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

【摘要】：在復(fù)雜的無線通信環(huán)境中,用戶信號(hào)經(jīng)過折射、繞射以及散射等原因產(chǎn)生多徑傳播現(xiàn)象導(dǎo)致在空間上、時(shí)間上的角度擴(kuò)展和時(shí)延擴(kuò)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(dòng)通信質(zhì)量的重要原因。在無線移動(dòng)通信系統(tǒng)中采用空、時(shí)聯(lián)合處理技術(shù)可以有效地抑制信號(hào)的多徑傳播、增大系統(tǒng)容量以及提高通信質(zhì)量等好處,具有重要的理論意義和適用價(jià)值。本論文針對(duì)上述問題,以無線通信環(huán)境多徑傳播為研究對(duì)象,深入地分析了現(xiàn)有信道模型,對(duì)空時(shí)分布式多徑信號(hào)參數(shù)聯(lián)合估計(jì)算法進(jìn)行了改進(jìn)以及提出了新的估計(jì)方法,論文的主要內(nèi)容如下:基于已知分布函數(shù)情況下的空時(shí)分布式信道模型,分析了空時(shí)信道模型噪聲間的相干關(guān)系以及經(jīng)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(shí)參數(shù)聯(lián)合估計(jì)方法在經(jīng)過去卷積后可使得信道的噪聲方差變大從而影響信號(hào)子空間,從而導(dǎo)致估計(jì)的不準(zhǔn)確。針對(duì)上述問題提出了一種改進(jìn)方法,通過計(jì)算空間協(xié)方差矩陣和時(shí)延協(xié)方差矩陣來構(gòu)建空間傳播算子和時(shí)延傳播算子,利用傳播算子和陣列流行向量正交性質(zhì)可以搜索出空時(shí)聯(lián)合參數(shù)信息,然后通過聯(lián)合傳播算子的正交投影來實(shí)現(xiàn)空時(shí)參數(shù)配對(duì)。根據(jù)均勻線陣相鄰元素間相位上保持旋轉(zhuǎn)不變特性,計(jì)算空間算子和時(shí)延旋轉(zhuǎn)算子來估計(jì)空間和時(shí)延參數(shù),該方法無需譜峰搜索就能夠有效的估計(jì)出中心DOA和中心時(shí)延。與現(xiàn)有的最大似然準(zhǔn)則、譜估計(jì)以及子空間的空時(shí)分布式多徑信號(hào)參數(shù)聯(lián)合估計(jì)方法相比,本文提出的改進(jìn)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(shù)估計(jì)中優(yōu)化問題,從而降低算法復(fù)雜度,而且具有較好的算法性能。分析了空時(shí)分布源多徑簇信號(hào)在信道中的稀疏特性,提出了基于稀疏重構(gòu)的相干空時(shí)分布式多徑簇信號(hào)的參數(shù)聯(lián)合估計(jì)算法,該算法在未知空時(shí)分布式多徑簇信號(hào)的角度密度函數(shù)和時(shí)延密度函數(shù)的情況下能夠有效地估計(jì)出中心DOA和中心時(shí)延,在知道其角度和時(shí)延的密度函數(shù)的分布擴(kuò)展形式下,則可以利用稀疏重構(gòu)方法估計(jì)出其角度擴(kuò)展和時(shí)延擴(kuò)展,具有估計(jì)精度高、分辨率好以及對(duì)信號(hào)的分布特性不敏感等優(yōu)點(diǎn)。
[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(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN929.5
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本文編號(hào)：2054434