本文選題：空時分布源 + 無線通信系統(tǒng)�。� 參考：《電子科技大學》2015年碩士論文

【摘要】：在復雜的無線通信環(huán)境中,用戶信號經過折射、繞射以及散射等原因產生多徑傳播現(xiàn)象導致在空間上、時間上的角度擴展和時延擴展造成碼間干擾以及同信道干擾,這些干擾都是影響現(xiàn)代無線移動通信質量的重要原因。在無線移動通信系統(tǒng)中采用空、時聯(lián)合處理技術可以有效地抑制信號的多徑傳播、增大系統(tǒng)容量以及提高通信質量等好處,具有重要的理論意義和適用價值。本論文針對上述問題,以無線通信環(huán)境多徑傳播為研究對象,深入地分析了現(xiàn)有信道模型,對空時分布式多徑信號參數聯(lián)合估計算法進行了改進以及提出了新的估計方法,論文的主要內容如下:基于已知分布函數情況下的空時分布式信道模型,分析了空時信道模型噪聲間的相干關系以及經過離散傅里葉變換和去卷積后的信道噪聲方差形式,發(fā)現(xiàn)傳統(tǒng)的空時參數聯(lián)合估計方法在經過去卷積后可使得信道的噪聲方差變大從而影響信號子空間,從而導致估計的不準確。針對上述問題提出了一種改進方法,通過計算空間協(xié)方差矩陣和時延協(xié)方差矩陣來構建空間傳播算子和時延傳播算子,利用傳播算子和陣列流行向量正交性質可以搜索出空時聯(lián)合參數信息,然后通過聯(lián)合傳播算子的正交投影來實現(xiàn)空時參數配對。根據均勻線陣相鄰元素間相位上保持旋轉不變特性,計算空間算子和時延旋轉算子來估計空間和時延參數,該方法無需譜峰搜索就能夠有效的估計出中心DOA和中心時延。與現(xiàn)有的最大似然準則、譜估計以及子空間的空時分布式多徑信號參數聯(lián)合估計方法相比,本文提出的改進子空間分解方法能夠有效地避免協(xié)方差矩陣特征值分解以及多維參數估計中優(yōu)化問題,從而降低算法復雜度,而且具有較好的算法性能。分析了空時分布源多徑簇信號在信道中的稀疏特性,提出了基于稀疏重構的相干空時分布式多徑簇信號的參數聯(lián)合估計算法,該算法在未知空時分布式多徑簇信號的角度密度函數和時延密度函數的情況下能夠有效地估計出中心DOA和中心時延,在知道其角度和時延的密度函數的分布擴展形式下,則可以利用稀疏重構方法估計出其角度擴展和時延擴展,具有估計精度高、分辨率好以及對信號的分布特性不敏感等優(yōu)點。
[Abstract]:In the complex wireless communication environment, the multipath propagation of user signals caused by refraction, diffraction and scattering causes intersymbol interference and cochannel interference in space, time angle spread and time delay spread. These interference are the important reasons that affect the quality of modern wireless mobile communication. The use of space-time joint processing technology in wireless mobile communication systems can effectively suppress the multipath propagation of signals, increase the system capacity and improve the communication quality. It has important theoretical significance and applicable value. Aiming at the above problems, this paper takes multipath propagation in wireless communication environment as the research object, deeply analyzes the existing channel models, improves the joint estimation algorithm of space-time distributed multipath signal parameters, and proposes a new estimation method. The main contents of this paper are as follows: based on the space-time distributed channel model with known distribution function, the coherent relation between the noise of space-time channel model and the variance of channel noise after discrete Fourier transform and deconvolution are analyzed. It is found that the traditional joint space-time parameter estimation method can cause the channel noise variance to increase after deconvolution, thus affecting the signal subspace, which leads to the inaccuracy of the estimation. In order to solve the above problems, an improved method is proposed to construct spatial propagation operator and delay propagation operator by computing spatial covariance matrix and delay covariance matrix. Space-time joint parameter information can be searched by orthogonal property of propagation operator and array popular vector, and then space-time parameter pairing can be realized by orthogonal projection of joint propagator. The spatial and delay parameters are estimated by calculating the spatial operator and the time-delay rotation operator according to the rotation invariant property of the phase between adjacent elements of the uniform linear array. This method can effectively estimate the central DOA and the central delay without the spectral peak search. Compared with the existing maximum likelihood criterion, spectral estimation and space-time distributed multipath signal parameter estimation in subspace, The improved subspace decomposition method proposed in this paper can effectively avoid the eigenvalue decomposition of covariance matrix and the optimization problem in multidimensional parameter estimation, thus reducing the complexity of the algorithm and having better algorithm performance. The sparse characteristics of space-time distributed source multipath cluster signals in the channel are analyzed, and a joint parameter estimation algorithm for coherent space-time distributed multipath cluster signals based on sparse reconstruction is proposed. The algorithm can effectively estimate the central DOA and the central delay when the angular density function and the delay density function of the distributed multipath cluster signals are unknown. The sparse reconstruction method can be used to estimate the angular spread and delay spread, which has the advantages of high estimation accuracy, good resolution and insensitivity to the signal distribution characteristics.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TN929.5
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本文編號：2054434