state information, the quality of channel estimation results will directly affect the performance of the entire communication system. At the same time, the complexity of the signal processing algorithm will be the key factor to determine the feasibility of the Massive MIMO system, because the BS terminal will be equipped with a large scale antenna. Therefore, how to obtain the accurate channel estimation results and signal detection results with low computational complexity is very important for Massive MIMO systems. In this paper, channel estimation and signal detection in Massive MIMO system are studied. Pilot based linear channel estimation algorithms require users to send a large number of pilot signals. These algorithms have low spectral efficiency and their performance is severely limited by pilot pollution. In this paper, a method for iterative calculation of fuzzy matrix is derived, and then the FRRH (Fast Recursive Row-Householder subspace tracking algorithm is used to reduce the computational complexity of the signal subspace estimation of the autocorrelation matrix of the received signal. Finally, a semi-blind channel estimation algorithm based on FRRH is presented. By comparing the computational complexity of the proposed algorithm with that of the semi-blind channel estimation algorithm based on EVD or SVD, it is found that the proposed algorithm has significantly reduced the computational complexity of the semi-blind channel algorithm. Simulation results show that the proposed semi-blind channel estimation algorithm based on FRRH has good convergence performance and can converge when the number of samples is low. The performance of the proposed algorithm is better than that of the LS channel estimation algorithm and the semi-blind channel estimation algorithm based on EVD, and is similar to that of the semi-blind channel estimation algorithm based on SVD. At the same time, the proposed algorithm can effectively reduce the influence of pilot pollution on the estimation performance. Aiming at the signal detection technology in Massive MIMO system, three linear detection algorithms, MRC,ZF and MMSE, are studied in this paper, and the lower bound expression of the uplink data rate for each user in the cell is given for each detection algorithm. In the single cell model and the multi-cell model, the BER performance of the three algorithms is simulated and compared. The simulation results show that when the number of antennas at the BS end increases, the BER of the three algorithms decreases significantly in the single cell model, and the BER performance of the three algorithms is almost the same, and the BER can reach the ideal bit error rate under the lower SNR. In the multi-cell model, both ZF and MMSE detection algorithms have approximate bit error rate (BER) under different SNR conditions, and can achieve lower BER than MRC detection algorithm. However, when the number of BS antennas is large and the value of SNR is high, the BER performance of MRC algorithm is similar to that of ZF and MMSE detection algorithms.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN919.3

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