Low-complexity channel estimation for V2X systems using feed-forward neural networks

In vehicular communications, channel estimation is a complex problem due to the joint time-frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed-forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state-of-the-art neural-network-based implementations such as feed-forward and super-resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi-path interference. In vehicular communication systems, estimating channels is complex due to their time-frequency selectivity. This paper introduces a novel pilot constellation scheme coupled with a compact feed-forward neural network, aiming to enhance the accuracy of channel estimation in vehicle-to-everything (V2X) systems while minimizing implementation complexity. The approach outperforms traditional methods and advanced neural-network-based methods, particularly in environments with small subcarrier spacings, thus aiding the provision of more dependable mobile services in rapidly changing vehicular communication channels with significant multipath interference. image