The key to friendly collaboration in vehicular communication systems lies in the reliable communication between vehicles. The current systems, which employ fixed transmission schemes, significantly constrain system capacity in respect of spectrum. Besides, considering the dynamic environment of vehicular communications, the requirement for real-time scenario identification is particularly urgent. Hence, in order to improve the reliability of communications, this paper proposes an adaptive modulation and coding (AMC) technique driven by deep learning (DL)-based scenario identification (SI) in vehicular communication systems, namely SI-AMC. In contrast to the traditional AMC technique, our proposed SI-AMC attains a refined channel response estimation through the pre-discrimination of scenario features, thereby further enhancing vehicular communication performance. During the transmission process, the SI-AMC scheme achieves environment adaptability through rate-adaptive adjustments. Moreover, in terms of SI, we creatively design an enhanced convolutional neural network structure which ex-ploits a novel activation function and regularization strategies to enhance the robustness of the model. Simulation results show that the accuracy of SI reaches up to 97.86 % and the throughput of the entire link in vehicular communications is effectively promoted. Code and the model will be released at https://github.com/communicationDLl/SI-AMC.

In simultaneous transmit and receive wireless communications, digital self-interference (SI) cancellation is required before estimating the remote transmission (RT) channel. Considering the inherent connection between SI channel reconstruction and RT channel estimation, we propose a multi-layered M-estimate total least mean squares (m-MTLS) joint estimator to estimate both channels. In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel. Then, it gives the final RT channel estimation based on the average of the estimation values obtained from each layer. Compared to traditional minimum mean square error estimator and single-layered MTLS estimator, it demonstrates that the m-MTLS estimator has better performance of normalized mean squared difference. Besides, the simulation results also show the robustness of m-MTLS estimator even in scenarios where the local reference signal is contaminated with noise, and the received signal is impacted by strong impulse noise.