Jung-Lang Yu

Generalized eigenspace-based beamformers

The generalized eigenspace-based beamformer (GEIB) is presented here, which utilizes the eigenstructure of the correlation matrix to enhance the performance of the linearly constrained minimum variance beamformer (LCMVB). The weight vector of the GEIB is found by projecting the LCMVB weight vector onto a vector subspace constructed from the eigenstructure of the correlation matrix. The GEIB and the LCMVB have the same responses to the desired signal and the interferers. However, the weight vector of the GEIB has a smaller norm and generates a lower output noise power. An additional advantage of the GEIB is that the linear constraints can be treated flexibly, i.e. each linear constraint can be chosen to be preserved or not preserved. The cost of preserving a linear constraint is to get more output noise power. In addition to developing the GEIB, we discuss the effects of imposing linear constraints on the output noise powers of the GEIB and the LCMVB. Computer simulations are also presented that demonstrate the merits of the GEIB.

MIMO Capon Receiver and Channel Estimation for Space-Time Coded CDMA Systems

In this paper the code-division multiple-access (CDMA) multiple-input multiple-output (MIMO) systems with space-time block code (STBC) are investigated. Both the forward-backward averaging technique and the eigenspace: technique are proposed to enhance the system performance. Moreover, we present a subspace-based channel estimator which utilizes the space-time coding property to improve the performance of channel estimator. Then the performance analysis using the first order perturbation theory is derived. Computer simulations are given to demonstrate the effectiveness of the channel estimation and receiver design for the STBC-based CDMA systems

MC-CDMA MIMO systems with quasi-orthogonal space–time block codes: Channel estimation and multiuser detection

This paper investigates blind channel estimation and multiuser detection for quasi-synchronous multi-carrier code-division multiple-access (MC-CDMA) multiple-input multiple-output (MIMO) systems with quasi-orthogonal space–time block codes (QO-STBC). Subspace-based blind channel estimation is proposed by considering a QO-STBC scheme that involves four transmit antennas and multiple receive antennas. Based on the first-order perturbation theory, the mean square error of the channel estimation is derived. With the estimated channel coefficients, we employ minimum output energy and eigenspace receivers for symbol detection. Using the QO-STBC coding property, the weight analyses are performed to reduce the computational complexity of the system. In addition, the forward–backward averaging technique is presented to enhance the performance of multiuser detection. Numerical simulations are given to demonstrate the superiority of the proposed channel estimation methods and symbol detection techniques. Copyright

Blind and semi-blind channel estimation with fast convergence for MIMO-OFDM systems

In this paper, the blind subspace channel estimation using the block matrix scheme is proposed for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the Toeplitz structure, the block matrix scheme collects a group of the received OFDM symbols into a vector, and then partitions it into a set of equivalent symbols. The number of equivalent symbols is about N times of OFDM symbols, where N is the size of FFT operation. With those equivalent symbols, the proposed blind subspace channel estimation can converge within a small amount of OFDM symbols. The identifiability of the proposed channel estimation is examined that the channel matrix is determined up to an ambiguity matrix. Besides, the semi-blind channel estimation is also investigated by combining few pilot sequences with the subspace method. Simulation results show that the proposed channel estimators perform very well even in a time-varying scenario. Especially the semi-blind methods achieve almost the same BERs as those by true channels.

Multiuser receivers for MC-CDMA MIMO systems with space-time block codes

In this paper, we examine the multiuser detection for quasi-synchronous multi-carrier code-division multiple-access (MC-CDMA) multiple-input multiple-output (MIMO) systems with space-time block codes (STBC). Considering Alamoutis STBC scheme which involves two transmit and multiple receive antennas, the Hermitian persymmetric-like property of the received signal is derived first. The forward-backward averaging technique and weighting analysis are then proposed for the blind constrained minimum output energy (CMOE), subspace-based and diagonal loading (DL)-based CMOE receivers. The forward-backward averaging technique, which uses twice the equivalent STBC blocks to design the multiuser receivers, reduces the bit-error-rate (BER) tremendously for the compared receivers. Besides, the weighting analyses are performed in which half of the computational cost is saved in the calculation of weight vectors. Finally, numerical simulations are given to evaluate and demonstrate the superiority of the multiuser receivers with forward-backward averaging technique and the correctness of weighting analyses.

Transformation-based adaptive array beamforming

Abstract Recently, the generalized eigenspace-based beamformer (GEIB) has been proposed to combat the pointing errors and to enhance the convergence speed. The weight vector of the GEIB is generated by projecting the weight vector of the linearly constrained minimum variance beamformer (LCMVB) onto a modified signal subspace. Unfortunately, numerical instability and high computational complexity have prohibited the GEIB from practical applications. In the paper, we propose the transformation-based adaptive array beamforming to overcome those problems. With the introduction of the transformation matrix, we first present an equivalent structure of the LCMVB. Based on the proposed LCMVB structure, the transformation-based GEIB is further developed without computing the modified signal subspace. With the removing of the computation of the modified signal subspace, the transformation-based GEIB becomes numerically stable and computationally efficient. Computer simulations are also given to demonstrate the correctness and usefulness of the transformation-based adaptive array beamforming.

A novel subspace tracking using correlation-based projection approximation

Abstract In this paper, we propose a novel subspace estimation technique, which is called correlation-based projection approximation subspace tracking (COPAST). The COPAST utilizes the projection approximation approach onto the correlation matrix to develop the subspace tracking algorithm. With the projection approximation, the RLS-based COPAST and the sequential-based COPAST algorithms are presented. The RLS-based COPAST algorithm has the better performance but the higher computational complexity than the recently developed PAST method. On the other hand, the sequential-based COPAST has reduced the computational complexity to nearly that of the PAST. Besides, the sequential-based COPAST has faster initial convergence speed than the PAST, while both nearly converge to the same value.

A Novel Subspace Channel Estimation with Fast Convergence for ZP-OFDM Systems

Novel subspace channel estimation with fast convergence is proposed for single-input multiple-output (SIMO) zero-padded orthogonal frequency-division multiplexing (ZP-OFDM) systems. Stacking all the received OFDM symbols turns the channel matrix to be block Toeplitz. The block matrix scheme is then presented so a group of sub-vectors is formed from the stacked OFDM signal and the number of samples is increased. With the sub-vector samples, the perturbations of the sample correlation matrix and the noise subspace are reduced considerably and therefore, the channel estimation error is lowered. Computer simulations demonstrate that the proposed channel estimation is effective and robust compared with existing methods.

Blind estimation of finite alphabet digital signals using eigenspace-based beamforming techniques

The eigenspace-based decoupled weighted iterative least-square with projection (DWILSP) is presented here, which utilizes the eigenstructure of the correlation matrix to enhance the performance of the DWILSP algorithm. In the DWILSP, the signal estimate is interpreted as the minimum variance distortionless response (MVDR) beamforming problem. However, the MVDR beamformer is sensitive to the finite samples and steering vector errors, which cause the performance degradation on the estimate of signals. We then use the eigenspace-based beamformer instead of the MVDR beamformer to alleviate the performance degradation, where the output signal-to-interference-plus-noise ratio is increased during the estimate of signal of interest. Further, to reduce the computational complexity of the developed algorithm, an efficient implementation approach is proposed. It is shown that the projection operations in the eigenspace-based beamformer can be performed in the whitening domain. Using the fact, the computational complexity of the eigenspace-based DWILSP is reduced considerably and even lower than that of the DWILSP. Computer simulations are given to demonstrate that the eigenspace-based DWILSP outperforms the other iterative least-square approaches.

Selective-tracing waveform relaxation algorithm for incremental circuit simulation

This paper discusses the large-scale circuit simulation and incremental circuit simulation, both which are based on selective-tracing waveform relaxation (STWR) algorithm. Utilizing the concept of exactness in simulating subcircuits, STWR usually spends fewer subcircuit calculations than traditional algorithms, such as WR and ITA. But STWR suffers from robustness problems. Techniques are proposed to reinforce characteristics of STWR. This paper also describes the incremental circuit simulation that is based on STWR. This new method manages the influence of design changes more exactly than previous works, and hence exhibits better efficiency. We have implemented all proposed methods and performed experiments. Simulation results justify better characteristics of proposed methods.