论文标题
基于学习的基于学习的通道估计，使用数据叠加叠加的飞行员在正交频道多路复用系统中
Transfer Learning-based Channel Estimation in Orthogonal Frequency Division Multiplexing Systems Using Data-nulling Superimposed Pilots
论文作者
论文摘要
数据提出的叠加飞行员（DNSP）有效地减轻了正交频施加多元（OFDM）系统中叠加训练（ST）的叠加训练（ST）的叠加干扰（CE），同时面临估计精度和计算复杂性的挑战。通过在无线通信的物理层中开发有希望的深度学习解决方案（DL），我们将融合DNSP和DL以应对本文中的这些挑战。然而，由于无线场景的变化，DL的模型不匹配导致CE的性能降级，因此面临网络再培训的问题。为了解决这个问题，进一步为基于DL的DNSP方案提供了轻巧的转移学习（TL）网络，因此在OFDM系统中构建了基于TL的CE。具体而言，基于线性接收器，首先采用最小二乘估计来提取CE的初始特征。借助提取的特征，我们开发了一个卷积神经网络（CNN），以融合DLB的CE和DNSP CE的解决方案。最后，构建了轻巧的TL网络来解决模型不匹配。为此，OFDM系统中DNSP方案的新型CE网络是结构的，这提高了其估计准确性并减轻模型不匹配。实验结果表明，在所有信噪比（SNR）区域中，所提出的方法比具有最小均方误差（MMSE）CE的现有DNSP方案的归一化平方误差（NMSE）较低。例如，当SNR为0分贝（DB）时，所提出的方案的NMSE与基于MMSE的CE方案的NMSE相似，从而显着提高了CE的估计精度。此外，相对于现有方案，提出的方案的改进还提出了其对参数变化影响的鲁棒性。
Data-nulling superimposed pilot (DNSP) effectively alleviates the superimposed interference of superimposed training (ST)-based channel estimation (CE) in orthogonal frequency division multiplexing (OFDM) systems, while facing the challenges of the estimation accuracy and computational complexity. By developing the promising solutions of deep learning (DL) in the physical layer of wireless communication, we fuse the DNSP and DL to tackle these challenges in this paper. Nevertheless, due to the changes of wireless scenarios, the model mismatch of DL leads to the performance degradation of CE, and thus faces the issue of network retraining. To address this issue, a lightweight transfer learning (TL) network is further proposed for the DL-based DNSP scheme, and thus structures a TL-based CE in OFDM systems. Specifically, based on the linear receiver, the least squares estimation is first employed to extract the initial features of CE. With the extracted features, we develop a convolutional neural network (CNN) to fuse the solutions of DLbased CE and the CE of DNSP. Finally, a lightweight TL network is constructed to address the model mismatch. To this end, a novel CE network for the DNSP scheme in OFDM systems is structured, which improves its estimation accuracy and alleviates the model mismatch. The experimental results show that in all signal-to-noise-ratio (SNR) regions, the proposed method achieves lower normalized mean squared error (NMSE) than the existing DNSP schemes with minimum mean square error (MMSE)-based CE. For example, when the SNR is 0 decibel (dB), the proposed scheme achieves similar NMSE as that of the MMSE-based CE scheme at 20 dB, thereby significantly improving the estimation accuracy of CE. In addition, relative to the existing schemes, the improvement of the proposed scheme presents its robustness against the impacts of parameter variations.
