Development of intelligent receiver for MIMO-OFDM system

Wang, Huiqin; Li, Jianzeng

doi:10.1109/icccsp.2017.7944063

Cited by 2 publications

(1 citation statement)

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“…Second, the neural network was used to map the relationship between the receiving symbol and transmitting symbol. The trained network can be utilized to decode the received signal directly [19] [20]. The channel estimation was implicit, and the real channel response could not be obtained.…”

Section: Introductionmentioning

confidence: 99%

Online LSTM-Based Channel Estimation for HF MIMO SC-FDE System

Wang

Yang

et al. 2020

IEEE Access

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The long distance high frequency (HF) communication suffers from time varying multipath fading. Multiple-Input Multiple-Output (MIMO) and Single-Carrier Frequency Domain Equalization (SC-FDE) have been introduced to HF communication system to combat fading. Because accurate channel estimation is essential for system operation, an architecture of signal flow chart has been designed for the HF MIMO SC-FDE system. In the architecture, an online Long Short-Term Memory (LSTM) estimator is proposed. Different from the channel responses time series created by the LSTM training and prediction loop, a corrected channel responses that are obtained through the received data symbols and the restored transmitting data symbols make up the channel responses time series. In order to evaluate the performance of channel estimators, a simulation system has been built. The uncorrelated and correlated channels are simulated referring to International Telecommunication Union (ITU)-R F.1487 standard and Kronecker model. The simulation results demonstrate that the online LSTM estimator outperforms Least Square (LS) and Recursive Least Square (RLS) estimators in terms of Bits Error Rate (BER) and Mean Square Error (MSE). The online LSTM estimator is capable of tracking the time varying HF MIMO channels. It has potentiality in actual long distance HF communication.

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Section: Introductionmentioning

confidence: 99%