基于基扩展模型的改进正则化正交匹配追踪V2X 快时变SC-FDMA信道估计

为了进一步提升车联万物(V2X)的通信性能,首先根据信道冲激响应的稀疏性建立了适用于高速移动场景的基扩展模型(BEM);其次,证明了BEM系数具有稀疏性,将信道估计问题转化为稀疏信号重构问题,进而提出基于BEM的改进正则化正交匹配追踪(iROMP)迭代稀疏信道估计算法(简称为BEM-iROMP算法)。所提算法通过iROMP获取BEM系数,利用反馈结果不断迭代以达到最优信道估计。仿真结果表明,与最小二乘法、线性最小均方误差和BEM-LS信道估计算法相比,所提算法能够有效提高V2X快时变信道下单载波频分多址系统的归一化均方误差和误码率性能。

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

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.