  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.
