Deep Learning-Based Channel Estimation for HPO-MIMO Systems in IoV Scenario

Non-linear MIMO technology is proven to work out the excessive power consumption issue caused by the base station with more than 100 antenna pairs that have been adopted for the Internet of Vehicles (IoV). However, the non-linear MIMO scheme applied in the IoV scenario does not consider the real-world channel with the characteristics of vehicle motion. In addition, traditional channel estimation (CE) in non-linear MIMO technology are not robust under the variation of channel parameters in IoV.A channel estimation scheme of Half Phase Only (HPO)-MIMO based on Convolutional Neural Network (CNN) is proposed, which can get more accurate channel estimation results and achieve perfect robustness for changing channel parameters. Besides, the COST 2100 channel model is used, which is more suitable for simulating the IoV scenarios. Moreover, the channel estimation scheme based on CNN al-gorithm can be used favorably in the non-linear MIMO and IoV scenarios. Simulation results show that the CNN-based CE scheme we proposed achieves outstanding mean squared error (MSE) performance compared to the Generalized Approximate Messaging (GAMP) algorithm. Furthermore, the rationality of using the COST 2100 channel model is proven that have excellent performances.