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Optimization-of-Channel-Estimation-Based-on-Deep-Learning
Optimization-of-Channel-Estimation-Based-on-Deep-Learning Public由于传统信道估计方法(如LS,MMSE)在高速运动和低SNR场景下信道估计精度不高,本项目基于深度学习方法中已有的 CNN卷积神经网络层、注意力机制、LSTM长短期记忆神经网络,将时间-频率-空间三者联合考虑,进行三维卷积变换提取特征,并 引入双向LSTM对抗多普勒频移,优化损失函数为绝对差损失和平滑损失两部分的线性叠加。最终搭建的STA-ResNet在10轮迭代后 train loss会逐…
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