本文選題：V-BLAST + MIMO系統(tǒng)��； 參考：《浙江大學(xué)》2014年碩士論文

【摘要】：MIMO和OFDM技術(shù)以其高頻譜利用率和對(duì)抗多徑效應(yīng)的優(yōu)勢在無線通信領(lǐng)域得到了廣泛應(yīng)用。隨著人們對(duì)通信帶寬的需求不斷提高,大規(guī)模MIMO技術(shù)受到關(guān)注,由于大規(guī)模MIMO系統(tǒng)天線數(shù)目遠(yuǎn)多于傳統(tǒng)MIMO系統(tǒng),因而對(duì)低復(fù)雜度、高性能的信號(hào)檢測算法提出了更高的要求。本文以級(jí)聯(lián)LDPC碼的V-BLAST結(jié)構(gòu)MIMO-OFDM系統(tǒng)為主要研究模型,利用OFDM的并行多子載波特性,將頻率選擇性衰落信道變?yōu)槎鄠�(gè)并行的平坦衰落信道,然后基于每個(gè)子載波,建模為平坦衰落V-BLAST MIMO系統(tǒng),來并行、獨(dú)立地進(jìn)行信號(hào)檢測。 根據(jù)信號(hào)均衡過程和信道譯碼器的譯碼過程之間的關(guān)系,并以(大規(guī)模)MIMO系統(tǒng)的低復(fù)雜度為要求,本文將MIMO系統(tǒng)信號(hào)檢測的方法分為三類展開研究：獨(dú)立譯碼檢測算法、Turbo迭代譯碼檢測算法和聯(lián)合譯碼檢測算法；分別針對(duì)上述三類檢測算法,進(jìn)行低復(fù)雜度的檢測器設(shè)計(jì)： (1)對(duì)獨(dú)立檢測和譯碼算法進(jìn)行了研究。首先分析了具有最優(yōu)檢測性能的ML算法,為了降低復(fù)雜度,提出了基于ML準(zhǔn)則的樹搜索模型,研究基于深度優(yōu)先搜索的SD算法和寬度優(yōu)先搜索的K-Best算法。在K-Best算法中,并行處理和固定可控的復(fù)雜度使得該方法很適合在實(shí)際系統(tǒng)中應(yīng)用；而K因子在K-Best算法中具有關(guān)鍵作用,通過選擇合適的K參數(shù),確保系統(tǒng)性能和計(jì)算復(fù)雜度的折中。 (2)基于Turbo迭代檢測算法的基本模型,研究了K-Best準(zhǔn)則和MMSE準(zhǔn)則下的Turbo檢測器設(shè)計(jì)。對(duì)于Turbo-K-Best算法,著重分析了輸出軟信息LLR的計(jì)算方案,通過利用有限搜索列表策略和Max-Log策略,大大降低了計(jì)算復(fù)雜度；對(duì)于Turbo-MMSE算法,著重研究具有先驗(yàn)信息的MMSE準(zhǔn)則濾波器,并將濾波后的其他天線干擾用高斯分布來近似,進(jìn)行LLR計(jì)算。對(duì)Turbo-K-Best和Turbo-MMSE的性能和復(fù)雜度進(jìn)行了綜合分析,發(fā)現(xiàn)通過選擇合適的K因子,可以使得Turbo-K-Best的復(fù)雜度與Turbo-MMSE持平甚至更低,并獲得優(yōu)于MMSE檢測的性能。 (3)將因子圖FG模型引入到信號(hào)檢測過程中。首先分析了基于因子圖模型的置信度傳遞(BP)算法,研究了圖中各個(gè)節(jié)點(diǎn)的信息傳遞過程。而在MIMO信號(hào)檢測中,通過引入高斯干擾近似,也可以利用FG-BP算法在變量節(jié)點(diǎn)和觀測節(jié)點(diǎn)之間不斷進(jìn)行似然比消息傳遞,通過不斷迭代使得變量節(jié)點(diǎn)收斂至邊緣概率,完成信號(hào)檢測；詳細(xì)分析了基于FG-BP的信號(hào)檢測算法,并將該算法擴(kuò)展到Turbo迭代結(jié)構(gòu)中,得到了Turbo-FG-BP信號(hào)檢測算法。除此之外,提出了聯(lián)合LDPC譯碼和信號(hào)檢測算法,用統(tǒng)一的因子圖對(duì)信道和LDPC編碼矩陣進(jìn)行建模,在每一次迭代中同時(shí)進(jìn)行變量節(jié)點(diǎn)、觀測節(jié)點(diǎn)和校驗(yàn)節(jié)點(diǎn)三大類之間的信息傳遞,獲得更多的外信息,從而獲得更快的收斂和更好的性能；為了進(jìn)一步提高檢測性能,提出了基于分層LFG-BP的迭代檢測算法,通過在每一次迭代內(nèi)增加對(duì)各個(gè)發(fā)送符號(hào)的串行處理,進(jìn)一步提升了收斂速率；針對(duì)一種特殊的應(yīng)用的環(huán)境——發(fā)送天線和接收天線不對(duì)等的大規(guī)模天線系統(tǒng)——提出了QR-LFG-BP算法,利用QR分解減少因子圖中的節(jié)點(diǎn)個(gè)數(shù)和連接線個(gè)數(shù),從而減少信息的計(jì)算和傳遞過程,在不損失性能的基礎(chǔ)上進(jìn)一步降低復(fù)雜度。
[Abstract]:MIMO and OFDM technology have been widely used in the field of wireless communication for their high frequency spectrum utilization and the advantages of resisting multipath effect. With the increasing demand for communication bandwidth, large scale MIMO technology is concerned. Because the number of large-scale MIMO systems is far more than the traditional MIMO system, the signal of low complexity and high performance The detection algorithm puts forward higher requirements. In this paper, the V-BLAST structure MIMO-OFDM system of cascaded LDPC codes is used as the main research model. Using the parallel multicarrier characteristics of OFDM, the frequency selective fading channel is transformed into multiple parallel flat fading channels, and then based on each subcarrier, a flat fading V-BLAST MIMO system is modeled to be parallel, Signal detection is performed independently.
According to the relationship between the signal equalization process and the decoding process of the channel decoder and the low complexity of the (large scale) MIMO system, this paper divides the methods of signal detection in the MIMO system into three categories: independent decoding detection algorithm, Turbo iterative decoding algorithm and joint decoding algorithm, respectively, for the above three. The class detection algorithm is designed with low complexity detector.
(1) the independent detection and decoding algorithms are studied. First, the ML algorithm with optimal detection performance is analyzed. In order to reduce the complexity, a tree search model based on ML criterion is proposed, and the SD algorithm based on depth first search and the K-Best algorithm of width first search are studied. In the K-Best algorithm, the parallel processing and the fixed and controllable complexity are in the K-Best algorithm. The degree makes the method suitable for application in the actual system, and the K factor plays a key role in the K-Best algorithm. By selecting the appropriate K parameters, the system performance and computational complexity are ensured.
(2) based on the basic model of the Turbo iterative detection algorithm, the K-Best criterion and the Turbo detector design under the MMSE criterion are studied. For the Turbo-K-Best algorithm, the calculation scheme of the output soft information LLR is emphatically analyzed. The computational complexity is greatly reduced by using the finite search list strategy and the Max-Log strategy, and the Turbo-MMSE algorithm is emphasized. The MMSE criterion filter with prior information is studied, and the other antenna interference after the filtering is approximated by the Gauss distribution. The LLR calculation is carried out. The performance and complexity of Turbo-K-Best and Turbo-MMSE are synthetically analyzed. It is found that the complexity of Turbo-K-Best can be equal to even lower by the selection of the appropriate K factor. And the performance is better than the MMSE detection.
(3) the factor graph FG model is introduced to the signal detection process. Firstly, the confidence transfer (BP) algorithm based on the factor graph model is analyzed, and the information transfer process of each node in the graph is studied. In the MIMO signal detection, by introducing the Gauss interference approximation, the FG-BP algorithm can also be used between the variable nodes and the observation nodes. The likelihood ratio message passing through continuous iteration makes the variable node converge to the edge probability and completes the signal detection. The signal detection algorithm based on FG-BP is analyzed in detail, and the algorithm is extended to the Turbo iterative structure, and the Turbo-FG-BP signal detection algorithm is obtained. In addition, the combined LDPC decoding and signal detection algorithms are proposed. A factor graph is used to model the channel and LDPC coding matrix. At the same time, the variable nodes are carried out at the same time, the information between the observation nodes and the three classes of the checkpoint nodes is transmitted, and more information is obtained, thus getting faster convergence and better performance. In order to improve the detection performance, a hierarchical LFG-BP is proposed. The iterative detection algorithm further improves the convergence rate by increasing the serial processing of each transmission symbol in each iteration. A QR-LFG-BP algorithm is proposed for a special application environment, the transmission antenna and the unequal antenna system of the receiving antenna, which uses the QR decomposition to reduce the node in the factor diagram. The number and number of connections can reduce the computation and transmission of information, and further reduce the complexity without losing the performance.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.23

【參考文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

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本文編號(hào)：1954442