基于压缩感知理论的无线多径信道估计方法研究
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摘要
随着人们对无线通信需求地不断增长,提供高质量、高速率的数据及多媒体业务成为移动通信领域的首要任务。模拟通信系统到数字通信系统的转变,是移动通信技术的一次重大革命,在频谱效率、系统容量、保密性以及通话质量方面得到了极大的改善。随着无线通信技术的进一步发展,正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)技术的提出,有效地提高频谱的利用率,并有效抵抗信道衰落和信道时延扩展的影响。多输入多输出(Multiple Input Multiple Output, MIMO)技术则通过空间分集增加无线系统的覆盖范围,有效地提高系统容量。目前,中继(relay)技术的研究,能够在不增加基站数目的前提下,提高小区边缘的通信质量。
虽然OFDM、MIMO和中继技术有诸多优点,但是,在具体实际应用中,仍然面临很多问题。OFDM系统对同步误差甚为敏感,在信号传输过程中,由于信号受周围环境及障碍物影响,产生不同程度地衰落和时延,时间同步误差会造成符号间干扰(Inter Symbol Interference, ISI),频率同步误差会产生子载波间干扰(Inter Carrier Interference, ICI)。只有对信道特征有很好的了解,才能有效克服干扰和失真。加入MIMO或中继后,符号间干扰和码间干扰更加严重,从而影响系统性能。因此,接收端需要获得精确的信道状态信息(Channel State Information, CSI),精确的信道估计起到尤为重要的作用。针对上述问题,论文对无线多径信道的信道估计进行了研究。重点分析压缩感知理论在无线多径信道估计中应用的可行性,并重点研究了基于压缩感知理论的单天线系统、多天线系统、双向中继系统以及水声通信系统中的信道估计算法,并取得相应的科研成果。论文主要完成了以下具有创新性的研究成果。
首先,论文从典型的单天线系统入手,通过分析其信道特征,建立稀疏信道模型,将传统的线性算法转化为非线性算法,将频域信道估计方法转化为时域信道估计方法,并利用压缩感知的凸优化算法来恢复信道状态信息,即压缩信道感知。所提方法充分运用无线多径信道固有的稀疏性特点,利用一阶范式最优化来恢复信道冲激响应,达到使用较短的训练序列获得较高的信道估计精确度的目的。
其次,研究发现,多天线系统与单天线系统相比,具有天线数目增多,数据传输速率较高,信道状态复杂等特点,因此,在信噪比低的快衰落信道状态下难以获得准确的信道估计。快速而准确的恢复信道估计状态是亟需解决的关键问题。对于多天线系统,在快衰落条件下,传统线性信道估计算法,如LS (Least Squares),虽然信道估计计算复杂度较低,但是信道估计精确度不高。基于压缩感知的凸优化信道估计算法,能显著提高信道估计性能,然而往往计算比较复杂。基于二者的优缺点,本文提出了一种基于压缩采样匹配追踪(Compressive Sampling Matching Pursuit, CoSaMP)的高效信道估计算法。该算法对高速移动的多天线系统,在快衰落信道情况下,能以很高的概率恢复信道状态信息,准确性和实时性有了很大地提高。另外,利用离散傅里叶变换(Discrete Fourier Transform, DFT)的特性,该算法可进一步扩展运用于MIMO-OFDM系统的信道估计中。
再次,就目前趋势而言,点对点通信已不能满足人们对通信网的需求,于是,催生了中继网络的发展。双向中继系统是指两个节点通过半双工中继进行信息的交互与传递,两节点直接无直达路径。对于双向中继系统,信道状态信息由传统点对点的单一的未知信道变量转化为三点间两个未知的信道变量,因此,信道估计过程复杂繁琐。本论文在深入研究双向中继系统信道特点的基础上,提出一种基于压缩感知理论的自适应的联合信道估计算法,从而巧妙的将二维变量转化为一维变量,该算法不但能显著提高双向中继系统的信道估计性能,同时,也大大提高了信道估计效率以及频谱利用率。
此外,本论文还关注先进理论在实际场景中的应用——基于压缩感知的水声通信系统信道估计的研究与应用。目前,声波是水下唯一进行远程信息传输的媒体,水声信道不但受频率的影响,还与海水的运动,高度以及密度等有关,因此,水声信道要比无线电信道复杂得多。本文深入研究水声信道的特殊复杂性,根据水声信道的稀疏表示模型与非相关观测矩阵构建压缩感知算法,并筛选出适合水声信道的重构算法,为水声通信系统信道估计提出了一种可行的解决途径,基于压缩感知的水声通信系统信道估计是该领域具有创新性的研究方向。
With the growing demand for wireless communications, providing high-quality, high-speed data and multimedia services becomes the primary task of the mobile communications. It is a great revolution in mobile communication technology for the change from analog communication system to the digital communication system. The revolutionary improvement includes spectrum efficiency, the system capacity, confidentiality and the call quality. With the development of wireless communications, the proposal of orthogonal frequency division multiplexing (OFDM) has improved the spectrum utilization rate effectively, and reduced the channel fading and channel delay spread effect effectively. The multiple input multiple output (MIMO) technology has improved the system capacity effectively by increase the wireless system coverage using spatial diversity. At present, the research in relay technology could improve the communication quality of the community edge without increase the number of base station.
Though the OFDM, MIMO and relay technologies have many advantages, they still face a lot of problems in some specific applications. The OFDM system is sensitive to synchronization error. In the process of signal transmission, the signal gets througth time delay and power attenuation due to the constructions and obstacles nearby. The time synchronization error can cause inter-symbol interference (ISI), while the frequency synchronization error can cause inter-carrier interference (ICI). The interference and distortion could be overcome effectively only the channel information is well understood. When the MIMO or relay is jointed, the ISI and ICI get lager, which influence the system performance seriously. Therefore, the receiver needs to get accurate channel state information (CSI), which is also very important for coherent detection and equalization. Concerned about the above issue, this paper researched the channel estimation of the wireless multipath channel. Specifically, emphatically analyzes the feasibility of the application of compressed sensing theory on wireless multipath channel estimation, mainly studies the channel estimation algorithm of single antenna system, multiple antenna system, two-way relay system and underwater acoustic system based on compressed sensing theory. Corresponding scientific research achievements have been obtained. This thesis mainly completed the following innovative research.
First, the classic single antenna system has been studied. The research includes analyzing the channel characteristics, creating the sparse channel model, and so on. The research shows that compressed sensing theory can be used for sparse channel estimation. This method can transform the traditional linear algorithm into nonlinear algorithm, and recover the channel state information in time domain instead of frequency domain. In other word, this method makes full use of the wireless multipath channel inherent sparse characteristic, recovers the channel impulse response by the l1norm minimization, and obtains higher channel estimation accuracy with fewer training signals.
Second, compare with the single antenna system, the multiple antenna system has the characteristics of more antenna number, higher data transmission rate, and more complicated channel state. Therefore, it is difficult to obtain accurate channel estimation in low SNR and fast fading conditions. Channel estimation aims for fast and accurate recovery of channel state information. For the multiple antenna system, in the fast fading conditions, the traditional linear channel estimation algorithm, i.e., least squares (LS), has lower computational complexity; however, the channel estimation accuracy is far from satisfied. The compressive sensing-based convex optimizing estimation algorithm, could significantly improve the performance of channel estimation, but implement complex. Based on the advantages and disadvantages of the method mentioned above, this thesis proposed a highly efficient channel estimation algorithm based on the compressive sampling matching pursuit (CoSaMP). For the high-speed multiple antenna systems, this algorithm can recover the channel state information with very high probability even in fast fading conditions. In the meanwhile, both the real-time performance and estimation accuracy have been greatly improved. By using the discrete Fourier transform (DFT), we could extend the application in the channel estimation of MIMO-OFDM systems.
Third, the conventional point to point systems have not satisfied people's demand, therefore, the relay networks have been intensively researched. For the two-way relay system, the two nodes interact and transfer information by the half duplex relay, and there is no direct path for the two nodes. The channel state information changed from the traditional single unknown channel variable into two unknown channel variables. Therefore, the channel estimation process is much more complicated than the point to point systems. Based on the thorough research of the channel characteristics of the two-way relay system, our thesis proposed an adaptive joint channel estimation algorithm based on the compressive sensing theory. Thus, transform the2-dimensional variables into a1-dimensional variable ingeniously, and improve the channel estimation performance of the two-way relay system significantly.
Moreover, this thesis is focused on the application of the advanced theory in the practical scene-the research and application of compressed sensing-based underwater acoustic channel estimation. At present, sound wave is the only one carrier for remote underwater information transmission. The underwater acoustic channel is influenced not only by the frequency, but also by the sea movement, height and density and so on. Therefore, underwater acoustic channel is much more complicated than radio channels. This thesis presents a thorough study on the complexity of the underwater acoustic channel, constructs compressed sensing algorithms according to the sparse underwater acoustic channel model and non-relevant observation matrix, and selects the suitable reconstruction algorithm for the underwater acoustic channel estimation. This thesis provides a feasible solution to the channel estimation of underwater acoustic communication system and a new research direction for this field.
虽然OFDM、MIMO和中继技术有诸多优点,但是,在具体实际应用中,仍然面临很多问题。OFDM系统对同步误差甚为敏感,在信号传输过程中,由于信号受周围环境及障碍物影响,产生不同程度地衰落和时延,时间同步误差会造成符号间干扰(Inter Symbol Interference, ISI),频率同步误差会产生子载波间干扰(Inter Carrier Interference, ICI)。只有对信道特征有很好的了解,才能有效克服干扰和失真。加入MIMO或中继后,符号间干扰和码间干扰更加严重,从而影响系统性能。因此,接收端需要获得精确的信道状态信息(Channel State Information, CSI),精确的信道估计起到尤为重要的作用。针对上述问题,论文对无线多径信道的信道估计进行了研究。重点分析压缩感知理论在无线多径信道估计中应用的可行性,并重点研究了基于压缩感知理论的单天线系统、多天线系统、双向中继系统以及水声通信系统中的信道估计算法,并取得相应的科研成果。论文主要完成了以下具有创新性的研究成果。
首先,论文从典型的单天线系统入手,通过分析其信道特征,建立稀疏信道模型,将传统的线性算法转化为非线性算法,将频域信道估计方法转化为时域信道估计方法,并利用压缩感知的凸优化算法来恢复信道状态信息,即压缩信道感知。所提方法充分运用无线多径信道固有的稀疏性特点,利用一阶范式最优化来恢复信道冲激响应,达到使用较短的训练序列获得较高的信道估计精确度的目的。
其次,研究发现,多天线系统与单天线系统相比,具有天线数目增多,数据传输速率较高,信道状态复杂等特点,因此,在信噪比低的快衰落信道状态下难以获得准确的信道估计。快速而准确的恢复信道估计状态是亟需解决的关键问题。对于多天线系统,在快衰落条件下,传统线性信道估计算法,如LS (Least Squares),虽然信道估计计算复杂度较低,但是信道估计精确度不高。基于压缩感知的凸优化信道估计算法,能显著提高信道估计性能,然而往往计算比较复杂。基于二者的优缺点,本文提出了一种基于压缩采样匹配追踪(Compressive Sampling Matching Pursuit, CoSaMP)的高效信道估计算法。该算法对高速移动的多天线系统,在快衰落信道情况下,能以很高的概率恢复信道状态信息,准确性和实时性有了很大地提高。另外,利用离散傅里叶变换(Discrete Fourier Transform, DFT)的特性,该算法可进一步扩展运用于MIMO-OFDM系统的信道估计中。
再次,就目前趋势而言,点对点通信已不能满足人们对通信网的需求,于是,催生了中继网络的发展。双向中继系统是指两个节点通过半双工中继进行信息的交互与传递,两节点直接无直达路径。对于双向中继系统,信道状态信息由传统点对点的单一的未知信道变量转化为三点间两个未知的信道变量,因此,信道估计过程复杂繁琐。本论文在深入研究双向中继系统信道特点的基础上,提出一种基于压缩感知理论的自适应的联合信道估计算法,从而巧妙的将二维变量转化为一维变量,该算法不但能显著提高双向中继系统的信道估计性能,同时,也大大提高了信道估计效率以及频谱利用率。
此外,本论文还关注先进理论在实际场景中的应用——基于压缩感知的水声通信系统信道估计的研究与应用。目前,声波是水下唯一进行远程信息传输的媒体,水声信道不但受频率的影响,还与海水的运动,高度以及密度等有关,因此,水声信道要比无线电信道复杂得多。本文深入研究水声信道的特殊复杂性,根据水声信道的稀疏表示模型与非相关观测矩阵构建压缩感知算法,并筛选出适合水声信道的重构算法,为水声通信系统信道估计提出了一种可行的解决途径,基于压缩感知的水声通信系统信道估计是该领域具有创新性的研究方向。
With the growing demand for wireless communications, providing high-quality, high-speed data and multimedia services becomes the primary task of the mobile communications. It is a great revolution in mobile communication technology for the change from analog communication system to the digital communication system. The revolutionary improvement includes spectrum efficiency, the system capacity, confidentiality and the call quality. With the development of wireless communications, the proposal of orthogonal frequency division multiplexing (OFDM) has improved the spectrum utilization rate effectively, and reduced the channel fading and channel delay spread effect effectively. The multiple input multiple output (MIMO) technology has improved the system capacity effectively by increase the wireless system coverage using spatial diversity. At present, the research in relay technology could improve the communication quality of the community edge without increase the number of base station.
Though the OFDM, MIMO and relay technologies have many advantages, they still face a lot of problems in some specific applications. The OFDM system is sensitive to synchronization error. In the process of signal transmission, the signal gets througth time delay and power attenuation due to the constructions and obstacles nearby. The time synchronization error can cause inter-symbol interference (ISI), while the frequency synchronization error can cause inter-carrier interference (ICI). The interference and distortion could be overcome effectively only the channel information is well understood. When the MIMO or relay is jointed, the ISI and ICI get lager, which influence the system performance seriously. Therefore, the receiver needs to get accurate channel state information (CSI), which is also very important for coherent detection and equalization. Concerned about the above issue, this paper researched the channel estimation of the wireless multipath channel. Specifically, emphatically analyzes the feasibility of the application of compressed sensing theory on wireless multipath channel estimation, mainly studies the channel estimation algorithm of single antenna system, multiple antenna system, two-way relay system and underwater acoustic system based on compressed sensing theory. Corresponding scientific research achievements have been obtained. This thesis mainly completed the following innovative research.
First, the classic single antenna system has been studied. The research includes analyzing the channel characteristics, creating the sparse channel model, and so on. The research shows that compressed sensing theory can be used for sparse channel estimation. This method can transform the traditional linear algorithm into nonlinear algorithm, and recover the channel state information in time domain instead of frequency domain. In other word, this method makes full use of the wireless multipath channel inherent sparse characteristic, recovers the channel impulse response by the l1norm minimization, and obtains higher channel estimation accuracy with fewer training signals.
Second, compare with the single antenna system, the multiple antenna system has the characteristics of more antenna number, higher data transmission rate, and more complicated channel state. Therefore, it is difficult to obtain accurate channel estimation in low SNR and fast fading conditions. Channel estimation aims for fast and accurate recovery of channel state information. For the multiple antenna system, in the fast fading conditions, the traditional linear channel estimation algorithm, i.e., least squares (LS), has lower computational complexity; however, the channel estimation accuracy is far from satisfied. The compressive sensing-based convex optimizing estimation algorithm, could significantly improve the performance of channel estimation, but implement complex. Based on the advantages and disadvantages of the method mentioned above, this thesis proposed a highly efficient channel estimation algorithm based on the compressive sampling matching pursuit (CoSaMP). For the high-speed multiple antenna systems, this algorithm can recover the channel state information with very high probability even in fast fading conditions. In the meanwhile, both the real-time performance and estimation accuracy have been greatly improved. By using the discrete Fourier transform (DFT), we could extend the application in the channel estimation of MIMO-OFDM systems.
Third, the conventional point to point systems have not satisfied people's demand, therefore, the relay networks have been intensively researched. For the two-way relay system, the two nodes interact and transfer information by the half duplex relay, and there is no direct path for the two nodes. The channel state information changed from the traditional single unknown channel variable into two unknown channel variables. Therefore, the channel estimation process is much more complicated than the point to point systems. Based on the thorough research of the channel characteristics of the two-way relay system, our thesis proposed an adaptive joint channel estimation algorithm based on the compressive sensing theory. Thus, transform the2-dimensional variables into a1-dimensional variable ingeniously, and improve the channel estimation performance of the two-way relay system significantly.
Moreover, this thesis is focused on the application of the advanced theory in the practical scene-the research and application of compressed sensing-based underwater acoustic channel estimation. At present, sound wave is the only one carrier for remote underwater information transmission. The underwater acoustic channel is influenced not only by the frequency, but also by the sea movement, height and density and so on. Therefore, underwater acoustic channel is much more complicated than radio channels. This thesis presents a thorough study on the complexity of the underwater acoustic channel, constructs compressed sensing algorithms according to the sparse underwater acoustic channel model and non-relevant observation matrix, and selects the suitable reconstruction algorithm for the underwater acoustic channel estimation. This thesis provides a feasible solution to the channel estimation of underwater acoustic communication system and a new research direction for this field.
引文
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