5G Positioning and Mapping with Diffuse Multipath

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 breakdown or lead to significant biases. We present a novel tensor-based 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

Impact of Rough Surface Scattering on Stochastic Multipath Component Models

Multipath-assisted positioning makes use of specular multipath components (MPCs), whose parameters are geometrically related to the positions of the transceiver nodes. Diffuse scattering from rough surfaces affects the observed specular reflections in the angular and delay domains. Based on the effective roughness approach, the angular delay power spectrum can be calculated as a function of location parameters, which-in a next step-could be useful to accurately characterize the position-related information of MPCs. The calculated power spectra follow reported characteristics of stochastic multipath models, i.e. Gaussian shape in the angular domain and an exponential shape in the delay domain. The resulting angular and delay spreads are in an equivalent range to values reported in literature

Tensor Decomposition Based Beamspace ESPRIT for Millimeter Wave MIMO Channel Estimation

We propose a search-free beamspace tensor-ESPRIT algorithm for millimeter wave MIMO channel estimation. It is a multidimensional generalization of beamspace-ESPRIT method by exploiting the multiple invariance structure of the measurements. Geometry-based channel model is considered to contain the channel sparsity feature. In our framework, an alternating least squares problem is solved for low rank tensor decomposition and the multidimensional parameters are automatically associated. The performance of the proposed algorithm is evaluated by considering different transformation schemes

Anchorless cooperative tracking using multipath channel information

Highly accurate location information is a key facilitator to stimulate future services for the commercial and public sectors. Positioning and tracking of absolute positions of wireless nodes usually requires information provided from technical infrastructure, e.g., satellites or fixed anchor nodes, whose maintenance is costly and whose limited operating coverage narrows the positioning service. In this paper, we present an algorithm aimed at the tracking of absolute positions without using information from fixed anchors, odometers, or inertial measurement units. We perform radio channel measurements, in order to exploit position-related information contained in multipath components (MPCs). Tracking of the absolute node positions is enabled by the estimation of MPC parameters followed by the association of these parameters to a floorplan. To account for uncertainties in the floorplan and for propagation effects like diffraction and penetration, we recursively update the provided floorplan using the measured MPC parameters. We demonstrate the ability to localize two agent nodes without the employment of further infrastructure, using data from ultra-wideband channel measurements. Furthermore, we show the potential performance gain if also one fixed anchor is available, and we validate the algorithm for a range of different signal bandwidths and a number of nodes