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

【摘要】：正交頻分復(fù)用(Orthogonal Frequency Division Multiplexing,OFDM)技術(shù)已經(jīng)成為無線通信技術(shù)中不可替代的部分,具有很高的應(yīng)用價(jià)值。信道估計(jì)是OFDM通信系統(tǒng)中的關(guān)鍵技術(shù),信道估計(jì)性能的好壞將直接影響到整個(gè)系統(tǒng)的通信質(zhì)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(jì)的性能,減少了信道估計(jì)所需要的導(dǎo)頻數(shù),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(jì)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(shù)選取下均能表現(xiàn)出良好的估計(jì)性能,表明了壓縮感知算法在OFDM系統(tǒng)信道估計(jì)中優(yōu)越性。然而,由于在實(shí)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(yù)知稀疏度的壓縮感知算法在OFDM信道估計(jì)中的實(shí)際應(yīng)用。為了更好的利用壓縮感知去實(shí)現(xiàn)OFDM信道估計(jì),需要研究自適應(yīng)稀疏度的恢復(fù)算法。文章先介紹了傳統(tǒng)的稀疏度自適應(yīng)匹配追蹤(Sparsity Adaptive Matching Pursuit,SAMP)算法信道估計(jì),而SAMP算法雖然可以達(dá)到自適應(yīng)稀疏度的效果,但由于存在欠估計(jì)和過估計(jì)的問題,給信道估計(jì)的性能帶來了較為不利的影響,同時(shí),為了追求更好的性能,需要提高算法的計(jì)算復(fù)雜度,極大的影響了通信系統(tǒng)的實(shí)時(shí)性。針對(duì)以上算法的不足,本文提出了一種正則化自適應(yīng)稀疏度的壓縮感知算法(Regularized Sparsity Adaptive Matching Pursuit,RSAMP),該算法不需要預(yù)先知道信道的稀疏度,首先通過選擇相關(guān)系數(shù)向量中最大后向差分的位置來選擇支撐集原子,再對(duì)已選擇的原子支撐集合進(jìn)行正則化,用來提高支撐集的準(zhǔn)確性,并通過迭代直至算法收斂。在未知信道稀疏度時(shí),算法有著良好的性能,并具有較低的計(jì)算復(fù)雜度。同時(shí),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(duì)OFDM系統(tǒng)進(jìn)行限幅操作來抑制高峰均比,就會(huì)使導(dǎo)頻信號(hào)受到非線性失真的影響,從而嚴(yán)重影響信道估計(jì)性能。針對(duì)這一問題,本文提出了利用迭代的方法,用壓縮感知對(duì)信道響應(yīng)和非線性失真分別進(jìn)行估計(jì),通過對(duì)導(dǎo)頻信號(hào)進(jìn)行補(bǔ)償,減小非線性失真對(duì)信道估計(jì)性能的影響,擴(kuò)展了壓縮感知在OFDM系統(tǒng)信道估計(jì)中的應(yīng)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(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.53

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 陳芳妮;王中鵬;;A post-coding scheme for peak-to-average power ratio reduction in intensity modulated optical OFDM systems[J];Optoelectronics Letters;2014年04期

2 楊真真;楊震;孫林慧;;信號(hào)壓縮重構(gòu)的正交匹配追蹤類算法綜述[J];信號(hào)處理;2013年04期

3 解永生;覃榮華;周磊磊;付耀先;;多媒體傳感網(wǎng)中限幅OFDM的信道估計(jì)算法[J];華中科技大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年08期

4 葉新榮;朱衛(wèi)平;孟慶民;;基于SAMP重構(gòu)算法的OFDM系統(tǒng)稀疏信道估計(jì)方法[J];信號(hào)處理;2012年03期

5 趙競;王玲;;壓縮感知理論在OFDM稀疏信道估計(jì)中的應(yīng)用[J];通信技術(shù);2012年03期

6 周克琴;宋榮方;何雪云;;MIMO NC-OFDM系統(tǒng)的半盲信道估計(jì)算法研究[J];南京郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年01期

7 焦李成;楊淑媛;劉芳;侯彪;;壓縮感知回顧與展望[J];電子學(xué)報(bào);2011年07期

8 何雪云;宋榮方;周克琴;;基于壓縮感知的OFDM系統(tǒng)稀疏信道估計(jì)新方法研究[J];南京郵電大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年02期

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