Performance Improvement of the Combined AMC-MIMO Systems with MCS Level Selection Technique

2007 ◽  
Author(s):  
Sangjin Ryoo ◽  
Kwangwook Choi ◽  
Kyunghwan Lee ◽  
Insik Cho ◽  
Gilsang Yoon ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Mimo Systems ◽  
Selection Technique

scholarly journals Performance Improvement for Multi-User Millimeter-Wave Massive MIMO Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 87735-87748
Author(s):  
Diego Fernando Carrera ◽  
Cesar Vargas-Rosales ◽  
Rafaela Villalpando-Hernandez ◽  
Jose Alejandro Galaviz-Aguilar
Keyword(s):  
Performance Improvement ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems

scholarly journals Computation of System Performance of MIMO Technique in Communication Channel using V-BLAST Method

2019 ◽  
Vol 8 (2) ◽  
pp. 898-901
Keyword(s):  
Performance Improvement ◽  
Error Rate ◽  
Communication Channel ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Block Codes ◽  
Space Time ◽  
System A ◽  
Multiple Input ◽  
Input Multiple Output

In order to improvement both system performances and data rate Multiple Input Multiple Output techniques play an important role in transmission system. A number of techniques are used to do the needful work for performance improvement in MIMO systems belongs to different block codes, apart from that BLAST architecture are used Such as Diagonal Bell laboratories layered space-time (D-BLAST), Vertical Bell Labs Space-Time Architecture (V-BLAST) method. This work defines the performance improvement using V-BLAST technique in Multiple Input Multiple Output detector. Here we discuss the concept of Multiple Input Multiple Output with BLAST architecture. Depends upon the Bit Error Rate and Frame Error Rate, the comparison is made with the existing methods.

scholarly journals Bat algorithm and k-means techniques for classification performance improvement

2019 ◽  
Vol 15 (3) ◽  
pp. 1411
Author(s):  
Rozlini Mohamed ◽  
Munirah Mohd Yusof ◽  
Noorhaniza Wahid ◽  
Norhanifah Murli ◽  
Muhaini Othman
Keyword(s):  
Feature Selection ◽  
Performance Improvement ◽  
Performance Measures ◽  
Selection Process ◽  
Bat Algorithm ◽  
Classification Performance ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Discretization Technique

This paper presents Bat Algorithm and K-Means techniques for classification performance improvement. The objective of this study is to investigate efficiency of Bat Algorithm in discrete dataset and to find the optimum feature in discrete dataset. In this study, one technique that comprise the discretization technique and feature selection technique have been proposed. Our contribution is in two process of classification: pre-processing and feature selection process. First, to proposed discretization techniques called as BkMD, where we hybrid Bat Algorithm technique and K-Means classifier. Second, to proposed BkMDFS as feature selection technique where Bat Algorithm is embed into BkMD. In order to evaluate our proposed techniques, 14 continuous dataset from various applications are used in experiment. From the experiment, results show that BkMDFS outperforms in most performance measures. Hence it shows that, Bat Algorithm have potential to be one of the discretization technique and feature selection technique.

New order selection technique using information criteria applied to SISO and MIMO systems predistortion

2013 ◽  
Vol 5 (2) ◽  
pp. 123-131 ◽  
Author(s):  
M.V. Amiri ◽  
S.A. Bassam ◽  
M. Helaoui ◽  
F.M. Ghannouchi
Keyword(s):  
Mean Square Error ◽  
Mimo Systems ◽  
Cost Minimization ◽  
Information Criterion ◽  
Information Criteria ◽  
New Order ◽  
Order Selection ◽  
Mean Square ◽  
Model Order Selection ◽  
Selection Technique

This paper presents a new order selection technique of matrix memory polynomial technique that models the nonlinearities of single-branch and multi-branch transmitters. The new criteria take into account the complexity of the model in addition to its mean-square error in the selection criteria. The quasi-convexity of the proposed criteria was proven in this work. By using this proposed Akaike information criterion (AIC) and Bayesian information criterion (BIC) criteria, the model order selection was cast as a cost minimization problem. To minimize the criteria, modified gradient descent and simulated annealing algorithms were utilized which resulted in a considerable reduction in the number of search iterations. The performances of the criteria were shown by comparing the normalized mean square error (NMSE) of a higher-order model and the optimum model. It has been shown that the NMSE difference is <0.5 dB, but the complexity is much smaller.

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