OFDM系统信道估计技术的研究
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摘要
正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)作为一种多载波并行传输技术,具有高效的频谱利用率、优良的抗多径衰落能力和简单的系统硬件结构,是未来移动通信系统最有竞争力的候选方案。在OFDM系统中,实时、准确的信道估计是进行信号相干检测的必要条件,也与自适应链路和多天线传输等关键技术的实现密切相关。因此本论文重点研究OFDM系统的信道估计技术,包括导频辅助信道估计和盲信道估计两类方法。
针对最小均方误差(Minimum Mean Square Error, MMSE)信道估计存在的先验统计特性未知和运算量大的问题,提出一种自适应的低秩(Low-Rank Adaptive, LRA)信道估计算法。该算法在通信过程中可以自适应地跟踪相关矩阵的主特征空间和系统信噪比变化,不需要信道统计特性的先验知识。通过低秩自适应滤波对MMSE估计低秩建模,避免了繁琐的矩阵求逆运算,使LRA算法复杂度较低。理论分析表明,LRA算法迭代收敛后的性能逼近于统计特性匹配时的MMSE估计。LRA算法还具有很强的适用性,不但可以对一维的频域或者时域MMSE信道估计进行改进,而且可以扩展运用到时频二维的导频辅助信道估计中。通过仿真验证了LRA算法的优异性能:在信道统计特性未知的条件下,LRA算法相比其他算法具有不同程度的性能增益。
以Alamouti编码的两发射天线STC-OFDM系统为平台,提出一种基于子空间分解的多信道盲估计算法。该算法利用STC-OFDM系统的后缀补零(Zero-Padding, ZP)时域保护间隔和虚载波(Virtual Carriers, VC)频域保护间隔引入的结构性冗余进行多信道盲估计,保证了信道的盲可辨识性不受信道零点分布和符号映射方式的影响。可辨识性分析证明了多信道的盲估计结果只存在一个标量的模糊度。通过在系统中插入少量导频构成半盲算法,可以消除盲估计结果的模糊度。借助矩阵特征分析的一阶扰动理论,推导了子空间多信道盲估计的均方误差(Mean Square Error, MSE)性能。仿真验证了子空间多信道盲估计的可辨识性和MSE性能,并且指出半盲信道估计可以在数据传输速率更高的条件下,获得比训练序列信道估计更好的性能。
原有的基于信息符号有限字符集特性的信道盲可辨识充分条件过于严格,在不满足该条件的情况下,信道仍有可能可以被辨识。提出并证明了信道盲可辨识的充分必要条件,该条件拓宽了原有充分条件的适用范围,包含了所有可辨识的情况。在此基础上,又提出一种频域最小距离(Frequency-domain Minimum Distance, FMD)算法。该算法通过对信道频率响应的多相分解和分段搜索,可以辨识出信道频率响应在所有子载波上的相位模糊度。只要系统和信道参数满足信道盲可辨识的充分必要条件,FMD算法均可使用,因此适用范围比现有算法广泛。仿真结果表明FMD算法可以在降低计算复杂度的同时具有更好的信道估计精度。
利用信息符号的有限字符集特性,提出一种基于矩阵开方(computing Roots of Matrices, RM)的盲信道估计算法。该算法避免了常规算法的循环搜索,通过在时域上对一个Toeplitz下三角矩阵开方运算实现信道解卷积,可以得到信道估计的闭合解。其复杂度远远低于常规搜索类的算法,所需乘法次数仅与信道阶数的平方成正比,但信道估计性能却接近于搜索算法的最优性能。基于RM算法中的代价函数,又提出一种自适应矩阵开方(Adaptive computing Roots of Matrices, ARM)盲信道估计算法。该算法通过最陡下降方法将代价函数最小化,可以进一步提高RM算法的信道估计准确性。仿真结果表明在信道阶数较大,搜索类的算法无法处理的情况下,RM算法仍然可以得到较好的信道估计结果,而ARM算法可以有效地消除低信噪比条件下,噪声干扰对RM算法的不利影响。
Orthogonal frequency division multiplexing (OFDM) is a promising candidate for next-generation high-speed mobile multimedia communication systems due to its high spectral efficiency, robustness to multipath-fading channel and simple hardware implementation. In OFDM systems, a real-time and accurate channel estimation is not only crucial for coherent detection, but also indispensable for adaptive link and multiple-input and multiple-output (MIMO) transmission. This thesis focuses on channel estimation for OFDM systems, including pilots symbol assisted methods and blind methods.
A low-rank adaptive (LRA) channel estimator is proposed in order to simultaneously reduce the computation complexity of minimum mean square error (MMSE) channel estimator and minimize its performance loss due to mismatch of the estimator-to-channel statistics. This algorithm adaptively tracks the noise power change and the principal eigenspace of time-average channel correlation matrix, and models the MMSE channel estimator by a low-rank adaptive filter. Therefore, the LRA has much lower computation complexity than the MMSE. Theoretical analysis indicates that without prior information of channel statistics, the LRA converges to the solution of statistics-matched MMSE estimation asymptotically. Moreover, LRA not only improves the one-dimensional (1-D) channel estimation, but also can be generalized to 2-D pilot-symbols assisted channel estimation. Simulation results show the effectiveness of LRA, as well as its improvement over other techniques when channel statistics information is unknown.
A subspace based blind channel estimation algorithm is proposed for an Alamouti coded STC-OFDM system with 2 transmit antennas. This algorithm makes use of the redundancy introduced by zero-padding and virtual carriers,therefore channel identifiability is guaranteed regardless of channel zero locations and underlying symbol constellations. Identifiability analysis shows that multiple channels can be identified simultaneously up to one scalar ambiguity. With a small quantity of pilots, a semi-blind estimator can be established to resolve this scalar ambiguity. The subspace blind estimate’s mean square error (MSE) is obtained based on the first order perturbation theory. Simulation results testify the theoretical MSE and indicate that the semi-blind estimator is more accurate and more efficient than the training-based one.
For OFDM systems, the necessary and sufficient conditions are proposed and proved for blind channel identifiability based on the finite alphabet property of information symbols. The existing sufficient condition is too strict while the proposed conditions are looser which can be applied in all cases of channel identifiability. Based on these new conditions, a novel minimum distance algorithm implementing in frequency domain (FMD) is proposed for blind channel estimation. By polyphase decomposition, the channel frequency response sequence is divided into several subsequences, and phase ambiguities of each subcarrier can be resolved by exhaustive searching only L elements in one subsequence, where L is the channel order. The applicability of FMD algorithm is more general than the existing ones because FMD algorithm can be utilized as long as parameters of the channel and the OFDM system satisfy the proposed conditions. Simulation results show that the FMD algorithm is accurate for channel estimation and has a low computation complexity.
A novel blind channel estimator based on computing roots of matrices (RM) is proposed for OFDM systems. This algorithm exploits the finite alphabet property of information symbols and implements channel deconvolution by computing the Jth principle root of a low-triangular Toeplitz matrix. Therefore, RM algorithm has a computation complexity proportional to the square order of channel impulse response, which is much lower than searching algorithms in previous works, and RM is able to function in the case of large channel order that is intractable by searching algorithms. Moreover, an adaptive RM (ARM) algorithm is proposed to adjust RM estimator by steepest descent method. Simulation results indicate that RM algorithm has great accuracy comparable to the optimal exhaustive search, and ARM improves the estimation performance of RM considerably in low SNR condition.
针对最小均方误差(Minimum Mean Square Error, MMSE)信道估计存在的先验统计特性未知和运算量大的问题,提出一种自适应的低秩(Low-Rank Adaptive, LRA)信道估计算法。该算法在通信过程中可以自适应地跟踪相关矩阵的主特征空间和系统信噪比变化,不需要信道统计特性的先验知识。通过低秩自适应滤波对MMSE估计低秩建模,避免了繁琐的矩阵求逆运算,使LRA算法复杂度较低。理论分析表明,LRA算法迭代收敛后的性能逼近于统计特性匹配时的MMSE估计。LRA算法还具有很强的适用性,不但可以对一维的频域或者时域MMSE信道估计进行改进,而且可以扩展运用到时频二维的导频辅助信道估计中。通过仿真验证了LRA算法的优异性能:在信道统计特性未知的条件下,LRA算法相比其他算法具有不同程度的性能增益。
以Alamouti编码的两发射天线STC-OFDM系统为平台,提出一种基于子空间分解的多信道盲估计算法。该算法利用STC-OFDM系统的后缀补零(Zero-Padding, ZP)时域保护间隔和虚载波(Virtual Carriers, VC)频域保护间隔引入的结构性冗余进行多信道盲估计,保证了信道的盲可辨识性不受信道零点分布和符号映射方式的影响。可辨识性分析证明了多信道的盲估计结果只存在一个标量的模糊度。通过在系统中插入少量导频构成半盲算法,可以消除盲估计结果的模糊度。借助矩阵特征分析的一阶扰动理论,推导了子空间多信道盲估计的均方误差(Mean Square Error, MSE)性能。仿真验证了子空间多信道盲估计的可辨识性和MSE性能,并且指出半盲信道估计可以在数据传输速率更高的条件下,获得比训练序列信道估计更好的性能。
原有的基于信息符号有限字符集特性的信道盲可辨识充分条件过于严格,在不满足该条件的情况下,信道仍有可能可以被辨识。提出并证明了信道盲可辨识的充分必要条件,该条件拓宽了原有充分条件的适用范围,包含了所有可辨识的情况。在此基础上,又提出一种频域最小距离(Frequency-domain Minimum Distance, FMD)算法。该算法通过对信道频率响应的多相分解和分段搜索,可以辨识出信道频率响应在所有子载波上的相位模糊度。只要系统和信道参数满足信道盲可辨识的充分必要条件,FMD算法均可使用,因此适用范围比现有算法广泛。仿真结果表明FMD算法可以在降低计算复杂度的同时具有更好的信道估计精度。
利用信息符号的有限字符集特性,提出一种基于矩阵开方(computing Roots of Matrices, RM)的盲信道估计算法。该算法避免了常规算法的循环搜索,通过在时域上对一个Toeplitz下三角矩阵开方运算实现信道解卷积,可以得到信道估计的闭合解。其复杂度远远低于常规搜索类的算法,所需乘法次数仅与信道阶数的平方成正比,但信道估计性能却接近于搜索算法的最优性能。基于RM算法中的代价函数,又提出一种自适应矩阵开方(Adaptive computing Roots of Matrices, ARM)盲信道估计算法。该算法通过最陡下降方法将代价函数最小化,可以进一步提高RM算法的信道估计准确性。仿真结果表明在信道阶数较大,搜索类的算法无法处理的情况下,RM算法仍然可以得到较好的信道估计结果,而ARM算法可以有效地消除低信噪比条件下,噪声干扰对RM算法的不利影响。
Orthogonal frequency division multiplexing (OFDM) is a promising candidate for next-generation high-speed mobile multimedia communication systems due to its high spectral efficiency, robustness to multipath-fading channel and simple hardware implementation. In OFDM systems, a real-time and accurate channel estimation is not only crucial for coherent detection, but also indispensable for adaptive link and multiple-input and multiple-output (MIMO) transmission. This thesis focuses on channel estimation for OFDM systems, including pilots symbol assisted methods and blind methods.
A low-rank adaptive (LRA) channel estimator is proposed in order to simultaneously reduce the computation complexity of minimum mean square error (MMSE) channel estimator and minimize its performance loss due to mismatch of the estimator-to-channel statistics. This algorithm adaptively tracks the noise power change and the principal eigenspace of time-average channel correlation matrix, and models the MMSE channel estimator by a low-rank adaptive filter. Therefore, the LRA has much lower computation complexity than the MMSE. Theoretical analysis indicates that without prior information of channel statistics, the LRA converges to the solution of statistics-matched MMSE estimation asymptotically. Moreover, LRA not only improves the one-dimensional (1-D) channel estimation, but also can be generalized to 2-D pilot-symbols assisted channel estimation. Simulation results show the effectiveness of LRA, as well as its improvement over other techniques when channel statistics information is unknown.
A subspace based blind channel estimation algorithm is proposed for an Alamouti coded STC-OFDM system with 2 transmit antennas. This algorithm makes use of the redundancy introduced by zero-padding and virtual carriers,therefore channel identifiability is guaranteed regardless of channel zero locations and underlying symbol constellations. Identifiability analysis shows that multiple channels can be identified simultaneously up to one scalar ambiguity. With a small quantity of pilots, a semi-blind estimator can be established to resolve this scalar ambiguity. The subspace blind estimate’s mean square error (MSE) is obtained based on the first order perturbation theory. Simulation results testify the theoretical MSE and indicate that the semi-blind estimator is more accurate and more efficient than the training-based one.
For OFDM systems, the necessary and sufficient conditions are proposed and proved for blind channel identifiability based on the finite alphabet property of information symbols. The existing sufficient condition is too strict while the proposed conditions are looser which can be applied in all cases of channel identifiability. Based on these new conditions, a novel minimum distance algorithm implementing in frequency domain (FMD) is proposed for blind channel estimation. By polyphase decomposition, the channel frequency response sequence is divided into several subsequences, and phase ambiguities of each subcarrier can be resolved by exhaustive searching only L elements in one subsequence, where L is the channel order. The applicability of FMD algorithm is more general than the existing ones because FMD algorithm can be utilized as long as parameters of the channel and the OFDM system satisfy the proposed conditions. Simulation results show that the FMD algorithm is accurate for channel estimation and has a low computation complexity.
A novel blind channel estimator based on computing roots of matrices (RM) is proposed for OFDM systems. This algorithm exploits the finite alphabet property of information symbols and implements channel deconvolution by computing the Jth principle root of a low-triangular Toeplitz matrix. Therefore, RM algorithm has a computation complexity proportional to the square order of channel impulse response, which is much lower than searching algorithms in previous works, and RM is able to function in the case of large channel order that is intractable by searching algorithms. Moreover, an adaptive RM (ARM) algorithm is proposed to adjust RM estimator by steepest descent method. Simulation results indicate that RM algorithm has great accuracy comparable to the optimal exhaustive search, and ARM improves the estimation performance of RM considerably in low SNR condition.
引文
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