Basis expansion model-based improved regularized orthogonal matching pursuit channel estimation for V2X fast time-varying SC-FDMA

Abstract

In order to further improve the vehicle-to-everything (V2X) communication performance of the Internet of vehicles, a basis expansion model (BEM) was adopted and suitable for high-speed mobile scenarios to transform the channel estimation into a sparse signal reconstruction.Furthermore, it was proved that the BEM coefficients were sparse, and an improved regularized orthogonal matching pursuit (iROMP) channel estimation algorithm based on BEM (BEM-iROMP) was proposed.BEM coefficients were acquired by the iROMP, and finally the feedback results were iterated to achieve the optimal channel estimation.Simulation results show that in comparison with the least square (LS), linear minimum mean squared error (LMMSE), and BEM-LS channel estimation algorithms, the proposed algorithm can effectively improve the normalized mean square error (NMSE) and bit error rate (BER) performance.

Keywords

• channel estimation;vehicle-to-everything;high-speed mobility;compressed sensing;basis expansion model;regularized orthogonal matching pursuit;SC-FDMA