【摘要】 为提高DNN模型在无线通信中信道估计精度，提出一种基于1D-Concatenate的信道估计DNN模型优化方法。该方法将Concatenate进行一维（1D）数据转换，以跳跃连接的方式引入DNN模型，抑制梯度消失问题，运用1D-Concatenate恢复网络训练过程中丢失的数据特征，提高DNN信道估计精度。为验证优化方法的有效性，选取较典型的基于DNN的无线通信信道估计模型进行对比仿真实验。实验结果表明，本文提出的优化方法对已有DNN模型的估计增益提升可达77.10%，在高信噪比下信道增益提升可达3 dB。该优化方法能有效提高DNN模型在无线通信中的信道估计精度，特别是高信噪比下提升效果显著。更多还原

【Abstract】 In order to improve the channel estimation accuracy of DNN model in wireless communication, a DNN model optimization method based on 1D-Concatenate was proposed. In this method, Concatenate performs one-dimensional data transformation, the DNN model was introduced by hopping connection, the gradient disappearance problem was suppressed, and 1D-Concatenate was used to restore the data features lost during network training to improve the accuracy of DNN channel estimation. In order to verify the effectiveness of the optimization method, a typical DNN-based wireless communication channel estimation model was selected for comparative simulation experiments. Experimental results show that the estimated gain of the existing DNN model can be increased by 77.10% by the proposed optimization method, and the channel gain can be increased by up to 3 d B under high signal-to-noise ratio. This optimization method can effectively improve the channel estimation accuracy of DNN model in wireless communication, especially the improvement effect is significant under high signal-to-noise ratio.更多还原