OFDM系統(tǒng)中基于壓縮感知的雙選擇性信道估計方法研究
發(fā)布時間：2019-01-05 21:28
【摘要】：無線通信系統(tǒng)的性能在很大程度上受到無線信道的約束。無線信號傳播會遭受各種復雜地物影響，接收端信號在幅度、相位和頻率方面會發(fā)生不同程度的失真。正交頻分復用(Orthogonal Frequency Division Multiplex, OFDM)技術雖能有效克服頻率選擇性衰落，但是對頻率偏移非常敏感，因此，信道估計顯得尤為重要。傳統(tǒng)關于OFDM系統(tǒng)的信道估計方法通常是假設信道具有豐富多徑，從而利用大量導頻信號獲取準確的信道狀態(tài)信息，這極大地降低了系統(tǒng)資源利用率。為解決這一問題，本文基于壓縮感知理論，對OFDM系統(tǒng)信道估計方法展開研究。 依據(jù)壓縮感知理論的三大關鍵技術，本文從信道系數(shù)的稀疏表示、導頻序列設計和重構算法選取三個方面展開研究，針對現(xiàn)有稀疏信道估計算法存在的一些問題，給出相應解決方法。 針對現(xiàn)有稀疏信道估計算法只考慮信道的頻率選擇性衰落，本文同時考慮信道的時間選擇性衰落。在實際系統(tǒng)中，信道時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題將極大減少等效信道稀疏性，本文針對這一問題通過提高信道離散精度，用過完備字典代替現(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，從而減少重構算法所需的觀測值，即導頻數(shù)量。仿真結果表明，無論是單天線還是多天線信道，過完備字典方法雖然增加一定的計算復雜度，但有效提高了信道估計精度，同時減少了對導頻數(shù)量的需求。 針對現(xiàn)有多天線稀疏信道估計算法只是簡單將多天線信道劃分為多個單對單信道的處理方式，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行聯(lián)合信道估計。仿真結果表明，聯(lián)合稀疏信道估計方法利用信道間的互相關和自相關性，使得對聯(lián)合稀疏支撐集估計更準確，，其性能要優(yōu)于基于單對單稀疏的信道估計方法。
[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.
【學位授予單位】：重慶郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN929.53
本文編號：2402331
[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.
【學位授予單位】：重慶郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN929.53
【參考文獻】
相關期刊論文 前8條
1 周鵬;趙春明;盛彬;;MIMO-OFDM系統(tǒng)中基于導頻輔助的信道估計[J];電子與信息學報;2007年01期
2 解志斌;薛同思;田雨波;鄒維辰;劉慶華;馬國華;;一種稀疏增強的壓縮感知MIMO-OFDM信道估計算法[J];電子與信息學報;2013年03期
3 吳海佳;張雄偉;陳衛(wèi)衛(wèi);;壓縮感知新技術專題講座(二) 第4講 壓縮感知理論中測量矩陣的構造方法[J];軍事通信技術;2012年01期
4 應軍科;鐘杰;趙民建;蔡云龍;;基于壓縮感知的非整數(shù)點多徑信道估計算法[J];哈爾濱工業(yè)大學學報;2013年11期
5 劉開華;陳偉凱;馬永濤;;OFDM系統(tǒng)中一種雙選擇性稀疏信道壓縮感知方法[J];天津大學學報;2012年12期
6 李子,蔡躍明,闞春榮,徐友云;OFDM系統(tǒng)中的盲信道估計[J];信號處理;2005年05期
7 朱行濤;劉郁林;趙翔;徐舜;;MIMO-OFDM系統(tǒng)中稀疏信道估計算法研究[J];云南大學學報(自然科學版);2007年06期
8 王妮娜;桂冠;張治;唐恬;;基于壓縮感知的MIMO系統(tǒng)稀疏信道估計[J];應用科學學報;2011年04期
相關博士學位論文 前1條
1 王東昊;OFDM系統(tǒng)中壓縮感知信道估計技術的研究[D];北京郵電大學;2011年
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