【摘要】：Massive MIMO技術(shù)擴(kuò)大系統(tǒng)天線配置達(dá)幾十甚至上百,天線數(shù)的量變使系統(tǒng)獲得了特性方面的質(zhì)變,在很大程度上提升了無(wú)線傳輸?shù)念l率利用率、信道容量及可靠性。與此同時(shí),天線數(shù)量較多,傳統(tǒng)的接收端的信號(hào)處理方式檢測(cè)性能較差,而且過(guò)于復(fù)雜不利于硬件實(shí)現(xiàn)。所以主要研究適用于Massive MIMO系統(tǒng)的低實(shí)現(xiàn)復(fù)雜度的信號(hào)檢測(cè)算法。首先對(duì)比了點(diǎn)到點(diǎn)MIMO以及多用戶MIMO的系統(tǒng)模型,在此基礎(chǔ)上擴(kuò)大天線配置,引出了Massive MIMO系統(tǒng)模型,并說(shuō)明了Massive MIMO技術(shù)的實(shí)際優(yōu)勢(shì)以及實(shí)際實(shí)現(xiàn)中的瓶頸。信號(hào)檢測(cè)方面,整理了常用的線性檢測(cè)與非線性檢測(cè)方法,指出其適應(yīng)的通信場(chǎng)景以及不足。鑒于基站天線數(shù)遠(yuǎn)大于發(fā)射端天線數(shù)時(shí),傳統(tǒng)的線性檢測(cè)即可獲得較好的性能,所以課題主要研究收發(fā)兩端天線配置較均衡時(shí)的信號(hào)檢測(cè)算法。為了進(jìn)一步提高檢測(cè)精度,研究算法都是以MMSE檢測(cè)算法為起始點(diǎn),不同于傳統(tǒng)的硬判決,幾種算法目標(biāo)信號(hào)的確定都是根據(jù)信息比特的對(duì)數(shù)似然比。其次在一維顯著特征向量搜索檢測(cè)算法的基礎(chǔ)上提出了改進(jìn)的多維顯著特征向量搜索檢測(cè)算法。一維特征向量搜索算法假設(shè)信號(hào)最可能存在在某一顯著特征向量方向上,針對(duì)選定的特征向量進(jìn)行遍歷從中選出最優(yōu)方向的信號(hào)。在此基礎(chǔ)上,多維特征向量搜索綜合多個(gè)特征向量的結(jié)果進(jìn)行信號(hào)的判決不僅可獲得更優(yōu)的檢測(cè)性能而且可以大大減少候選信號(hào)的個(gè)數(shù)。根據(jù)復(fù)雜度統(tǒng)計(jì)結(jié)果,多維搜索的復(fù)雜度始終低于一維搜索。所以與其一維搜索算法相比,改進(jìn)的多維特征向量搜索檢測(cè)獲得了較好的檢測(cè)性能以及較低的硬件實(shí)現(xiàn)復(fù)雜度。最后研究了基于檢測(cè)誤差建模的天線選擇檢測(cè)算法。該算法設(shè)某天線集合上的信號(hào)恰為某星座點(diǎn)集合,最終求得使檢測(cè)誤差最小的目標(biāo)信號(hào)向量。若選取天線的個(gè)數(shù)為1,則為單天線選擇檢測(cè)算法,若為多個(gè)則為多天線選擇檢測(cè)算法。低信噪比時(shí),單天線選擇檢測(cè)的檢測(cè)性能即優(yōu)于本文其他算法,高信噪比時(shí),多天線選擇檢測(cè)算法在復(fù)雜度提高較少時(shí)仍能維持較好的檢測(cè)性能。仿真對(duì)比可發(fā)現(xiàn)單天線選擇檢測(cè)算法的復(fù)雜度能較好的適應(yīng)天線配置的改變,實(shí)現(xiàn)低復(fù)雜度的Massive MIMO信號(hào)檢測(cè)算法。
[Abstract]:The Massive MIMO technology expands the antenna configuration of the system to tens or even hundreds. The quantitative change of the antenna number makes the system obtain the qualitative change of characteristic, which greatly improves the frequency utilization ratio, channel capacity and reliability of wireless transmission. At the same time, the number of antennas is large, the detection performance of the traditional signal processing method is poor, and the complexity is not conducive to hardware implementation. Therefore, a low complexity signal detection algorithm suitable for Massive MIMO system is studied. Firstly, the system models of point-to-point MIMO and multi-user MIMO are compared. On this basis, the antenna configuration is expanded, and the Massive MIMO system model is introduced, and the practical advantages of Massive MIMO technology and the bottleneck in practical implementation are explained. In the aspect of signal detection, the common linear and nonlinear detection methods are sorted out, and the suitable communication scenes and their shortcomings are pointed out. Because the base station antenna number is far larger than the transmitter antenna number, the traditional linear detection can obtain better performance, so the thesis mainly studies the signal detection algorithm when the antenna configuration is more balanced. In order to further improve the detection accuracy, the algorithms are based on MMSE detection algorithm as the starting point, which is different from the traditional hard decision. The determination of the target signal of several algorithms is based on the logarithmic likelihood ratio of information bits. Secondly, an improved multi-dimensional salient feature vector search detection algorithm is proposed on the basis of one-dimensional salient feature vector search detection algorithm. The one-dimensional eigenvector search algorithm assumes that the signal is most likely to exist in the direction of a salient eigenvector and traverses the selected eigenvector to select the optimal direction of the signal. On this basis, multi-dimensional eigenvector search synthesizes the result of multi-eigenvector to judge the signal not only can obtain better detection performance, but also can greatly reduce the number of candidate signals. According to the statistical results of complexity, the complexity of multidimensional search is always lower than that of one-dimensional search. Compared with the one-dimensional search algorithm, the improved multi-dimensional eigenvector search algorithm has better detection performance and lower hardware implementation complexity. Finally, the antenna selection detection algorithm based on error modeling is studied. In this algorithm, the signal on a set of antennas is exactly a set of constellation points, and finally the target signal vector with minimum detection error is obtained. If the number of antennas selected is 1, it is a single antenna selection detection algorithm, and if more than one antenna is selected, it is a multi-antenna selection detection algorithm. When the SNR is low, the detection performance of single antenna selection detection is better than that of other algorithms in this paper. When the SNR is high, the multi-antenna selection detection algorithm can maintain better detection performance even when the complexity is less improved. The simulation results show that the complexity of the single antenna selection detection algorithm can adapt to the antenna configuration change and realize the low complexity Massive MIMO signal detection algorithm.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN919.3

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本文編號(hào)：2376041