The performance of minimum output energy(MOE) detector will significantly degrade when the weight vector is affected by the noise.In order to resolve this problem,a noise suppression based linear conjugate MOE detector is designed. The constrained least mean square(LMS) algorithm is applied to the new MOE detector and a blind noise suppression based linear conjugate MOE multi-user detection algorithm based on constrained LMS is proposed.The proposed algorithm eliminates the noise in the weight vector and utilizes the complex conjugate of the received vector.Thus the output signal-tointerference -plus-noise-ratio(SINR) and bit error rate(BER) performance is improved.Simulation results demonstrate that the proposed algorithm is of better performance.

From the foundation of the investigation on the least mean square(LMS) blind multiuser detection,an adaptive semi-blind multiuser detection algorithm based on the subspace constrained LMS is proposed for multicarrier CDMA(MC-CDMA) uplink.A semi-blind multiuser detector based on the minimum output energy(MOE) principle is designed,and a subspace constrained LMS algorithm is proposed to obtain the MOE weight vector adaptively.To reduce the computational complexity,an improved projection approximation subspace tracking with deflation(PASTd) algorithm is adopted for adaptive signal subspace estimation.This proposed algorithm suppresses the interferers by exploiting the spreading sequences of all known users and mitigates the noise by constraining the weight vector in the signal subspace.As a result,the performance of the output signal-to-interference-plus-noise-ratio(SINR) and the bit error rate(BER) is improved.Simulation results demonstrate the effectiveness of the proposed algorithm.

From the foundation of the investigation on the RLS-MOE blind muhiuser detection,a semi-hlind muhiuser detection based on the subspace constrained RLS algorithm is proposed for MC-CDMA uplink.A seim-blind muhiuser detection hased on the MOE principle is designed by exploiting the spreading sequences of all known users.We combine the RLS algorithm with the subspace ap- proach and propose a subspace constrained RLS algorithm to obtain the MOE weight vector adaptively.This proposed detector suppresses the interferers within the cell and mitigates the noise.As a result,the performance of the system is improved.A modified PASTd algo- titian is presented for adaptive subspace estimation,which reduces the computational complexity.Simulation results show that the pro- posed algorithm can offer better performance in SINR and BER than the RLS-MOE blind muhiuser detection.

该文提出MC-CDMA系统下一种基于递归最小二乘(Recursive Least-Squares, RLS)的最小输出能量(Minimum Output Energy, MOE)噪声抑制线性共轭多用户检测算法.该算法定义了一种新的基于MOE准则的代价函数,同时将噪声子空间作为MOE代价函数的约束条件,设计了一种噪声抑制的线性共轭检测器,并采用RLS算法自适应得到权向量.所提算法将权向量和噪声子空间正交,消除了权向量中的噪声分量,并且利用了伪自相关矩阵的信息,从而提高了系统的性能.仿真结果证明了本文算法的有效性和优越性.

The least squares constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. To further enhance the performance, we project the weight vector obtained by the proposed linearly constrained LSCMA algorithm onto the signal subspace and propose a subspace-based linearly constrained LSCMA multiuser detection algorithm. The proposed algorithm ensures the algorithm convergence to the desired user and suppresses the noise subspace in the weight vector. Thus the performance of the system is improved. Moreover, to reduce the computational complexity, an improved projection approximation subspace tracking with deflation (PASTd) algorithm is proposed for adaptive signal subspace estimation. The simulation results demonstrate that the proposed algorithm achieves better output signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) performance than the traditional LSCMA algorithm, linearly constrained LSCMA algorithm and subspace-based MMSE algorithm.

In this paper, an adaptive semi-blind multiuser detection (MUD) algorithm based on improved projecting approximation subspace tracking with deflation (PASTd) subspace tracking is proposed for multicarrier code division multiple access (MC-CDMA) uplink where the base station receiver has the knowledge of the spreading sequences of all the users within the cell, but not that of the users from other cells. It is known that the PASTd algorithm has the drawback of slow convergence rate. Based on this, we develop an improved PASTd algorithm and apply it to the adaptive linear hybrid semi-blind multiuser detection. The improved PASTd algorithm guarantees the orthonormality between the estimated eigenvectors such that a fast convergence rate can be achieved. Simulation results show the proposed semi-blind MUD has a fast convergence rate and provides the similar output signal-to-interference-plus-noise-ratio (SINR) and bit error rate (BER) as the Singular Value Decomposition (SVD) semi-blind MUD.

A linear conjugate semi-blind multi-user detector based on the subspace method was designed by introducing the idea of the linear conjugate to the subspace-based semi-blind multi-user detection,which makes improvements in terms of signal-to-interference-ratio(SIR) and bit error rate(BER).The PASTd algorithm with less computation complexity is widely used in the multi-user detection,however,and it can not guarantee the orthogonality of eigenvectors which induces the slow convergence rate.To overcome this problem,an improved PASTd subspace tracking algorithm was proposed,and was applied to the linear conjugate semi-blind multi-user detector for adaptive subspace estimation,which enhances the convergence rate and reduces calculation of the algorithm.Simulation results demonstrate the effectiveness and feasibility of the proposed algorithm.

基于MOE准则,充分利用小区内所有用户的扩频码给出一种MOE半盲检测器,结合修正的PASTd算法和LMS算法自适应得到MOE半盲检测器的权向量,提出MC-CDMA系统下一种基于LMS的自适应半盲多用户检测算法.该算法利用已知的信息使系统的输出信干比和误码率优于LMS盲多用户检测,同时避免LMS算法中的特征值分解问题,显著降低计算量.仿真实验验证了该算法的有效性和优越性.

In this paper, a new semi-blind multiuser detection algorithm based on subspace constrained least mean-squared (LMS) is proposed for multicarrier code division multiple access (MC-CDMA) uplink system. This proposed detector makes use of the spreading sequences of all known users to impose multiple constraints on the minimum output energy (MOE) multiuser detection so that the interference within the cell can be suppressed. A subspace constrained LMS algorithm is proposed to obtain the MOE weight vector adaptively. To reduce the computational complexity, an improved projecting approximation subspace tracking with deflation (PASTd) algorithm is developed for adaptive signal subspace estimation. Computer simulations show that the performance of the proposed semi-blind multiuser detection is significantly superior to that of the LMS-based blind multiuser detection

In this paper, a new semi-blind multiuser detection based on a hybrid of Chebyshev approximation (CA) and subspace estimation is proposed for multicarrier code division multiple access (MC-CDMA) uplink It is shown that the detector can be expressed as an anchored signal in the signal subspace and the coefficients can be estimated by CA algorithm. The spreading sequences of all known users are used to impose the linear vector constraints on the multiuser detector such that the interferers within the cell are suppressed effectively. To reduce the computational complexity, an improved projecting approximation subspace tracking with deflation (PASTd) algorithm is presented for adaptive subspace estimation. Simulation results demonstrate that the proposed semi-blind multiuser detection offers substantial performance gains over the blind multiuser detection in the MC-CDMA uplink environment.

In this paper, a new semi-blind adaptive multiuser detection based on a subspace constrained Chebyshev approximation (CA) is proposed for multicarrier code division multiple access (MC-CDMA) uplink system. This proposed detector fully utilizes all known information to suppress the interferers within the cell, while mitigating the inter-cell interferers based on the minimum output energy (MOE) principle. A subspace constrained CA algorithm is designed to obtain the MOE weight vector adaptively, which significantly outperforms the CA algorithm. To reduce the computational complexity, an improved projecting approximation subspace tracking with deflation (PASTd) algorithm is developed for adaptive signal subspace estimation. Computer simulations show that the performance of the proposed semi-blind multiuser detection is much better than that of the blind multiuser detection based on Chebyshev approximation

In this paper, a new subspace semi-blind adaptive multiuser detection based on a hybrid of Kalman filter and subspace estimation is proposed for MC-CDMA uplink. It is shown that the detector can be expressed as an anchored signal in the signal subspace and coefficients can be estimated by the Kalman filter. The spreading sequences of all known users are exploited to develop a new efficient semi-blind adaptive multiuser detection requiring fewer adaptive coefficients. To reduce the computational complexity, an improved projecting approximation subspace tracking with deflation (PASTd) algorithm is developed for adaptive signal subspace estimation. Simulation results demonstrate that the proposed semi-blind multiuser detection offers substantial performance gains over the blind multiuser detection method in the MC-CDMA uplink environment