【摘要】：無(wú)線通信系統(tǒng)的性能在很大程度上受到無(wú)線信道的約束。無(wú)線信號(hào)傳播會(huì)遭受各種復(fù)雜地物影響，接收端信號(hào)在幅度、相位和頻率方面會(huì)發(fā)生不同程度的失真。正交頻分復(fù)用(Orthogonal Frequency Division Multiplex, OFDM)技術(shù)雖能有效克服頻率選擇性衰落，但是對(duì)頻率偏移非常敏感，因此，信道估計(jì)顯得尤為重要。傳統(tǒng)關(guān)于OFDM系統(tǒng)的信道估計(jì)方法通常是假設(shè)信道具有豐富多徑，從而利用大量導(dǎo)頻信號(hào)獲取準(zhǔn)確的信道狀態(tài)信息，這極大地降低了系統(tǒng)資源利用率。為解決這一問(wèn)題，本文基于壓縮感知理論，對(duì)OFDM系統(tǒng)信道估計(jì)方法展開(kāi)研究。 依據(jù)壓縮感知理論的三大關(guān)鍵技術(shù)，本文從信道系數(shù)的稀疏表示、導(dǎo)頻序列設(shè)計(jì)和重構(gòu)算法選取三個(gè)方面展開(kāi)研究，針對(duì)現(xiàn)有稀疏信道估計(jì)算法存在的一些問(wèn)題，給出相應(yīng)解決方法。 針對(duì)現(xiàn)有稀疏信道估計(jì)算法只考慮信道的頻率選擇性衰落，本文同時(shí)考慮信道的時(shí)間選擇性衰落。在實(shí)際系統(tǒng)中，信道時(shí)延和多普勒頻移通常不能被整數(shù)倍采樣，由此造成的能量泄漏問(wèn)題將極大減少等效信道稀疏性，本文針對(duì)這一問(wèn)題通過(guò)提高信道離散精度，用過(guò)完備字典代替現(xiàn)有的傅里葉正交基提高等效信道系數(shù)在字典域的稀疏性，從而減少重構(gòu)算法所需的觀測(cè)值，即導(dǎo)頻數(shù)量。仿真結(jié)果表明，無(wú)論是單天線還是多天線信道，過(guò)完備字典方法雖然增加一定的計(jì)算復(fù)雜度，但有效提高了信道估計(jì)精度，同時(shí)減少了對(duì)導(dǎo)頻數(shù)量的需求。 針對(duì)現(xiàn)有多天線稀疏信道估計(jì)算法只是簡(jiǎn)單將多天線信道劃分為多個(gè)單對(duì)單信道的處理方式，本文分析多天線信道的聯(lián)合稀疏特性，利用分布式壓縮感知理論方法進(jìn)行聯(lián)合信道估計(jì)。仿真結(jié)果表明，聯(lián)合稀疏信道估計(jì)方法利用信道間的互相關(guān)和自相關(guān)性，使得對(duì)聯(lián)合稀疏支撐集估計(jì)更準(zhǔn)確，，其性能要優(yōu)于基于單對(duì)單稀疏的信道估計(jì)方法。
[Abstract]:The performance of wireless communication systems is largely constrained by wireless channels. Wireless signal propagation will be affected by various complex ground objects, and the amplitude, phase and frequency of the signal at the receiving end will be distorted to varying degrees. Orthogonal Frequency Division Multiplexing (Orthogonal Frequency Division Multiplex, OFDM) can overcome frequency selective fading effectively, but it is very sensitive to frequency offset, so channel estimation is very important. The traditional channel estimation methods for OFDM systems usually assume that the channel has abundant multipath, so a large number of pilot signals are used to obtain accurate channel state information, which greatly reduces the system resource utilization. In order to solve this problem, the channel estimation method of OFDM system is studied based on compressed sensing theory. According to the three key technologies of compressed sensing theory, this paper studies the sparse representation of channel coefficients, pilot sequence design and reconstruction algorithm selection, aiming at some problems of existing sparse channel estimation algorithms. The corresponding solutions are given. Since the existing sparse channel estimation algorithms only consider the frequency selective fading of the channel, the time selective fading of the channel is also considered in this paper. In practical systems, the channel delay and Doppler frequency shift can not be sampled by integer multiple sampling, and the energy leakage problem will greatly reduce the equivalent channel sparsity. In this paper, we improve the channel dispersion accuracy by improving the channel dispersion accuracy. The overcomplete dictionary is used to replace the existing Fourier orthogonal basis to improve the sparsity of the equivalent channel coefficients in the dictionary domain, thus reducing the observed values required for the reconstruction algorithm, namely, the number of pilots. Simulation results show that the over-complete dictionary method increases the computational complexity of both single antenna and multi-antenna channels, but effectively improves the channel estimation accuracy and reduces the need for the number of pilots. In this paper, the joint sparse characteristic of multi-antenna channel is analyzed in view of the fact that the existing multi-antenna sparse channel estimation algorithm is only a simple way to divide the multi-antenna channel into several single-pair single-channel. Joint channel estimation is based on distributed compressed sensing theory. Simulation results show that the joint sparse channel estimation method makes use of the cross-correlation and self-correlation between the channels to estimate the joint sparse support set more accurately and the performance of the joint sparse channel estimation method is better than that based on the single-pair sparse channel estimation method.
【學(xué)位授予單位】：重慶郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN929.53

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