Geometry-Based Stochastic Modeling and Estimation of Vehicle to Vehicle Channels

In this paper, a geometry-based stochastic channel model (GSCM) for vehicle-to-vehicle (V2V) wireless communica- tion is developed. The channel model reveals that the channel representation in delay-Doppler domain can be divided into four regions. In each region, the V2V channel can be modeled using a hybrid sparse/diffuse (HSD) model. Prior art on hybrid channel estimation for linear time-invariant channels is extended to the time-varying case. Furthermore, the effects of pulse shape leakage are explicitly determined and compensated. Simulation results shows that exploiting the V2V channel properties in the delay-Doppler domain, yields significantly improved channel estimates over unstructured approaches (more than 10 dB gain in SNR)

Structured Sparse Approximation via Generalized Regularizers: With Application to V2V Channel Estimation

In this paper, we consider the estimation of a signal that has both group- and element-wise sparsity (joint sparsity); motivated by channel estimation in vehicle-to-vehicle channels. A general approach for the design of separable regularizing functions is proposed to adaptively induce sparsity in the estimation. A joint sparse signal estimation problem is formulated via these regularizers and its optimal solution is computed based on proximity operations. Our optimization results are quite general and they can be applied in the context of hierarchical sparsity models as well. The proposed recovery algorithm is a nested iterative method based on the alternating direction method of multipliers (ADMM). Due to regularizer separability, key operations can be performed in parallel. V2V channels are estimated by exploiting the joint sparsity (group/element-wise) exhibited in the delay-Doppler domain. Simulation results reveal that the proposed method can achieve as much as a 10 dB gain over previously examined methods

Geometry-Based Modeling of Wideband Industrial Indoor Radio Propagation Channels

In this paper, we present a geometrical scattering model for a typical class of industrial indoor environments. The proposed industrial reference model takes into account scattering components arising from metallic structures and the surrounding walls of the investigated environment. Starting from the geometrical scattering model, we derive the analytical expressions of the probability density function (PDF) of the angle of arrival (AoA), PDF of the time of arrival (ToA), and the autocorrelation function (ACF) in the frequency domain. The obtained results reveal a large difference between industrial channels and other home and office environments. The theoretical results of the reference model are validated by simulation results of a channel simulator designed by employing the sum-of-cisoids (SOC) principle. The proposed channel model is useful for the design and performance evaluation of wireless communication systems operating in industrial environments.acceptedVersionnivå

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.Comment: 36 pages, 7 figures, journa