基于ResNet-DNN的滤波器组多载波系统信道估计与检测

CHANNEL ESTIMATION AND DETECTION OF FILTER BANK MULTICARRIER SYSTEM BASED ON RESNET-DNN

  • 摘要: 滤波器组多载波FBMC系统因频谱灵活性高以及高频谱效率受到广泛关注。针对FBMC系统存在固有的虚部干扰,导致信道估计困难,提出基于残差神经网络的两种FBMC系统信道估计与检测方案,方案一中采用残差神经网络对信道估计模块进行建模,完成稀疏信道时频响应矩阵到真实信道时频响应的逼近。方案二中采用残差神经网络对信道估计、信道均衡、OQAM解调及判决模块进行建模和集成。实验结果表明采用两种方案进行信道估计与检测相比传统信道估计算法有更好的误码率性能。

     

    Abstract: Filter bank multicarrier FBMC systems are widely concerned because of their high spectral flexibility and efficiency. Two channel estimation and detection schemes of FBMC system based on residual neural network are proposed for the inherent imaginary interference of FBMC system. In scheme 1, residual neural network was used to model the channel estimation module to complete the approximation of the time-frequency response matrix of sparse channel to the time-frequency response of real channel. In scheme 2, residual neural network was used to model and integrate channel estimation, channel equalization, OQAM demodulation and decision module. Experimental results show that the two schemes have better bit error rate performance than the traditional channel estimation algorithms.

     

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