A Polynomial Matrix SVD Approach for Time Domain Broadband Beamforming in MIMO-OFDM Systems

Zamiri-Jafarian, H.; Rajabzadeh, Morteza

doi:10.1109/vetecs.2008.175

Cited by 18 publications

(16 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, in the literature we can find a several solutions related to SVD factorization in form, e.g., P-SVD (Polynomial-SVD) [20][21][22]. In this paper a new approach to perfect signal recovery is presented, which is based on so-called polynomial matrix S-inverse.…”

Section: Polynomial Matrix S-inversementioning

confidence: 99%

New approach to wireless data communication in a propagation environment

Hunek

Majewski

2017

E3S Web Conf.

View full text Add to dashboard Cite

Abstract. This paper presents a new idea of perfect signal reconstruction in multivariable wireless communications systems including a different number of transmitting and receiving antennas. The proposed approach is based on the polynomial matrix S-inverse associated with Smith factorization. Crucially, the above mentioned inverse implements the so-called degrees of freedom. It has been confirmed by simulation study that the degrees of freedom allow to minimalize the negative impact of the propagation environment in terms of increasing the robustness of whole signal reconstruction process. Now, the parasitic drawbacks in form of dynamic ISI and ICI effects can be eliminated in framework described by polynomial calculus. Therefore, the new method guarantees not only reducing the financial impact but, more importantly, provides potentially the lower consumption energy systems than other classical ones. In order to show the potential of new approach, the simulation studies were performed by author's simulator based on wellknown OFDM technique.

show abstract

Section: Polynomial Matrix S-inversementioning

confidence: 99%

New approach to wireless data communication in a propagation environment

Hunek

Majewski

2017

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Moreover, in many applications, specially those which are related to MIMO precoding, we can relax constraints of the problem by letting singular values to be complex (see applications of polynomial SVD in [4,18])…”

Section: Problem Formulationmentioning

confidence: 99%

“…Nowadays, polynomial matrices have a wide spectrum of applications in MIMO communications [2][3][4][5][6], source separation [7], and broadband array processing [8]. They also have a dominant role in development of multirate filterbanks [9].…”

Section: Introductionmentioning

confidence: 99%

A DFT-based approximate eigenvalue and singular value decomposition of polynomial matrices

Tohidian

Amindavar

Azimi

2013

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

In this article, we address the problem of singular value decomposition of polynomial matrices and eigenvalue decomposition of para-Hermitian matrices. Discrete Fourier transform enables us to propose a new algorithm based on uniform sampling of polynomial matrices in frequency domain. This formulation of polynomial matrix decomposition allows for controlling spectral properties of the decomposition. We set up a nonlinear quadratic minimization for phase alignment of decomposition at each frequency sample, which leads to a compact order approximation of decomposed matrices. Compact order approximation of decomposed matrices makes it suitable in filterbank and multiple-input multiple-output (MIMO) precoding applications or any application dealing with realization of polynomial matrices as transfer function of MIMO systems. Numerical examples demonstrate the versatility of the proposed algorithm provided by relaxation of paraunitary constraint, and its configurability to select different properties.

show abstract

“…The columns are visited according to the ordering , which is important for convergence of the algorithm. Following column-steps of the algorithm, the transformation is of the form (16) where is formed from a series of EPGRs interspersed with paraunitary inverse delay matrices and is therefore paraunitary by construction. Note that over each iteration of each step of the algorithm, the order of the matrices and will increase due to the application of both the EPGR and inverse delay step.…”

Section: B the Algorithmmentioning

confidence: 99%