A Novel Block Diagonalization Algorithm to Suppress Inter-user Interference in a Multi-user Mimo System

2021 ◽  
pp. 117-126
Author(s):  
Harsha Gurdasani ◽  
A. G. Ananth ◽  
N. Thangadurai
Keyword(s):  
Mimo System ◽  
Block Diagonalization ◽  
User Interference ◽  
Diagonalization Algorithm

An Improved MMSE Pre-Coding Method for the Downlink Multi-User MIMO System

2012 ◽  
Vol 457-458 ◽  
pp. 600-606
Author(s):  
Xian Kun Gao ◽  
Yan Cui ◽  
Ji Lai Ying ◽  
Yong Chang Yu
Keyword(s):  
Numerical Simulations ◽  
Mimo System ◽  
Multiple Antennas ◽  
Poor Performance ◽  
Inversion Method ◽  
Block Diagonalization ◽  
Coding Method ◽  
Diagonalization Method ◽  
Noise Ratio ◽  
Theoretical Analyses

Recently many practical downlink multi-user MIMO linear pre-coding methods have been proposed, such as the channel inversion method and the block diagonalization method (BD). Considering the channel inversion method based on MMSE criterion (MMSE-CI) which is confined to a single receives antenna case, the BD has more advantages in multiple antennas cases, however, it has poor performance at the low and medium SNR regime on account of no consideration on the noise. In this paper, an improved MMSE pre-coding method is proposed with multi receive antennas of each user. Based on MMSE-CI, the cooperation of multiple antennas is adopted to further suppress the residual interference during designing the pre-coding matrix, which could increase the signal-to- interference-plus-noise ratio (SINR) at each user’s receiver. The proposed method obtains a better performance than the MMSE-CI and the BD algorithms, and its effectiveness is validated by both theoretical analyses and numerical simulations.

scholarly journals Bidiagonalization of (k, k + 1)-tridiagonal matrices

2019 ◽  
Vol 7 (1) ◽  
pp. 20-26 ◽  
Author(s):  
S. Takahira ◽  
T. Sogabe ◽  
T.S. Usuda
Keyword(s):  
Tridiagonal Matrix ◽  
Permutation Matrix ◽  
Block Diagonalization ◽  
Tridiagonal Matrices ◽  
The Matrix ◽  
Appl Math ◽  
Diagonalization Algorithm

Abstract In this paper,we present the bidiagonalization of n-by-n (k, k+1)-tridiagonal matriceswhen n < 2k. Moreover,we show that the determinant of an n-by-n (k, k+1)-tridiagonal matrix is the product of the diagonal elements and the eigenvalues of the matrix are the diagonal elements. This paper is related to the fast block diagonalization algorithm using the permutation matrix from [T. Sogabe and M. El-Mikkawy, Appl. Math. Comput., 218, (2011), 2740-2743] and [A. Ohashi, T. Sogabe, and T. S. Usuda, Int. J. Pure and App. Math., 106, (2016), 513-523].

scholarly journals Partial Nulling Regularized Block Diagonalization Using Unfair Channel Selection for Post-Coding with Low-Complexity

2020 ◽  
Vol 10 (19) ◽  
pp. 6809
Author(s):  
Hyun-Sun Hwang ◽  
Jae-Hyun Ro ◽  
Young-Hwan You ◽  
Duckdong Hwang ◽  
Hyoung-Kyu Song
Keyword(s):  
Performance Enhancement ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Diversity Gain ◽  
Block Diagonalization ◽  
Time Diversity ◽  
Multiple Input ◽  
Input Multiple Output ◽  
User Interference

A number of requirements for 5G mobile communication are satisfied by adopting multi-user multiple input multiple output (MU-MIMO) systems. The inter user interference (IUI) which is an inevitable problem in MU-MIMO systems becomes controllable when the precoding scheme is used. The proposed scheme, which is one of the precoding schemes, is built on regularized block diagonalization (RBD) precoding and utilizes the partial nulling concept, which is to leave part of the IUI at the same time. Diversity gain is obtained by leaving IUI, which is made by choosing the row vectors of the channel matrix that are not nullified. Since the criterion for choosing the row vectors of the channel is the power of the channel, the number of selected row vectors of the channel for each device can be unfair. The proposed scheme achieves performance enhancement by obtaining diversity gain. Therefore, the bit error rate (BER) performance is better and the computational complexity is lower than RBD when the same data rate is achieved. When the number of reduced data streams is not enough for most devices to achieve diversity gain, the proposed scheme has better performance compared to generalized block diagonalization (GBD). The low complexity at the receiver is achieved compared to GBD by using the simple way to remove IUI.

A Nonunitary Joint Block Diagonalization Algorithm for Blind Separation of Convolutive Mixtures of Sources

2007 ◽  
Vol 14 (11) ◽  
pp. 860-863 ◽  
Author(s):  
Hicham Ghennioui ◽  
Fadaili El Mostafa ◽  
NadÈge Thirion-Moreau ◽  
Abdellah Adib ◽  
Eric Moreau
Keyword(s):  
Block Diagonalization ◽  
Blind Separation ◽  
Convolutive Mixtures ◽  
Diagonalization Algorithm

Design of Massive Multiuser MIMO System to Mitigate Inter Antenna Interference and Multiuser Interference in 5G Wireless Networks

2020 ◽  
pp. 693-701 ◽  
Author(s):  
Naga Raju Challa ◽  
◽  
Kalapraveen Bagadi
Keyword(s):  
Communication Networks ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Matrix Computations ◽  
User Terminal ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Major Factors ◽  
Value Decomposition ◽  
User Interference

Massive Multi-user Multiple Input Multiple Output (MU‒MIMO) system is aimed to improve throughput and spectral efficiency in 5G communication networks. Inter-antenna Interference (IAI) and Multi-user Interference (MUI) are two major factors that influence the performance of MU–MIMO system. IAI arises due to closely spaced multiple antennas at each User Terminal (UT), whereas MUI is generated when one UT comes in the vicinity of another UT of the same cellular network. IAI can be mitigated by the use of a pre-coding scheme such as Singular Value Decomposition (SVD) and MUI can be cancelled through efficient Multi-user Detection (MUD) schemes. The highly complex and optimal Maximum Likelihood (ML) detector involves a large number of computations, especially when in massive structures. Therefore, the local search-based algorithm such as Likelihood Ascent Search (LAS) has been found to be a better alternative for mitigation of MUI, as it results in near optimal performance using lesser number of matrix computations. Most of the literature have been aimed at mitigating either IAI or MUI, whereas the proposed work presents SVD pre-coding and LAS MUD to mitigate both IAI and MUI. Simulation results indicate that the proposed scheme can attain near-optimal bit error rate (BER) performance with fewer computations.

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