本文選題：OFDM　切入點：信道估計　出處：《天津工業(yè)大學》2017年碩士論文

【摘要】：正交頻分復用(Orthogonal Frequency Division Multiplexing,OFDM)技術已經成為無線通信技術中不可替代的部分,具有很高的應用價值。信道估計是OFDM通信系統(tǒng)中的關鍵技術,信道估計性能的好壞將直接影響到整個系統(tǒng)的通信質量。'壓縮感知理論的提出,有效的改善了 OFDM系統(tǒng)稀疏信道估計的性能,減少了信道估計所需要的導頻數,提高了系統(tǒng)的頻譜利用率。本文分析了基于壓縮感知的OFDM系統(tǒng)信道估計問題。在OFDM系統(tǒng)中,如果信道的稀疏度是已知的,傳統(tǒng)的壓縮感知算法,如正交匹配追蹤算法(Orthogonal Matching Pursuit,OMP)、壓縮采樣匹配追蹤算法(Compressive Sampling Matching Pursuit,CoSaMP)、子空間追蹤(Subspace Pursuit,SP)在合理的參數選取下均能表現(xiàn)出良好的估計性能,表明了壓縮感知算法在OFDM系統(tǒng)信道估計中優(yōu)越性。然而,由于在實際系統(tǒng)中,信道的稀疏度通常是未知的,極大的限制了需要預知稀疏度的壓縮感知算法在OFDM信道估計中的實際應用。為了更好的利用壓縮感知去實現(xiàn)OFDM信道估計,需要研究自適應稀疏度的恢復算法。文章先介紹了傳統(tǒng)的稀疏度自適應匹配追蹤(Sparsity Adaptive Matching Pursuit,SAMP)算法信道估計,而SAMP算法雖然可以達到自適應稀疏度的效果,但由于存在欠估計和過估計的問題,給信道估計的性能帶來了較為不利的影響,同時,為了追求更好的性能,需要提高算法的計算復雜度,極大的影響了通信系統(tǒng)的實時性。針對以上算法的不足,本文提出了一種正則化自適應稀疏度的壓縮感知算法(Regularized Sparsity Adaptive Matching Pursuit,RSAMP),該算法不需要預先知道信道的稀疏度,首先通過選擇相關系數向量中最大后向差分的位置來選擇支撐集原子,再對已選擇的原子支撐集合進行正則化,用來提高支撐集的準確性,并通過迭代直至算法收斂。在未知信道稀疏度時,算法有著良好的性能,并具有較低的計算復雜度。同時,由于OFDM系統(tǒng)中較高的峰均比影響了功率放大器的工作性能,如果對OFDM系統(tǒng)進行限幅操作來抑制高峰均比,就會使導頻信號受到非線性失真的影響,從而嚴重影響信道估計性能。針對這一問題,本文提出了利用迭代的方法,用壓縮感知對信道響應和非線性失真分別進行估計,通過對導頻信號進行補償,減小非線性失真對信道估計性能的影響,擴展了壓縮感知在OFDM系統(tǒng)信道估計中的應用場景。
[Abstract]:Orthogonal Frequency Division multiplexing (OFDM) technology has become an irreplaceable part of wireless communication technology and has high application value.Channel estimation is a key technology in OFDM communication system. The performance of channel estimation will directly affect the communication quality of the whole system.The proposed compressed sensing theory can effectively improve the performance of sparse channel estimation in OFDM systems, reduce the number of pilots needed for channel estimation, and improve the spectral efficiency of the system.In this paper, the problem of channel estimation for OFDM systems based on compressed sensing is analyzed.In OFDM systems, if the channel sparsity is known, the traditional compression sensing algorithm,For example, orthogonal Matching pursuit algorithm, compressed Sampling Matching pursuit algorithm, subspace tracker subspace pursuit algorithm can all show good estimation performance under reasonable parameter selection, which shows the superiority of compressed sensing algorithm in channel estimation of OFDM system.However, because the channel sparsity is usually unknown in the actual system, it greatly limits the practical application of the compression sensing algorithm which needs to predict the sparse degree in OFDM channel estimation.In order to make better use of compressed sensing to realize OFDM channel estimation, it is necessary to study an adaptive sparse recovery algorithm.This paper first introduces the channel estimation of the traditional sparse adaptive matching tracking Adaptive Matching pursuit algorithm. Although the SAMP algorithm can achieve the effect of adaptive sparsity, it has the problem of underestimation and overestimation.At the same time, in order to achieve better performance, it is necessary to improve the computational complexity of the algorithm, which greatly affects the real-time performance of the communication system.To overcome the shortcomings of the above algorithms, a regularized Sparsity Adaptive Matching pursuit algorithm is proposed in this paper. The algorithm does not need to know the sparse degree of the channel in advance.Firstly, the support set atom is selected by selecting the position of the largest backward difference in the correlation coefficient vector, and then the selected atomic support set is regularized to improve the accuracy of the support set, and then iterate until the algorithm converges.When the channel sparsity is unknown, the algorithm has good performance and low computational complexity.At the same time, because the high peak-to-average ratio (PAPR) in the OFDM system affects the performance of the power amplifier, if the OFDM system is limited to suppress the PAPR, the pilot signal will be affected by nonlinear distortion.Thus the channel estimation performance is seriously affected.To solve this problem, an iterative method is proposed to estimate channel response and nonlinear distortion separately by compression sensing, and to reduce the influence of nonlinear distortion on channel estimation performance by compensating pilot signals.The application of compressed sensing in OFDM channel estimation is extended.
【學位授予單位】：天津工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.53

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本文編號：1705046