Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications

TL;DR

This paper compares neural network-based channel estimation with classical Wiener filtering in V2X communications, demonstrating that RNNs can offer robustness and adaptability advantages in dynamic channel conditions.

Contribution

The study introduces a low-complexity RNN-based estimator for 2D channel estimation in V2X, showing its competitive performance and robustness over traditional LMMSE methods.

Findings

RNN-based estimator performs comparably to LMMSE.

Neural network approach offers increased robustness to system mismatches.

Data-augmented equalization yields significant performance improvements.

Abstract

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.