本文選題：壓縮感知 + OFDM��； 參考：《電子科技大學》2017年碩士論文

【摘要】：近年來,隨著壓縮感知(Compressed sensing,CS)理論的普及,該技術已經(jīng)被廣泛應用于正交頻分復用(Orthogonal Frequency Division Multiplexing,OFDM)的稀疏信道估計研究中。不同于傳統(tǒng)的信道估計方法,基于CS的信道估計技術可以利用極低的采樣速率對信號進行采樣,并有效地重建信號,這樣可以減少對導頻的使用,提高系統(tǒng)的傳輸效率。其中,常見的稀疏信號重構算法主要有:正交匹配追蹤(Orthogonal matching pursuit,OMP)算法,壓縮采樣匹配追蹤(Compressive sampling matching pursuit,CoSaMP)算法等貪婪算法。此外,一些凸優(yōu)化算法如SpaRSA(Sparse Reconstruction by Separable Approximation)和YALL1等算法同樣也可以作為重構算法對稀疏信號進行恢復。然而,大部分學者把研究的重點都放在了信道估計算法的改進與創(chuàng)新上,卻忽略了影響信道估計性能的其他因素,如導頻結(jié)構的設計。現(xiàn)有研究表明,不同的導頻結(jié)構對稀疏信道估計的最終性能也起到了十分重要的作用。因此,本文將對OFDM稀疏信道估計下的導頻結(jié)構設計問題進行重點研究,通過對導頻結(jié)構進行針對性設計,提高整個系統(tǒng)的信道估計性能。本文首先在確定性導頻結(jié)構設計標準的基礎上,對現(xiàn)有標準及其實現(xiàn)算法進行了歸納與總結(jié),并在此基礎上實現(xiàn)了完善與改進,減小了算法的復雜度,提高了算法的收斂速率。此外,本文還給出了一種自適應導頻結(jié)構優(yōu)化設計算法。不同于傳統(tǒng)的確定性導頻設計算法,該自適應算法將根據(jù)實際傳輸環(huán)境對導頻結(jié)構進行動態(tài)的調(diào)整,使整個系統(tǒng)始終保持較高的估計性能。本文的主要工作如下:1.本文對現(xiàn)有的確定性導頻結(jié)構設計標準進行了歸納與總結(jié),并針對不同的標準分別給出了一種具體的算法實現(xiàn)。通過對仿真實驗結(jié)果的分析與對比,詳細說明了各標準所適用的場景以及估計性能之間的差異。2.本文在傳統(tǒng)MIP標準的基礎上對其實現(xiàn)算法進行了改進,通過與遺傳算法的結(jié)合,降低了原有算法的復雜度,提高了算法的穩(wěn)定性及收斂速率。3.本文對MIP標準本身進行了完善,給出了一種改進后的確定性導頻結(jié)構設計標準。與一般的MIP標準相比,改進后的標準更充分地考慮了稀疏信號在恢復過程中的其他因素,豐富了對測量矩陣的設計,使在該標準下所得到的導頻結(jié)構具有更加穩(wěn)定更加精確的估計性能。4.本文提出了一種自適應導頻結(jié)構設計思想,并給出了其具體的算法實現(xiàn)。不同于傳統(tǒng)的確定性導頻結(jié)構設計,該思想強調(diào)將導頻結(jié)構的設計問題與實時的信道估計相結(jié)合,利用實時估計出的稀疏信道脈沖響應(Channel impulse response,CIR)反饋到導頻結(jié)構的重構設計上。此外,為了解決貪婪算法在恢復精度上欠缺的問題,本文還利用了凸優(yōu)化算法中的最小1l范數(shù)模型對導頻結(jié)構進行進一步的篩選,并模擬遺傳算法中的迭代過程不斷地對導頻結(jié)構進行重建,直至其最終收斂。5.為了降低自適應算法在工程應用中的實現(xiàn)難度,本文還將確定性導頻結(jié)構設計算法與自適應性導頻結(jié)構設計算法相結(jié)合,通過對導頻結(jié)構進行一定的預處理,減少了自適應性算法所需要的收斂時間,提高了算法的效率。
[Abstract]:In recent years, with the popularity of Compressed sensing (CS) theory, this technology has been widely used in the research of sparse channel estimation for orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM). Unlike traditional channel estimation methods, the channel estimation based on CS can make use of very low sampling rates. The signal is sampled and the signal is rebuilt effectively, which can reduce the use of pilot and improve the transmission efficiency of the system. Among them, the common sparse signal reconstruction algorithms are orthogonal matching tracking (Orthogonal matching pursuit, OMP), compressed sampling matching tracking (Compressive sampling matching pursuit, CoSaMP) and so on In addition, some convex optimization algorithms such as SpaRSA (Sparse Reconstruction by Separable Approximation) and YALL1 can also be used as reconstruction algorithms to restore sparse signals. However, most of the scholars focus on the improvement and innovation of the channel estimation method, but ignore the influence of channel estimation. Other factors, such as the design of pilot structures, have shown that different pilot structures play a very important role in the final performance of sparse channel estimation. Therefore, this paper will focus on the design of pilot structure under OFDM sparse channel estimation, and improve the pilot structure to improve the pilot structure. The performance of the channel estimation for the whole system. Firstly, based on the design standard of deterministic pilot structure, the existing standard and its implementation algorithm are summarized and summarized. On this basis, the improvement and improvement are realized, the complexity of the algorithm is reduced and the convergence rate of the algorithm is improved. In addition, this paper also gives an adaptive guide. The frequency structure optimization design algorithm is different from the traditional deterministic pilot design algorithm. The adaptive algorithm will dynamically adjust the pilot structure according to the actual transmission environment, and make the whole system maintain a high estimation performance. The main work of this paper is as follows: 1. this paper has carried out the standard of certain deterministic pilot structure design. A specific algorithm implementation is given for different standards. Through the analysis and comparison of the results of the simulation experiments, the scene and the difference between the estimated performance are explained in detail..2. is improved on the basis of the traditional MIP standard and through the genetic algorithm. Combining it, the complexity of the original algorithm is reduced, the stability and the convergence rate of the algorithm are improved.3.. The MIP standard itself is perfected in this paper. A modified deterministic pilot structure design standard is given. Compared with the general MIP standard, the improved standard more fully considers the other factors of the sparse signal in the recovery process. The design of the measurement matrix is enriched and the pilot structure obtained under this standard has a more stable and more accurate estimation performance..4. this paper proposes an adaptive pilot structure design idea and gives its specific algorithm implementation. Combined with real-time channel estimation, the real-time estimated sparse channel impulse response (Channel impulse response, CIR) is used to feed back to the pilot structure reconfiguration design. In addition, in order to solve the problem that the greedy algorithm lacks the recovery precision, this paper also uses the minimum 1L norm model in the convex optimization algorithm for the pilot junction. Further screening is carried out, and the iterative process in the genetic algorithm is simulated continuously to reconstruct the pilot structure until its final convergence is.5. in order to reduce the difficulty of realizing the adaptive algorithm in the engineering application. The preprocessing of the structure reduces the convergence time of the adaptive algorithm and improves the efficiency of the algorithm.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.53

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