基于稀疏信號的同步與信道估計技術研究
發(fā)布時間：2018-08-24 17:21
【摘要】：稀疏表示(Sparse Representation,SR)在不同領域都有很多的運用案例,在信號處理方面最主要的應用就是從大量的繁雜信號以不同的要求提取出重要的元素。稀疏表示理論放棄了香農(nóng)采樣定理(Shannon’s Sampling Theorm,SST)和奈奎斯特采樣準則(Nyquist Sampling Law,NSL)的原始測量,采用更有效的采樣率來測量原始采樣,隨后采用最優(yōu)的重構算法進行采樣重構。在壓縮感知的背景下,假設了所有的信號時稀疏或近似足夠稀疏,和主信號空間相比,可靠的信號集的大小在稀疏性的約束下極可能非常小,因此,基于稀疏表示的大量算法能有效地解決信號處理領域的信號重構和恢復問題。而且稀疏表示技術能節(jié)約大量的采樣時間和采樣存儲,具有很大的優(yōu)勢和潛力。全球定位系統(tǒng)的同步過程是為了獲取信號從定位器到定位衛(wèi)星的傳播時間而進行定位的技術。目前來說同步過程算法比較成熟,但如何進行更簡單有效的同步過程依然是一個可研究的方向,尤其是在如今的各類智能家居以及帶定位功能的微型設備的普及。信道估計技術歷來都是通信領域的重要研究方向之一,在現(xiàn)在第五代移動通信技術研究方向偏向于多天線系統(tǒng)的趨勢下,多天線系統(tǒng)下的信道估計研究是研究熱點之一。本論文從信號的稀疏表示特性出發(fā),分別研究了基于稀疏傅里葉變換的快速同步方法和多天線系統(tǒng)下的信道估計技術。第一章對同步技術和稀疏信道估計的背景以及研究現(xiàn)狀進行了基本的概括。第二章主要研究了用于稀疏表示的范式最小化問題的建模分析。對不同范數(shù)進行介紹,對它們之間的聯(lián)系進行分析,針對范式最小化問題的解決方案,著重介紹了基于貪心策略的稀疏重構算法。第三章首先對全球定位系統(tǒng)(Global Positioning System,GPS)同步過程進行闡述說明,建立同步模型;對稀疏傅里葉變換也進行詳細的表述。針對稀疏傅里葉變換利用混疊操作降低傅里葉變換規(guī)模的過程的啟示,借用到GPS同步過程以降低快速傅里葉變換(Fast Fourier Transformation,FFT)和快速反傅里葉變換(Inverse Fast Fourier Transform,IFFT)運算的規(guī)模,達到降低整個同步過程復雜度的目的。仿真結果也驗證了快速GPS的同步時間復雜度上比傳統(tǒng)的基于FFT的同步算法要小很多,具有實際運用的前景。第四章對多天線下的信道估計技術進行了研究,首先對多天線的信道進行分析,闡述了時域信道沖擊響應的抽頭稀疏的特點,針對這一特性,在多天線系統(tǒng)中天線之間具有相關性的情景下,在正交頻分復用(Orthogonal Frequency Division Multiplexing,OFDM)系統(tǒng)中給出相關相關性良好的先導序列代和OFDM塊中的導頻結合的導頻設計方案,利用得到的共同稀疏支持信息可以減少導頻使用,增加系統(tǒng)的頻譜利用率。最后對多天線系統(tǒng)相位利用旋轉(zhuǎn)導頻做信道估計進行研究,針對相關發(fā)射天線的信道相關性進行時域信道重要抽頭的錯開移位操作,在接收端利用已知的移位因子設計兩個重要抽頭分類算法并進行仿真驗證。第五章對全文的研究內(nèi)容進行了總結,同時指出了快速同步技術和相關天線信道估計依然需要進行的研究點和研究方向。
[Abstract]:Sparse Representation (SR) has many applications in different fields. The main application in signal processing is to extract important elements from a large number of complex signals with different requirements. The original measurement of Nyquist Sampling Law (NSL) uses a more efficient sampling rate to measure the original sample, and then uses an optimal reconstruction algorithm to reconstruct the sample. The lower pole may be very small, so a large number of algorithms based on sparse representation can effectively solve the problem of signal reconstruction and recovery in the field of signal processing. At present, the algorithm of synchronization process is relatively mature, but how to carry out more simple and effective synchronization process is still a research direction, especially in today's smart homes and the popularity of micro-devices with positioning function. Channel estimation technology has always been a communication collar. One of the important research directions in the domain is that the research direction of the fifth generation mobile communication technology is inclined to multi-antenna system, and the channel estimation in multi-antenna system is one of the research hotspots. Chapter 1 summarizes the background and research status of synchronization technology and sparse channel estimation. Chapter 2 mainly studies the modeling and analysis of the normal form minimization problem for sparse representation. In the third chapter, the synchronization process of Global Positioning System (GPS) is described and the synchronization model is established. The sparse Fourier transform is also described in detail. The aliasing operation is used to reduce Fourier transform for sparse Fourier transform. The enlightenment of the process of transforming scale is borrowed from the GPS synchronization process to reduce the operation scale of Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) to reduce the complexity of the whole synchronization process. In the fourth chapter, the channel estimation technology under multi-antenna is studied. Firstly, the channel of multi-antenna is analyzed, and the characteristics of sparse time-domain channel impulse response taps are described. In the case of correlation, the pilot sequence generation with good correlation and the pilot combination in the OFDM block are given in the Orthogonal Frequency Division Multiplexing (OFDM) system. The common sparse support information can be used to reduce the pilot usage and increase the spectrum utilization of the system. Finally, the phase estimation of multi-antenna system using rotating pilot is studied. The important taps of time-domain channel are staggered and shifted according to the channel correlation of correlated transmit antennas. Two important tap classification algorithms are designed and verified by simulation at the receiver using known shift factors. The capacitance is summarized, and the research points and directions of fast synchronization technology and related antenna channel estimation are pointed out.
【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】：TN911
本文編號：2201506
[Abstract]:Sparse Representation (SR) has many applications in different fields. The main application in signal processing is to extract important elements from a large number of complex signals with different requirements. The original measurement of Nyquist Sampling Law (NSL) uses a more efficient sampling rate to measure the original sample, and then uses an optimal reconstruction algorithm to reconstruct the sample. The lower pole may be very small, so a large number of algorithms based on sparse representation can effectively solve the problem of signal reconstruction and recovery in the field of signal processing. At present, the algorithm of synchronization process is relatively mature, but how to carry out more simple and effective synchronization process is still a research direction, especially in today's smart homes and the popularity of micro-devices with positioning function. Channel estimation technology has always been a communication collar. One of the important research directions in the domain is that the research direction of the fifth generation mobile communication technology is inclined to multi-antenna system, and the channel estimation in multi-antenna system is one of the research hotspots. Chapter 1 summarizes the background and research status of synchronization technology and sparse channel estimation. Chapter 2 mainly studies the modeling and analysis of the normal form minimization problem for sparse representation. In the third chapter, the synchronization process of Global Positioning System (GPS) is described and the synchronization model is established. The sparse Fourier transform is also described in detail. The aliasing operation is used to reduce Fourier transform for sparse Fourier transform. The enlightenment of the process of transforming scale is borrowed from the GPS synchronization process to reduce the operation scale of Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) to reduce the complexity of the whole synchronization process. In the fourth chapter, the channel estimation technology under multi-antenna is studied. Firstly, the channel of multi-antenna is analyzed, and the characteristics of sparse time-domain channel impulse response taps are described. In the case of correlation, the pilot sequence generation with good correlation and the pilot combination in the OFDM block are given in the Orthogonal Frequency Division Multiplexing (OFDM) system. The common sparse support information can be used to reduce the pilot usage and increase the spectrum utilization of the system. Finally, the phase estimation of multi-antenna system using rotating pilot is studied. The important taps of time-domain channel are staggered and shifted according to the channel correlation of correlated transmit antennas. Two important tap classification algorithms are designed and verified by simulation at the receiver using known shift factors. The capacitance is summarized, and the research points and directions of fast synchronization technology and related antenna channel estimation are pointed out.
【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】：TN911
【參考文獻】
相關碩士學位論文 前1條
1 王明明;GPS軟件接收機基帶算法研究[D];山東大學;2006年
，本文編號：2201506
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