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

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.

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Maximum likelihood-based adaptive iteration algorithm design for joint CFO and channel estimation in MIMO-OFDM systems

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-020-00711-5 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Nan-Hung Cheng ◽

Kai-Chieh Huang ◽

Yung-Fang Chen ◽

Shu-Ming Tseng

Keyword(s):

Maximum Likelihood ◽

Channel Estimation ◽

Orthogonal Frequency Division Multiplexing ◽

Carrier Frequency Offset ◽

Mean Squared Error ◽

Joint Estimation ◽

Iteration Algorithm ◽

Time Varying ◽

Ofdm Systems ◽

Mimo Ofdm

AbstractIn this paper, we present a joint time-variant carrier frequency offset (CFO) and frequency-selective channel response estimation scheme for multiple input-multiple output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems for mobile users. The signal model of the MIMO-OFDM system is introduced, and the joint estimator is derived according to the maximum likelihood criterion. The proposed algorithm can be separated into three major parts. In the first part of the proposed algorithm, an initial CFO is estimated using derotation, and the result is used to apply a frequency-domain equalizer. In the second part, an iterative method is employed to locate the fine frequency peak for better CFO estimation. An adaptive process is used in the third part of the proposed algorithm to obtain the updated CFO estimation and track parameter time variations, including the time-varying CFOs and time-varying channels. The computational complexity of the proposed algorithm is considerably lower than that of the maximum likelihood-based grid search method. In a simulation, the mean squared error performance of the proposed algorithm was close to the Cramer-Rao lower bound. The simulation results indicate that the proposed novel joint estimation algorithm provides a bit error rate performance close to that in the perfect channel estimation condition. The results also suggest that the proposed method has reliable tracking performance in Jakes’ channel models.

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