论文标题
在环境反向散射通信中，深度剩余的学习辅助渠道估计
Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications
论文作者
论文摘要
渠道估计是实现有效的环境反向散射通信（AMBC）系统的一个挑战性问题。在这封信中，AMBC中的频道估计是建模为一个降级问题，并且开发了基于卷积的神经网络的深度剩余学习DeNoiser（CRLD），以直接从接收到的噪声飞行员信号中恢复通道系数。为了同时利用飞行员信号的空间和时间特征，专门设计了一种新颖的三维（3D）denoising块，以促进CRLD中的denoing。此外，我们提供理论分析以表征所提出的CRLD的性质。仿真结果表明，所提出的方法的性能方法是使用完美的统计通道相关矩阵的最佳最小均方误差（MMSE）估计器的性能。
Channel estimation is a challenging problem for realizing efficient ambient backscatter communication (AmBC) systems. In this letter, channel estimation in AmBC is modeled as a denoising problem and a convolutional neural network-based deep residual learning denoiser (CRLD) is developed to directly recover the channel coefficients from the received noisy pilot signals. To simultaneously exploit the spatial and temporal features of the pilot signals, a novel three-dimension (3D) denoising block is specifically designed to facilitate denoising in CRLD. In addition, we provide theoretical analysis to characterize the properties of the proposed CRLD. Simulation results demonstrate that the performance of the proposed method approaches the performance of the optimal minimum mean square error (MMSE) estimator with perfect statistical channel correlation matrix.
