BIMA: blind iterative MIMO algorithm (original) (raw)

A sequential subspace method for blind identification of general FIR MIMO channels

IEEE Transactions on Signal Processing, 2005

This correspondence addresses the problem of blindly identifying multiple input multiple output (MIMO) finite impulse response (FIR) channels without the conventional assumption of identical column degrees. A subspace-based algorithm is developed that identifies the channel's columns sequentially from the lowest degree columns to the highest degree ones. Compared with the previous generalized subspace method by Gorokhov and Loubaton, the new method is simpler and more accurate.

Efficient Semi-Blind Subspace Channel Estimation for MIMO-OFDM System

2018 26th European Signal Processing Conference (EUSIPCO)

This paper deals with channel estimation for Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) wireless communications systems. Herein, we propose a semi-blind (SB) subspace channel estimation technique for which an identifiability result is first established for the subspace based criterion. Our algorithm adopts the MIMO-OFDM system model without cyclic prefix and takes advantage of the circulant property of the channel matrix to achieve lower computational complexity and to accelerate the algorithm's convergence by generating a group of sub vectors from each received OFDM symbol. Then, through simulations, we show that the proposed method leads to a significant performance gain as compared to the existing SB subspace methods as well as to the classical last-squares channel estimator.

Blind MIMO Eigenmode Transmission Based on the Algebraic Power Method

IEEE Transactions on Signal Processing, 2004

Identification of the channel matrix is of main concern in wireless multiple input multiple output (MIMO) systems. To maximize the SNR, the best way to utilize a MIMO system is to communicate on the top singular vectors of the channel matrix. Here, we present a new approach for direct blind identification of the main independent singular modes, without estimating the channel matrix itself. The right and left singular vectors with maximum corresponding singular values are determined using payload data and are continuously updated while at the same time being used for communication. The feasibility of the approach is demonstrated by simulating the performance over a noisy, fading time-varying channel. Mathematically, the technique is related to the iterative numerical Power method for finding eigenvalues of a matrix as well as the "time reversal mirror" technique developed within acoustics.

A blind MIMO channel estimation method robust to order overestimation

Signal Processing, 2004

A new second-order statistics (SOS)-based blind algorithm for MIMO channel estimation is proposed. It can give estimations of all channel impulse responses subject to a scalar matrix ambiguity which is intrinsic to all SOS-based blind methods. As a generalization of the blind identiÿcation algorithm for SIMO system in Gazzah et al. (IEEE Trans. Signal Process. 50 , this method is truly robust to channel order overestimation, i.e., it can accurately estimate the channels when only upper bounds for all MIMO channel orders are known. ?

Deterministic Blind Subspace MIMO Equalization

EURASIP Journal on Advances in Signal Processing, 2002

A subspace based approach for the blind multiple signal separation and recovery for MIMO systems is proposed in this paper. Instead of using the statistics of the received signal, the proposed algorithm exploits the received signal structure and the finite alphabet property of the desired signals. The finite alphabet property is used to remove the unknown unitary matrix that is associated with most of the statistics-based MIMO system identification algorithms. The proposed algorithm also incorporates an error-correcting procedure; therefore, it has more accuracy than the existing algorithms. Computer simulation results demonstrate that the algorithm can detect the signals and estimate channel parameters accurately with very few symbols, even under high noise and bad channel conditions.

Blind MIMO communication based on subspace estimation

2004

A new method is proposed for blindly estimating the top singular modes in a reciprocal MIMO Output) channel, while at the same time using these modes for multi-stream communication without the need for training data. The uplink and downlink parties obtain the relevant singular modes from the received data blocks as the eigenvectors/eigenvalues of the spatial empirical correlation matrices. The only requirement is that the separate data streams are statistically uncorrelated. The approach relies on a key and simple "need to know" observation about MIMO transmission :

Matrix outer-product decomposition method for blind multiple channel identification

1997

Blind channel identification and equalization have recently attracted a great deal of attention due to their potential application in mobile communications and digital TV systems. In this paper, we present a new algorithm that utilizes second-order statistics for multichannel parameter estimation. The algorithm is simple and relies on an outer-product decomposition. Its implementation requires no adjustment for single-or multiple-user systems. This new algorithm can be viewed as a generalization of a recently proposed linear prediction algorithm. It is capable of generating more accurate channel estimates and is more robust to overmodeling errors in channel order estimate. The superior performance of this new algorithm is demonstrated through simulation examples.

A Tensor-Based Subspace Method for Blind Estimation of MIMO Channels

In this paper, we introduce a tensor-based subspace method for solving the blind channel estimation problem in a multiple-input multiple-output (MIMO) system. The current subspace methods of blind channel estimation require stacking the multidimensional measurement data into one highly structured vector and estimate the signal subspace via a singular value decomposition (SVD) of the correlation matrix of the measurement data. In contrast to this, we propose a 3-way measurement tensor to exploit the structure inherent in the measurement data and introduce a Higher-Order SVD (HOSVD) to obtain the signal subspace. This tensor-based subspace estimate is an improved estimate of the signal subspace, thereby leading to an improved estimate of the system channels. Numerical simulations demonstrate that the proposed method outperforms the current subspace based blind channel estimation methods in terms of the channel estimation accuracy. Furthermore, we show that the accuracy of the estimations is significantly improved by employing overlapping observed data windows at the receiver.

Blind Channel Estimation for MIMO OFDM Systems via Nonredundant Linear Precoding

IEEE Transactions on Signal Processing, 2007

Based on the assumption that the transmitted symbols are independent and identically distributed (i.i.d.), we develop a simple subspace-based blind channel estimation technique for orthogonal frequency-division multiplexing (OFDM) systems by utilizing nonredundant linear block precoding. A novel contribution is that the proposed method can be applied for scenarios, where the number of receive antennas is less than the number of transmit antennas, e.g., multiple-input single-output (MISO) transmissions, in which case the traditional subspace-based methods could not be applied. Further consideration that can eliminate the multidimensional ambiguity in channel estimation under multiple transmitter scenarios is also proposed. The numerical results clearly show the effectiveness of our proposed algorithm.

A Fast Adaptive Method for Subspace Based Blind Channel Estimation

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006

In this paper, a new fast adaptive blind channel estimation method is proposed using the subspace information from the correlation matrix. The algorithm is fully adaptive in the sense that both the subspace information and the optimization which leads to the channel estimation are computed adaptively. It is based on the recently proposed YAST subspace tracker which has been shown to outperform other methods both in terms of speed of convergence and computational complexity. A discussion on the convergence properties of the proposed algorithm is presented. We also propose a hybrid method which makes use of the YAST subspace tracker for initial fast convergence and the subspace information is then updated using the numerically stable OPAST subspace tracker.