Sparse Channel Estimation for Cluster-Based Vehicle-to-Vehicle Channels in Roadside Scattering Environments

Abstract

In this paper, the sparsity adaptive matching pursuit (SAMP) channel estimation scheme for cluster-based vehicle-to-vehicle (V2V) channel model is proposed. To efficiently illustrate the real vehicular scenarios and evaluate non-stationarity that has a significant impact on the design of V2V channel estimation, we divide all effective scatterers into three categories of clusters in terms of relative position of the scattering objects. A mathematical expression of channel impulse response (CIR) is derived. Furthermore, the sparse channel estimation schemes for V2V channel model are thoroughly studied. Finally, numerical simulations are presented to demonstrate the effectiveness of the proposed SAMP method in comparison with the conventional channel estimation schemes.