Nested Sparse Approximation: Structured Estimation of V2V Channels Using Geometry-Based Stochastic Channel Model

Future intelligent transportation systems promise increased safety and energy efficiency. Realization of such systems will require vehicle-to-vehicle (V2V) communication technology. High fidelity V2V communication is, in turn, dependent on accurate V2V channel estimation. V2V channels have characteristics differing from classical cellular communication channels. Herein, geometry-based stochastic modeling is employed to develop a characterization of V2V channel channels. The resultant model exhibits significant structure; specifically, the V2V channel is characterized by three distinct regions within the delay-Doppler plane. Each region has a unique combination of specular reflections and diffuse components resulting in a particular element-wise and group-wise sparsity. This joint sparsity structure is exploited to develop a novel channel estimation algorithm. A general machinery is provided to solve the jointly element/group sparse channel (signal) estimation problem using proximity operators of a broad class of regularizers. The alternating direction method of multipliers using the proximity operator is adapted to optimize the mixed objective function. Key properties of the proposed objective functions are proven which ensure that the optimal solution is found by the new algorithm. The effects of pulse shape leakage are explicitly characterized and compensated, resulting in measurably improved performance. Numerical simulation and real V2V channel measurement data are used to evaluate the performance of the proposed method. Results show that the new method can achieve significant gains over previously proposed methods.