本文選題：大規(guī)模多輸入多輸出正交頻分復(fù)用系統(tǒng)　切入點：結(jié)構(gòu)化壓縮感知　出處：《南京郵電大學》2017年碩士論文

【摘要】：大規(guī)模多輸入多輸出-正交頻分復(fù)用(MIMO-OFDM)系統(tǒng),因其既可以獲得較高的信道容量,同時又會得到較高的能量有效性,而成為未來5G技術(shù)的關(guān)鍵。本文研究了將結(jié)構(gòu)化壓縮感知理論用于該系統(tǒng)的稀疏信道估計。本文的主要貢獻在于:(1)考慮到大規(guī)模MIMO-OFDM系統(tǒng)中將每個發(fā)送天線上的導(dǎo)頻重疊放置,即每個發(fā)送天線可以在相同的時頻資源塊上發(fā)送導(dǎo)頻符號,那么此時的系統(tǒng)稀疏信道估計問題可以建模為結(jié)構(gòu)化壓縮感知重建問題,從而建立了稀疏信道估計與結(jié)構(gòu)化壓縮感知的對應(yīng)關(guān)系。(2)考慮到導(dǎo)頻設(shè)計涉及導(dǎo)頻位置以及符號兩個關(guān)鍵因素,為了優(yōu)化導(dǎo)頻位置和導(dǎo)頻符號來改進稀疏信道估計的質(zhì)量,本文首先針對導(dǎo)頻位置選取提出了與之對應(yīng)的最小化完全塊間相關(guān)值的導(dǎo)頻優(yōu)化準則以及基于此準則的導(dǎo)頻搜索算法。完全塊間相關(guān)值是結(jié)構(gòu)化壓縮感知框架下衡量恢復(fù)矩陣子塊間相關(guān)程度的量值。仿真結(jié)果表明,與其他未優(yōu)化導(dǎo)頻相比,使用此優(yōu)化方法獲得的導(dǎo)頻可以使信道估計誤差(MSE)明顯減小,信道估計性能提高約2-4dB。(3)將導(dǎo)頻位置與導(dǎo)頻符號這兩個因素結(jié)合在一起,提出了這種情況下的基于最小化完全塊間相關(guān)值的導(dǎo)頻優(yōu)化準則以及基于此準則的導(dǎo)頻搜索算法。仿真結(jié)果同樣表明,與其他導(dǎo)頻相比,使用此優(yōu)化算法獲得的導(dǎo)頻可以使信道估計的MSE明顯減小,約2-5dB。同時仿真結(jié)果表明導(dǎo)頻位置和符號聯(lián)合優(yōu)化方法獲得的優(yōu)化導(dǎo)頻性能優(yōu)于單純優(yōu)化導(dǎo)頻位置獲得的優(yōu)化導(dǎo)頻,它能使得大規(guī)模MIMO-OFDM系統(tǒng)的信道估計具有更低的MSE。
[Abstract]:Large-scale multi-input-multiple-output (MIMO) -OFDM (orthogonal Frequency Division Multiplexing) system is the key of 5G technology in the future because of its high channel capacity and high energy efficiency.In this paper, we study the application of structured compressed sensing theory to sparse channel estimation of the system.The main contribution of this paper is to take into account the fact that in large scale MIMO-OFDM systems the pilots on each transmit antenna are superimposed, that is, each transmission antenna can transmit pilot symbols on the same time-frequency resource block.Then the system sparse channel estimation problem can be modeled as a structured compressed perceptual reconstruction problem.Therefore, the relationship between sparse channel estimation and structured compressed sensing is established. Considering that pilot design involves two key factors, pilot position and symbol, the quality of sparse channel estimation is improved in order to optimize pilot position and pilot symbol.In this paper, a pilot optimization criterion for minimizing the correlation between complete blocks and a pilot search algorithm based on the pilot position selection are proposed.The complete block correlation is a measure of the correlation between subblocks of the recovery matrix under the framework of structured compression perception.The simulation results show that compared with other unoptimized pilots, the channel estimation error (MSE) can be significantly reduced by using this optimization method, and the channel estimation performance is improved by about 2-4dB.m3), which combines the pilot position with the pilot symbol.In this case, the pilot optimization criterion based on minimizing the correlation between complete blocks and the pilot search algorithm based on this criterion are proposed.The simulation results also show that compared with other pilot frequencies, the MSE of channel estimation can be significantly reduced by using this optimization algorithm, about 2-5 dB.The simulation results show that the optimal pilot performance obtained by the combined pilot position and symbol optimization method is better than that obtained by the simple optimization pilot position method, which can make the channel estimation of large-scale MIMO-OFDM system have lower MSE.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.53;TN919.3

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本文編號：1725062