基于深度学习的多载波系统信道估计与检测

Channel estimation and detection method for multicarrier system based on deep learning

为了提升滤波器组多载波(FBMC)系统的通信质量,针对符号检测与信道估计问题,研究系统框架和虚部干扰问题,提出基于深度学习的FBMC系统信道估计与检测方法.搭建完整的FBMC-偏移正交幅度调制(OQAM)系统与深度学习模型结合的仿真系统,设计接收数据的特征与标签处理;采用ResNet-DNN神经网络对信道符号检测模块建模,改进原模型网络结构和优化模型参数,和传统的分类器相比,提高了符号检测的准确性;采用CNN+NN模型对信道估计、均衡、符号检测模块进行建模和集成,理论分析和仿真结果表明,新方法的抗噪声能力、鲁棒性和误比特率(BER)性能均优于正交频分复用(OFDM)系统和基于导频估计的FBMC系统性能.

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.