MIMO系统的信号检测及迭代接收技术研究
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摘要
在MIMO(Multi-Input Multi-Output)和MIMO-OFDM(MIMO-Orthogonal Frequency Division Multiplex)系统中,往往涉及高复杂度的多维信号检测和联合参数估计问题,为系统实现带来很大困难。迭代参数估计技术可以降低联合参数估计的复杂度,并获得渐近最优的性能。本文以寻求最佳性能与复杂度折中方案为原则,针对V-BLAST(Vertical-Bell Labs lAyered Space-Time)系统的信号检测问题和MIMO-OFDM系统的迭代参数估计问题展开研究,取得以下研究成果:
1.论文针对V-BLAST系统,基于信道矩阵的最小均方误差(MMSE,Minimum Mean Square Error )排序QR分解( MMSE-SQRD , MMSE-Sorted QR Decomposition),分别提出了半径约束的QRD-M算法和半径约束的Stack的树搜索检测算法,并建立了半径的自适应更新策略。与传统的QRD-M和Stack算法相比,所提算法不仅可以改善检测性能,还显著降低了计算复杂度。
2.论文针对编码V-BLAST系统,基于最大似然(ML,Maximum Likelihood)度量提出了宽度优先的低复杂度列表检测(BrF-LCLD)算法,此外基于最大后验概率(MAP,Maximum A Posteriori)度量提出了降低复杂度的列表QRD-M(RC-QRD-M)算法,并为所提算法设计了合适的排序QR分解算法。相对于ML度量下和MAP度量下的列表QRD-M算法,所提算法均以较小的性能损失为代价有效降低了检测的复杂度。此外,所提算法还具有复杂度固定和易于并行实现的优势。
3.论文针对MIMO-OFDM系统,基于变分贝叶斯期望最大化(VBEM,Variational Bayesian Expectation Maximization)算法,首先推导了贝叶斯意义上最优的联合信道估计和信号检测译码的迭代接收机,然后提出了适用于该接收机的时频域联合递推信道估计(SFD-RVBCE)算法,最后通过合理的近似得到了只在频域递推的FD-RVBCE估计算法。理论分析和仿真表明,所提接收机可以接近具有理想信道信息情况下最优接收机的性能。而且,与现有的变分贝叶斯迭代接收机相比,所提接收机具有更好的性能和更低的计算复杂度。
4.论文针对MIMO-OFDM系统,基于信号的分解模型提出了低复杂度的软判决指导递推信道跟踪算法。基于非分解的原始信号模型,推导了所提递推跟踪算法的简化算法。所提算法通过在时域上进行信道预测,在频域上进行递推估计,完全避免了矩阵求逆运算,有效降低了计算复杂度。在快时变多径信道下,与性能较好的Turbo-Kalman和Turbo-RLS算法相比,所提算法的复杂度大大降低,性能与它们几乎相同或相差较小。另外,所提算法具有统一的递推跟踪架构,只需进行较小的改变就可适用不同的应用环境,比较利于硬件实现。
In Multiple-Input Multiple-Output (MIMO) and MIMO-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, receivers usually need to solve the problems of high dimensional signal detection and iterative parameter estimation, which leads to prohibitively high complexity. The iterative parameter estimation technique can obtain asymptotically optimal performance and reduce the complexity of joint parameter estimation. On the purpose of pursuing good tradeoff between performance and complexity, the signal detection in V-BLAST (Vertical-Bell Labs lAyered Space-Time) systems and the iterative parameter estimation in MIMO-OFDM systems are studied respectively. The main contributions of the dissertation are summarized as follows:
1. By exploiting the minimum mean square error based sorted QR decomposition (MMSE-SQRD) of the channel matrix, a sphere constraint QRD-M algorithm and a sphere constraint Stack algorithm were proposed for V-BLAST systems. Accordingly, the strategies of updating sphere radius were developed for the proposed two algorithms. The newly proposed algorithms not only outperform the conventional algorithms, but also dramatically reduce the computational complexity. Moreover, they can achieve almost the same performance as the SE sphere decoding (SE-SD) algorithm while visiting much fewer nodes in the tree-search procedure.
2. Base on the maximum likelihood (ML) metric and the maximum a posteriori (MAP) metric, a breadth-first low complexity list detection (BrF-LCLD) algorithm and a reduced complexity QRD-M (RC-QRD-M) algorithm were presented for coded V-BLAST systems. Furthermore, the original sorted QR decomposition (SQRD) algorithm of the channel matrix was modified to improve performance. Compared with the ML and MAP QRD-M algorithm, the proposed algorithms efficiently reduce the computational complexity at the price of slight performance loss. In addition, they are quite suitable to practical implementation and maintain fixed computational complexity.
3. Using the Variational Bayesian EM (VBEM) algorithm, an asymptotically optimal Bayesian iterative receiver with joint channel estimation and signal detection/decoding was firstly derived for MIMO-OFDM systems. And then, a space-frequency domain (SFD) combined recursive variational Bayesian channel estimation (SFD-RVBCE) algorithm was proposed for the novel VBEM iterative receiver. By making some reasonable approximations, a frequency domain RVBCE (FD-RVBCE) algorithm was finally obtained. Theoretical analysis and simulation results demonstrate that the proposed receiver not only achieves near optimal performance, but also shows better performance and lower computational complexity than the existing VBEM iterative receiver.
4. Based on the decomposed signal model, several soft-decision-directed recursive channel tracking algorithms were developed to reduce complexity. Based on the original signal model, the corresponding simplified algorithms were derived subsequently. The proposed algorithms completely avoid matrix inversion by performing channel prediction on time domain and recursive channel estimation on frequency domain. Theoretical analysis and simulation results show that they can significantly reduce the computational complexity, and approach the performance of the excellent Turbo-Kalman and Turbo-RLS algorithms. In addition, they can be applied to different environment after small modifications due to their common recursive tracking framework, therefore are of benefit to hardware implementation.
1.论文针对V-BLAST系统,基于信道矩阵的最小均方误差(MMSE,Minimum Mean Square Error )排序QR分解( MMSE-SQRD , MMSE-Sorted QR Decomposition),分别提出了半径约束的QRD-M算法和半径约束的Stack的树搜索检测算法,并建立了半径的自适应更新策略。与传统的QRD-M和Stack算法相比,所提算法不仅可以改善检测性能,还显著降低了计算复杂度。
2.论文针对编码V-BLAST系统,基于最大似然(ML,Maximum Likelihood)度量提出了宽度优先的低复杂度列表检测(BrF-LCLD)算法,此外基于最大后验概率(MAP,Maximum A Posteriori)度量提出了降低复杂度的列表QRD-M(RC-QRD-M)算法,并为所提算法设计了合适的排序QR分解算法。相对于ML度量下和MAP度量下的列表QRD-M算法,所提算法均以较小的性能损失为代价有效降低了检测的复杂度。此外,所提算法还具有复杂度固定和易于并行实现的优势。
3.论文针对MIMO-OFDM系统,基于变分贝叶斯期望最大化(VBEM,Variational Bayesian Expectation Maximization)算法,首先推导了贝叶斯意义上最优的联合信道估计和信号检测译码的迭代接收机,然后提出了适用于该接收机的时频域联合递推信道估计(SFD-RVBCE)算法,最后通过合理的近似得到了只在频域递推的FD-RVBCE估计算法。理论分析和仿真表明,所提接收机可以接近具有理想信道信息情况下最优接收机的性能。而且,与现有的变分贝叶斯迭代接收机相比,所提接收机具有更好的性能和更低的计算复杂度。
4.论文针对MIMO-OFDM系统,基于信号的分解模型提出了低复杂度的软判决指导递推信道跟踪算法。基于非分解的原始信号模型,推导了所提递推跟踪算法的简化算法。所提算法通过在时域上进行信道预测,在频域上进行递推估计,完全避免了矩阵求逆运算,有效降低了计算复杂度。在快时变多径信道下,与性能较好的Turbo-Kalman和Turbo-RLS算法相比,所提算法的复杂度大大降低,性能与它们几乎相同或相差较小。另外,所提算法具有统一的递推跟踪架构,只需进行较小的改变就可适用不同的应用环境,比较利于硬件实现。
In Multiple-Input Multiple-Output (MIMO) and MIMO-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, receivers usually need to solve the problems of high dimensional signal detection and iterative parameter estimation, which leads to prohibitively high complexity. The iterative parameter estimation technique can obtain asymptotically optimal performance and reduce the complexity of joint parameter estimation. On the purpose of pursuing good tradeoff between performance and complexity, the signal detection in V-BLAST (Vertical-Bell Labs lAyered Space-Time) systems and the iterative parameter estimation in MIMO-OFDM systems are studied respectively. The main contributions of the dissertation are summarized as follows:
1. By exploiting the minimum mean square error based sorted QR decomposition (MMSE-SQRD) of the channel matrix, a sphere constraint QRD-M algorithm and a sphere constraint Stack algorithm were proposed for V-BLAST systems. Accordingly, the strategies of updating sphere radius were developed for the proposed two algorithms. The newly proposed algorithms not only outperform the conventional algorithms, but also dramatically reduce the computational complexity. Moreover, they can achieve almost the same performance as the SE sphere decoding (SE-SD) algorithm while visiting much fewer nodes in the tree-search procedure.
2. Base on the maximum likelihood (ML) metric and the maximum a posteriori (MAP) metric, a breadth-first low complexity list detection (BrF-LCLD) algorithm and a reduced complexity QRD-M (RC-QRD-M) algorithm were presented for coded V-BLAST systems. Furthermore, the original sorted QR decomposition (SQRD) algorithm of the channel matrix was modified to improve performance. Compared with the ML and MAP QRD-M algorithm, the proposed algorithms efficiently reduce the computational complexity at the price of slight performance loss. In addition, they are quite suitable to practical implementation and maintain fixed computational complexity.
3. Using the Variational Bayesian EM (VBEM) algorithm, an asymptotically optimal Bayesian iterative receiver with joint channel estimation and signal detection/decoding was firstly derived for MIMO-OFDM systems. And then, a space-frequency domain (SFD) combined recursive variational Bayesian channel estimation (SFD-RVBCE) algorithm was proposed for the novel VBEM iterative receiver. By making some reasonable approximations, a frequency domain RVBCE (FD-RVBCE) algorithm was finally obtained. Theoretical analysis and simulation results demonstrate that the proposed receiver not only achieves near optimal performance, but also shows better performance and lower computational complexity than the existing VBEM iterative receiver.
4. Based on the decomposed signal model, several soft-decision-directed recursive channel tracking algorithms were developed to reduce complexity. Based on the original signal model, the corresponding simplified algorithms were derived subsequently. The proposed algorithms completely avoid matrix inversion by performing channel prediction on time domain and recursive channel estimation on frequency domain. Theoretical analysis and simulation results show that they can significantly reduce the computational complexity, and approach the performance of the excellent Turbo-Kalman and Turbo-RLS algorithms. In addition, they can be applied to different environment after small modifications due to their common recursive tracking framework, therefore are of benefit to hardware implementation.
引文
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