Interference Alignment Based on Subspace Tracking in MIMO Cognitive Networks with Multiple Primary Users

The interference alignment （IA） algorithm based on FDPM subspace tracking （FDPM-ST IA） is proposed for MIMO cognitive network （CRN） with multiple primary users in this paper. The feasibility conditions of FDPM-ST IA is also got. Futherly, IA scheme of secondary network and IA scheme of primary network are given respectively without assuming a priori knowledge of interference covariance matrices. Moreover, the paper analyses the computational complexity of FDPM-ST IA. Simulation results and theoretical calculations show that the proposed algorithm can achieve higher sum rate with lower computational complexity.

Blind Estimation of Long Code DSSS Signal Based On Subspace Tracking

Aiming at solving the blind estimation problem of dispreading spectrum sequence under low SNR, a spread-spectrum estimation algorithm based subspace tracking is studied in this paper. This method avoids the direct eigen decomposition, using the sliding window technique to obtain the code synchronization, then use segmentation subspace tracking method estimate spreading sequence and splice in a certain order to achieve pseudo-code blind estimation. The results show that the algorithm can complete the accurate estimation of PN code sequence in low SNR conditions, reduce the amount of data storage and be easy hardware implementation

AN IMPROVED SUBSPACE TRACKING ALGORITHM FOR BLIND ADAPTIVE MULTIUSER DETECTION

As the Projection Approximation Subspace Tracking with deflation(PASTd) algorithm is sensitive to impulsive noise, an improved subspace tracking algorithm is proposed and applied to blind adaptive multi-user detection. Simulation results show that the improved PASTd algorithm not only remains the properties of the conventional PASTdalgorithm, but also has good Bit Error Rate(BER) performance in impulsive noise environment, thus it can effectively improve the system performance.

INVERSE QR ITERATIVE METHOD FOR SUBSPACE TRACKING

The Bi-LS method based on QR decomposition provides a convenient framework for de-veloping efficient subspace tracking algorithms.To overcome the shortcoming of the backsubstitution step and improve the parallel architecture of the Bi-LS algorithms,a Bi-LS subspace tracking algorithm based on Inverse QR(IQR) decomposition is developed.The proposed IQR iterative algorithm for subspace tracking is well suited for the parallel implementation in the systolic array.Simulation results are presented to illustrate the effectiveness of the proposed IQR subspace tracking algorithm.

Channel estimation for MIMO-OFDM systems in mobile wireless channels

A channel estimation method is proposed for nmltiple-input multiple-output orthogonal frequency division muhiplexing （MIMO-OFDM） systems in time-varying fading channels. In this method, a decision-directed space-alternating generalized expectation-maximization （SAGE） algorithm is introduced to the tracking of time-varying fading. In order to improve the estimation performance of the SAGE algorithm, a low rank approximation method is presented by using the signal subspace of the channel frequency autocorrelation matrix. The study reveals that this method can be incorporated into the SAGE algorithm. Furthermore, a modified fast sub- space tracking algorithm is given to adaptively estimate the signal subspace by utilizing training OFDM blocks sent at regular interval. Simulation results demonstrate the considerable benefits of the proposed channel estimation method.

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output （SIMO） FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares （Bi-LS） subspace tracking method. The new algorithm requires only a computational complexity of O（md2） at each iteration, or even as low as O（md） if only equalization is necessary, where m is the dimension of the received data vector （or the row rank of channel matrix） and d is the dimension of the signal subspace （or the column rank of channel matrix）. To overcome the shortcoming of the back substitution, an inverse QR iteration algorithm for subspace tracking and channel equalization is also developed. The inverse QR iteration algorithm is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed algorithms for the channel identification and equalization.