Reduced-state maximum-likelihood detection for OFDM systems with insufficient cyclic prefix

2021 ◽  
pp. 101560
Author(s):  
Wei Shi ◽  
Chunming Zhao ◽  
Wei Xu
Keyword(s):  
Maximum Likelihood ◽  
Cyclic Prefix ◽  
Ofdm Systems ◽  
Maximum Likelihood Detection ◽  
Reduced State

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
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