5G Positioning and Mapping with Diffuse Multipath

Abstract

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters (delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods break down or lead to significant biases. We present a novel tensorbased method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach.

Exploiting Diffuse Multipath in 5G SLAM

“…The roughness determines the amount of diffuse multipath components. We model each surface with three related parameters: the scattering power S ≥ 0 (which determines the fraction of power that is scattered), the reflection power R ≥ 0 (which determines the fraction of power that is reflected, with R+S ≤ 1, as some of the power can be absorbed) and the smoothness 1 parameter α R ≥ 0 (which determines the spread of the diffuse multipath) [8]. The state of a surface x can therefore be described by these three parameters and the fixed virtual anchor (VA), located at x VA ∈ R 3 , which is the reflection of the BS with respect to the surface.…”

“…Conceptually, these paths can be interpreted as coming from random points on the surface, with a spatial distribution that depends on the roughness. Among the paths, there may be a deterministic specular component, while all remaining paths are diffuse components and thus random [8].…”

“…• Channel estimation method: We consider the channel estimator in [8], which uses a tensor ESPRIT-based method to estimate 5G channel parameters in the presence of combined specular and diffuse multipath from the surface. With this specific channel estimator, we can generate simulated data to determine the likelihood function.…”

“…In [7] surface roughness was considered in a radar applications, modeled as a number of sub-reflectors, in an environment with known wall geometry. A similar model with random subreflectors was evaluated in [8], where the estimated diffuse paths were used for positioning and mapping, but using a simple geometric approach. However, these methods do not solve the 5G SLAM problem over time or provide uncertainty information.…”

“…The proposed scheme can estimate the vehicle location, orientation and clock bias as well as the locations and roughness of landmarks in the environment. The main contributions of this paper are summarized as follows: (i) We extend the filtering approach from [11] by utilizing a more powerful Poisson multi-Bernoulli mixture (PMBM) filter, and by considering a likelihood with multiple channel parameter estimates per surface; (ii) We derive a novel likelihood function for channel estimation from [8] of the cluster of paths from rough surfaces, for different types of surfaces with different roughness.…”