Vehicle-to-Vehicle Communication Channel Estimator Based on Gate Recurrent Unit

With the development of autonomous vehicle operation, vehicle-to-vehicle (V2V) communication plays an increasingly important role. However, in high-speed mobile environments, the channel has fast time-varying, which significantly decreases the property of channel estimation. On the other hand, the frame structure of the IEEE 802.11p standard contains a few number of pilots and a large pilot interval, which is not sufficient to track the rapidly changing channel environment accurately. In recent years, deep learning has been widely used for channel estimation. However, these methods typically perform poorly in high-speed mobility scenarios or have excessively high computational complexity. To alleviate such issues, this study proposes a channel estimation method by combining the sparrow search algorithm (SSA) and gated recurrent unit (GRU). In addition, this paper adds the attention mechanism to GRU to improve the robustness of the model. The computer simulation results confirm that, compared to traditional schemes, the proposed estimator can achieve a lower bit error rate (BER) and normalized mean squared error (NMSE). At the same time, the computational complexity of the algorithm has been reduced to some extent, allowing the estimator to complete the channel estimation faster. This study provides a useful reference for optimizing neural networks and thus improving the performance of channel estimators.