An efficient approximate maximum likelihood signal detection for MIMO systems

2007 ◽  
Vol 24 (1) ◽  
pp. 23-26
Author(s):  
Xuehong Cao
Keyword(s):  
Maximum Likelihood ◽  
Signal Detection ◽  
Mimo Systems ◽  
Approximate Maximum Likelihood

scholarly journals Parallel signal detection for generalized spatial modulation MIMO systems

2021 ◽  
Author(s):  
Victor M. Garcia-Molla ◽  
M. Angeles Simarro ◽  
F. J. Martínez-Zaldívar ◽  
Murilo Boratto ◽  
Pedro Alonso ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Maximum Likelihood ◽  
Signal Detection ◽  
Parallel Algorithm ◽  
Mimo Systems ◽  
Spatial Modulation ◽  
Parallel Method ◽  
Worst Case

AbstractGeneralized Spatial Modulation is a recently developed technique that is designed to enhance the efficiency of transmissions in MIMO Systems. However, the procedure for correctly retrieving the sent signal at the receiving end is quite demanding. Specifically, the computation of the maximum likelihood solution is computationally very expensive. In this paper, we propose a parallel method for the computation of the maximum likelihood solution using the parallel computing library OpenMP. The proposed parallel algorithm computes the maximum likelihood solution faster than the sequential version, and substantially reduces the worst-case computing times.

scholarly journals Selecting between causal and noncausal models with quantile autoregressions

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alain Hecq ◽  
Li Sun
Keyword(s):  
Time Series ◽  
Maximum Likelihood ◽  
Latin American ◽  
Financial Time Series ◽  
Selection Criterion ◽  
Likelihood Methods ◽  
Approximate Maximum Likelihood ◽  
Financial Time ◽  
Maximum Likelihood Methods ◽  
Latin American Countries

AbstractWe propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This new modelling perspective is appealing for investigating the presence of bubbles in economic and financial time series, and is an alternative to approximate maximum likelihood methods. We illustrate our analysis using hyperinflation episodes of Latin American countries.

scholarly journals Learning Based Signal Detection for MIMO Systems with Unknown Noise Statistics

2021 ◽  
pp. 1-1
Author(s):  
Ke He ◽  
Le He ◽  
Lisheng Fan ◽  
Yansha Deng ◽  
George K. Karagiannidis ◽  
...  
Keyword(s):  
Signal Detection ◽  
Mimo Systems ◽  
Noise Statistics

WAVELET ESTIMATORS FOR LONG MEMORY IN STOCK MARKETS

2009 ◽  
Vol 12 (03) ◽  
pp. 297-317 ◽  
Author(s):  
ANOUAR BEN MABROUK ◽  
HEDI KORTAS ◽  
SAMIR BEN AMMOU
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Stock Returns ◽  
Long Memory ◽  
Weighted Least Squares ◽  
Ordinary Least Squares ◽  
Estimation Accuracy ◽  
Approximate Maximum Likelihood ◽  
Approximate Maximum Likelihood Estimator ◽  
Wavelet Estimators

In this paper, fractional integrating dynamics in the return and the volatility series of stock market indices are investigated. The investigation is conducted using wavelet ordinary least squares, wavelet weighted least squares and the approximate Maximum Likelihood estimator. It is shown that the long memory property in stock returns is approximately associated with emerging markets rather than developed ones while strong evidence of long range dependence is found for all volatility series. The relevance of the wavelet-based estimators, especially, the approximate Maximum Likelihood and the weighted least squares techniques is proved in terms of stability and estimation accuracy.

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