Multiple Sequential Constraint Removal Algorithm For Channel Estimation In Vehicular Environment

Since the vehicular environment is highly mobile, the transmitted messages are affected by the wireless channel effect. This makes channel estimation one of the important tasks in Vehicle-To-Everything (V2X) communications. In this paper, we propose a novel Multiple Sequential Constraint Removal (MSCR) algorithm of channel estimation, dedicated to V2X communications. This method is performed on a vehicular doubly selective channel, where the communication system is based on the Orthogonal Frequency Division Multiplexing (OFDM) waveform, with a high order modulation (64-QAM). The proposed approach is compared to state-of-the-art Least square (LS), minimum mean square error (MMSE) estimators and the iterative Sequential Constraint Removal (SCR) algorithms. It shows that it achieves better performance, where we can get a small symbol error rate (SER) on highly mobile scenarios (Highway with non-line of the sight), with a latency time that meets with the V2X communication requirements.