Near maximum likelihood detection algorithm based on 1-flip local search over uniformly distributed codes

2013 ◽  
Author(s):  
Amor Nafkha
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Detection Algorithm ◽  
Maximum Likelihood Detection ◽  
Distributed Codes

SAGE-ML Joint Estimation and Detection Algorithm in MIMO-OFDM Systems

2013 ◽  
Vol 389 ◽  
pp. 494-500
Author(s):  
Jing Peng Gao ◽  
Dan Feng Zhao ◽  
Chao Qun Wu
Keyword(s):  
Maximum Likelihood ◽  
Channel Estimation ◽  
Detection Algorithm ◽  
Joint Estimation ◽  
Ofdm Systems ◽  
Channel State ◽  
Channel Change ◽  
Maximum Likelihood Detection ◽  
System Overhead ◽  
Mimo Ofdm

In order to improve the decoding performance of MIMO-OFDM system in the case of the channel state information is not accurate enough, a new algorithm is proposed, which combines SAGE algorithm, DFT-LS channel estimation and maximum likelihood detection algorithm. The algorithm utilizes joint iterative technology to achieve channel estimation and decoding effect, thereby enhances the reliability of the system. Theoretical study and simulation results show that the proposed algorithm can track the channel change correctly without increasing the system overhead, and the convergence speed is accelerated. Besides, the performance is superior to the commonly used joint detection algorithm. Moreover, comparing with the ideal channel estimation under the maximum likelihood detection algorithm, the new proposed algorithm only has a loss of 0.5dB with the same bit error rate.

Joint maximum likelihood detection and link selection for cooperative MIMO relay systems

2014 ◽  
Vol 8 (14) ◽  
pp. 2489-2499 ◽  
Author(s):  
Thomas Hesketh ◽  
Stephen Wales ◽  
Rodrigo C. de Lamare
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Detection ◽  
Mimo Relay ◽  
Relay Systems ◽  
Cooperative Mimo ◽  
Selection For

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

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