Learning the Wireless V2I Channels Using Deep Neural Networks
This addresses the challenge of efficient channel detection for high data rate wireless systems in V2I scenarios, representing an incremental improvement by applying existing deep learning techniques to a specific domain.
The paper tackles the problem of real-time channel estimation for high-mobility vehicle-to-infrastructure (V2I) communications by developing a deep learning-based prediction method, demonstrating that neural networks can quickly learn channel properties and trends to estimate future channel responses.
For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenges to obtaining real-time channel measurements. Deep learning (DL) techniques, in this context, offer learning ability and optimization capability that can approximate many kinds of functions. In this paper, we develop a DL-based channel prediction method to estimate channel responses for V2I communications. We have demonstrated how fast neural networks can learn V2I channel properties and the changing trend. The network is trained with a series of channel responses and known pilots, which then speculates the next channel response based on the acquired knowledge. The predicted channel is then used to evaluate the system performance.