论文标题
双重选择性OFDM渠道的盲目估计：深度学习算法和理论
Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory
论文作者
论文摘要
我们为正交频施加多路复用（OFDM）系统的双重选择性褪色通道估计的基本旧问题提供了新一代解决方案。对于基于OFDM的系统，我们提出了一个深度学习（DL）的盲目选择性频道估计器。与相应的最新估计器不同，即使在对深度褪色的双重选择通道的估计期间，该估计器确实不需要试点符号。我们还提供了基于过度参数的RELU FNN的盲人OFDM通道估计器的测试平方误差（MSE）性能的第一个此类理论。
We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems. For systems based on OFDM, we propose a deep learning (DL)-based blind doubly selective channel estimator. This estimator does require no pilot symbols, unlike the corresponding state-of-the-art estimators, even during the estimation of a deep fading doubly selective channel. We also provide the first of its kind theory on the testing mean squared error (MSE) performance of our investigated blind OFDM channel estimator based on over-parameterized ReLU FNNs.
