本文選題：正交頻分復用　切入點：信道估計　出處：《西南交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：在正交頻分復用(OFDM)系統中,準確、實時的信道估計是接收信息成功解調的重要保證。高速移動和多徑傳播環(huán)境使得基站和移動臺之間的信號受到多普勒效應和多徑效應的影響。為獲得準確的信道響應,傳統的信道估計算法利用大量的導頻獲取信道狀態(tài)信息CSI(Channel State Information),使得導頻開銷難以降低,造成系統頻譜利用率下降。因此,在信道估計性能和導頻開銷之間取得良好的折中,一直是信道估計亟需解決的問題。本文主要研究基于壓縮感知理論的信道估計算法,主要研究內容如下:1.研究了無線通信傳播環(huán)境的特性和無線信道建模的相關知識,建立信道的CE-BEM模型,研究表明,信道基擴展模型的基系數表現出稀疏性,為壓縮感知信道估計提供了理論保證;搭建SISO-OFDM鏈路級仿真平臺,利用搭建的仿真平臺,對傳統信道估計算法和壓縮感知信道估計算法進行仿真分析。2.從理論上分析了壓縮感知理論應用于信道估計的可行性,建立了壓縮感知理論和OFDM系統信道估計算法的聯系;恢復算法是壓縮感知領域研究的重點之一,本文對多種壓縮感知恢復算法進行理論分析,針對算法的缺陷,設計算法的優(yōu)化方案;在鏈路級系統仿真平臺上,對多種壓縮感知信道估計算法進行了仿真驗證。3.論文針對正交匹配追蹤算法具有信道估計效率低、需要信道稀疏度作為先驗信息等缺點,進行算法優(yōu)化。首先,針對需要稀疏度作為先驗信息的缺點,采用稀疏度自適應匹配追蹤(CE-SAMP)算法進行信道估計,實驗結果表明,CE-SAMP信道估計算法不需要確定的稀疏度作為先驗信息,而且算法效率高于正交匹配追蹤算法;針對SAMP算法不能準確逼近真實稀疏度的缺陷,設計了可變步長稀疏度自適應匹配追蹤(CE-AsSAMP)估計算法;論文結合分段匹配追蹤和共軛梯度方向更新的思想,設計了分段共軛匹配追蹤(CE-StCGP)估計算法,并進行了仿真分析,實驗結果表明,在實現相同信道估計均方誤差性能的情況下,分段共軛匹配追蹤算法的恢復速度最快。
[Abstract]:In OFDM (orthogonal Frequency Division Multiplexing) system, Real-time channel estimation is an important guarantee for successful demodulation of received information. In order to obtain accurate channel response, the signal between base station and mobile station is affected by Doppler effect and multipath effect in high speed mobile and multipath transmission environment. The traditional channel estimation algorithm uses a large number of pilots to obtain channel state information CSI(Channel State information, which makes the pilot cost difficult to reduce and the system spectrum efficiency drop. Therefore, a good compromise between channel estimation performance and pilot overhead is achieved. Channel estimation has always been a problem that needs to be solved. This paper mainly studies the channel estimation algorithm based on compressed sensing theory. The main research contents are as follows: 1. The characteristics of wireless communication environment and the related knowledge of wireless channel modeling are studied. The CE-BEM model of the channel is established. The research shows that the base coefficient of the channel expansion model is sparse, which provides a theoretical guarantee for the compressed perceptual channel estimation. The SISO-OFDM link-level simulation platform is built and the simulation platform is built. The traditional channel estimation algorithm and compressed perceptual channel estimation algorithm are simulated. 2. The feasibility of applying compressed sensing theory to channel estimation is analyzed theoretically, and the relationship between compressed sensing theory and channel estimation algorithm in OFDM system is established. The restoration algorithm is one of the focal points in the field of compression perception. In this paper, a variety of compression sensing recovery algorithms are theoretically analyzed, and the optimization scheme of the algorithm is designed in view of the shortcomings of the algorithm; on the link-level system simulation platform, In this paper, several compressed perceptual channel estimation algorithms are simulated. 3. Aiming at the shortcomings of orthogonal matching tracking algorithm, such as low channel estimation efficiency and the need of channel sparsity as prior information, the algorithm is optimized. Aiming at the shortcoming of the need for sparse degree as prior information, a sparse adaptive matching tracking CE-SAMP algorithm is used for channel estimation. The experimental results show that the sparse degree is not required as prior information in CE-SAMP channel estimation algorithm. Moreover, the efficiency of the algorithm is higher than that of orthogonal matching tracking algorithm. Aiming at the defect that SAMP algorithm can not approach the true sparsity accurately, a variable step size adaptive matching tracking CE-AsSAMP-based estimation algorithm is designed. Combined with the idea of piecewise matching tracing and conjugate gradient direction updating, a piecewise conjugate matching tracking CE-StCGP-based estimation algorithm is designed and simulated. The experimental results show that the performance of mean square error of the same channel estimation is realized. Piecewise conjugate matching tracking algorithm has the fastest recovery speed.
【學位授予單位】：西南交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.53

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