The system framework and imaginary interference were analyzed aiming at the problem of symbol detection and channel estimation in order to effectively improve the communication quality of filter bank multicarrier (FBMC) system. A channel estimation and detection method for FBMC system was proposed based on deep learning. A complete simulation system was established by combining FBMC-offset quadrature amplitude modulation (OQAM) with deep learning model, and the characteristics and label processing of received data were designed. ResNet-DNN neural network was used to model the channel symbol detection module. The original model structure and optimized model parameters were improved, which improved the accuracy of symbol detection compared with traditional classifiers. CNN+NN model was used to model and integrate for estimating, equalizing and detecting channel symbols. The theoretical analysis and simulation results show that the new method is superior to orthogonal frequency division multiplexing (OFDM) system and FBMC system based on pilot estimation in terms of noise resistance, robustness and bit error rate (BER) performance.
Zhou-fei WANG,Wei-na YUAN. Channel estimation and detection method for multicarrier system based on deep learning. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 732-738.
Tab.3Communication simulation parameters of FBMC system
参数
数值
激活函数
ReLU
第1个残差层神经元个数
500
后2个残差层神经元个数
200
输出层全连接层神经元个数
16
Dropout率
0.5
损失函数
交叉熵
优化器
Adam
Tab.4ResNet-DNN model simulation parameters
Fig.4BER performance comparison on AWGN channel
Fig.5BER performance comparison on PedestrianA channel
Fig.6BER Performance comparison of adding DFT algorithms in AWGN Channel
Fig.7CNN+NN network model structure
参数
数值
激活函数
ReLU
第1通道卷积核大小
2×12
第2通道卷积核大小
3×12
正则项
L2正则
Dropout率
0.5
损失函数
交叉熵函数
优化器
Adam
NN隐藏层神经元个数
500
Tab.5CNN+NN model simulation parameters
Fig.8BER performance on AWGN channel（CNN+NN）
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