We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. For this, we propose a low-complexity recurrent neural network (RNN)-based estimator that allows channel equalization of a sequence of channel observations based on independent time- and frequency-domain long short-term memory (LSTM) cells. Motivated by Vehicle-to-Everything (V2X) applications, we simulate time- and frequency-selective channels with orthogonal frequency division multiplex (OFDM) and extend our channel models in such a way that a continuous degradation from line-of-sight (LoS) to non-line-of-sight (NLoS) conditions can be emulated. It turns out that the NN-based system cannot just compete with the LMMSE equalizer, but it also can be trained w.r.t. resilience against system parameter mismatch. We thereby showcase the conceptual simplicity of such a data-driven system design, as this not only enables more robustness against, e.g., signal-to-noise-ratio (SNR) or Doppler spread estimation mismatches, but also allows to use the same equalizer over a wider range of input parameters without the need of re-building (or re-estimating) the filter coefficients. Particular attention has been paid to ensure compatibility with the existing IEEE 802.11p piloting scheme for V2X communications. Finally, feeding the payload data symbols as additional equalizer input unleashes further performance gains. We show significant gains over the conventional LMMSE equalization for highly dynamic channel conditions if such a data-augmented equalization scheme is used.

翻译：我们将基于神经网络(NN)的频道估计潜力与古典线性最小平均差平方差(LMMSE)的测深器(也称为Wiener过滤器)进行比较。 为此,我们提议了一个低复杂性经常性神经网络(RNN)基于网络的测深器(RNN)的测深器,允许频道在独立的时间和频率-频率-长期短期内存(LSTM)的基础上对频道观测序列进行均匀。受“车辆对一切(V2X)”应用的激励,我们模拟基于或调频分多功能(OFDM)的测算器(LMS)的测算器,并扩展我们的频道的兼容性模型,以便从“视线(LOS)”到“无线-视距(NLLLLS)”的连续性变异性变异性(LLS)的测算器持续率。 NER的测算法不能与LMS(LMS)的测算法相比,如果“Vr.t.I-real-ral-lational-lational-lational laisal laislational yal Syal yal lax)的测算系统的测算系统的测算,也不能的测算法,也使得LO-sal-l-s的测算能能能能能能能能能够比等高的测算结果,也比。