Exploiting the spatio-temporal channel properties of multiple antenna systems

The spatio-temporal channel properties of multiple antenna systems are exploited to obtain new approaches to localization and channel prediction. It is shown that a mobile station can be localized in multipath environments under the explicit consideration of scatterers. Thus, unlike conventional localization systems, the scatterers are used as an aid in localization. Moreover, it is shown that channel prediction in multiple antenna systems can be performed using linear prediction filters. This result is used to propose optimal and computationally inexpensive suboptimal channel predictors

Millimeter-wave Mobile Sensing and Environment Mapping: Models, Algorithms and Validation

Integrating efficient connectivity, positioning and sensing functionalities into 5G New Radio (NR) and beyond mobile cellular systems is one timely research paradigm, especially at mm-wave and sub-THz bands. In this article, we address the radio-based sensing and environment mapping prospect with specific emphasis on the user equipment (UE) side. We first describe an efficient l1-regularized least-squares (LS) approach to obtain sparse range--angle charts at individual measurement or sensing locations. For the subsequent environment mapping, we then introduce a novel state model for mapping diffuse and specular scattering, which allows efficient tracking of individual scatterers over time using interacting multiple model (IMM) extended Kalman filter and smoother. We provide extensive numerical indoor mapping results at the 28~GHz band deploying OFDM-based 5G NR uplink waveform with 400~MHz channel bandwidth, covering both accurate ray-tracing based as well as actual RF measurement results. The results illustrate the superiority of the dynamic tracking-based solutions, compared to static reference methods, while overall demonstrate the excellent prospects of radio-based mobile environment sensing and mapping in future mm-wave networks

Measurement-based Geometrical Characterization of the Vehicle-to-Vulnerable-Road-User Communication Channel

Vehicle-to-vulnerable road user (V2VRU) communications have the ability to provide 360 degrees of awareness to both vehicles and vulnerable road users (VRUs) to prevent accidents. An accurate V2VRU channel model in critical accident scenarios is essential to develop a reliable communications system. Therefore, extensive wideband single-input and single-output (SISO) channel measurement campaigns at 5.2 GHz were carried out in open-field and urban environments. Accident prone scenarios between a vehicle and a cyclist as well as between a vehicle and a pedestrian are considered. In this paper, locations of the scatterers in the propagation environment are estimated. We propose a method to extract specular MPCs from the estimated time variant channel impulse response (CIR) based on the density of neighboring MPCs. The specular MPCs are then tracked using a novel tracking algorithm based on the multipath component distance (MCD) approach. Each path is then related to a physical scatterer in the propagation environment by employing a joint delay-Doppler estimation. According to the results, single and double bounce reflections from buildings and parked vehicles are identified in line-of-sight (LoS) situation. In non-LoS (NLoS) situation, scattering from nearby trees as well as reflections from traffic signs and lampposts beneath the trees canopy are identified

Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and therefore also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40 MHz. The results show that high accuracy localization is possible even with such a low bandwidth.Comment: 14 pages (two columns), 13 figures. This work has been submitted to the IEEE Transaction on Wireless Communications for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Wiometrics: Comparative Performance of Artificial Neural Networks for Wireless Navigation

Radio signals are used broadly as navigation aids, and current and future terrestrial wireless communication systems have properties that make their dual-use for this purpose attractive. Sub-6 GHz carrier frequencies enable widespread coverage for data communication and navigation, but typically offer smaller bandwidths and limited resolution for precise estimation of geometries, particularly in environments where propagation channels are diffuse in time and/or space. Non-parametric methods have been employed with some success for such scenarios both commercially and in literature, but often with an emphasis on low-cost hardware and simple models of propagation, or with simulations that do not fully capture hardware impairments and complex propagation mechanisms. In this article, we make opportunistic observations of downlink signals transmitted by commercial cellular networks by using a software-defined radio and massive antenna array mounted on a passenger vehicle in an urban non line-of-sight scenario, together with a ground truth reference for vehicle pose. With these observations as inputs, we employ artificial neural networks to generate estimates of vehicle location and heading for various artificial neural network architectures and different representations of the input observation data, which we call wiometrics, and compare the performance for navigation. Position accuracy on the order of a few meters, and heading accuracy of a few degrees, are achieved for the best-performing combinations of networks and wiometrics. Based on the results of the experiments we draw conclusions regarding possible future directions for wireless navigation using statistical methods

Geometry-based Radio Channel Characterization and Modeling: Parameterization, Implementation and Validation

The propagation channel determines the fundamental basis of wireless communications, as well as the actual performance of practical systems. Therefore, having good channel models is a prerequisite for developing the next generation wireless systems. This thesis first investigates one of the main channel model building blocks, namely clusters. To understand the concept of clusters and channel characterization precisely, a measurement based ray launching tool has been implemented (Paper I). Clusters and their physical interpretation are studied by using the implemented ray launching tool (Paper II). Also, this thesis studies the COST 2100 channel model, which is a geometry-based channel model using the concept of clusters. A complete parameter set for the outdoor sub-urban scenario is extracted and validated for the COST 2100 channel model (Paper III). This thesis offers valuable insights on multi-link channel modeling, where it will be widely used in the next generation wireless systems (Paper IV and Paper V). In addition, positioning and localization by using the phase information of multi-path components, which are estimated and tracked from the radio channels, are investigated in this thesis (Paper VI). Clusters are extensively used in geometry-based stochastic channel models, such as the COST 2100 and WINNER II channel models. In order to gain a better understanding of the properties of clusters, thus the characteristics of wireless channels, a measurement based ray launching tool has been implemented for outdoor scenarios in Paper I. With this ray launching tool, we visualize the most likely propagation paths together with the measured channel and a detail floor plan of the measured environment. The measurement based ray launching tool offers valuable insights of the interacting physical scatterers of the propagation paths and provides a good interpretation of propagation paths. It shows significant advantages for further channel analysis and modeling, e.g., multi-link channel modeling. \par The properties of clusters depend on how clusters are identified. Generally speaking, there are two kinds of clusters: parameter based clusters are characterized with the parameters of the associated multi-path components; physical clusters are determined based on the interacting physical scatterers of the multi-path components. It is still an open issue on how the physical clusters behave compared to the parameter based clusters and therefore we analyze this in more detail in Paper II. In addition, based on the concept of physical clusters, we extract modeling parameters for the COST 2100 channel model with sub-urban and urban micro-cell measurements. Further, we validate these parameters with the current COST 2100 channel model MATLAB implementation. The COST 2100 channel model is one of the best candidates for the next generation wireless systems. Researchers have made efforts to extract the parameters in an indoor scenario, but the parameterization of outdoor scenarios is missing. Paper III fills this blank, where, first, cluster parameters and cluster time-variant properties are obtained from the 300~MHz measurements by using a joint clustering and tracking algorithm. Parameterization of the COST 2100 channel model for single-link outdoor MIMO communication at 300~MHz is conducted in Paper III. In addition, validation of the channel model is performed for the considered scenario by comparing simulated and measured delay spreads, spatial correlations, singular value distributions and antenna correlations. Channel modeling for multi-link MIMO systems plays an important role for the developing of the next generation wireless systems. In general, it is essential to capture the correlations between multi-link as well as their correlation statistics. In Paper IV, correlation between large-scale parameters for a macro cell scenario at 2.6 GHz has been analyzed. It has been found that the parameters of different links can be correlated even if the base stations are far away from each other. When both base stations were in the same direction compared to the movement, the large-scale parameters of the different links had a tendency to be positively correlated, but slightly negatively correlated when the base stations were located in different directions compared to the movement of the mobile terminal. Paper IV focuses more on multi-site investigations, and paper V gives valuable insights for multi-user scenarios. In the COST 2100 channel model, common clusters are proposed for multi-link channel modeling. Therefore, shared scatterers among the different links are investigated in paper V, which reflects the physical existence of common clusters. We observe that, as the MS separation distance is increasing, the number of common clusters is decreasing and the cross-correlation between multiple links is decreasing as well. Multi-link MIMO simulations are also performed using the COST 2100 channel model and the parameters of the extracted common clusters are detailed in paper V. It has been demonstrated that the common clusters can represent multi-link properties well with respect to inter-link correlation and sum rate capacity. Positioning has attracted a lot of attention both in the industry and academia during the past decades. In Paper VI, positioning with accuracy down to centimeters has been demonstrated, where the phase information of multi-path components from the measured channels is used. First of all, an extended Kalman filter is implemented to process the channel data, and the phases of a number of MPCs are tracked. The tracked phases are converted into relative distance measures. Position estimates are obtained with a method based on so called structure-of-motion. In Paper VI, circular movements have been successfully tracked with a root-mean-square error around 4 cm when using a bandwidth of 40 MHz. It has been demonstrated that phase based positioning is a promising technique for positioning with accuracy down to centimeters when using a standard cellular bandwidth. In summary, this thesis has made efforts for the implementation of the COST 2100 channel model, including providing model parameters and validating such parameters, investigating multi-link channel properties, and suggesting implementations of the channel model. The thesis also has made contributions to the tools and algorithms that can be used for general channel characterizations, i.e., clustering algorithm, ray launching tool, EKF algorithm. In addition, this thesis work is the first to propose a practical positioning method by utilizing the distance estimated from the phases of the tracked multi-path components and showed a preliminary and promising result

Multi-antenna non-line-of-sight identification techniques for target localization in mobile ad-hoc networks

Target localization has a wide range of military and civilian applications in wireless mobile networks. Examples include battle-field surveillance, emergency 911 (E911), traffc alert, habitat monitoring, resource allocation, routing, and disaster mitigation. Basic localization techniques include time-of-arrival (TOA), direction-of-arrival (DOA) and received-signal strength (RSS) estimation. Techniques that are proposed based on TOA and DOA are very sensitive to the availability of Line-of-sight (LOS) which is the direct path between the transmitter and the receiver. If LOS is not available, TOA and DOA estimation errors create a large localization error. In order to reduce NLOS localization error, NLOS identifcation, mitigation, and localization techniques have been proposed. This research investigates NLOS identifcation for multiple antennas radio systems. The techniques proposed in the literature mainly use one antenna element to enable NLOS identifcation. When a single antenna is utilized, limited features of the wireless channel can be exploited to identify NLOS situations. However, in DOA-based wireless localization systems, multiple antenna elements are available. In addition, multiple antenna technology has been adopted in many widely used wireless systems such as wireless LAN 802.11n and WiMAX 802.16e which are good candidates for localization based services. In this work, the potential of spatial channel information for high performance NLOS identifcation is investigated. Considering narrowband multiple antenna wireless systems, two xvNLOS identifcation techniques are proposed. Here, the implementation of spatial correlation of channel coeffcients across antenna elements as a metric for NLOS identifcation is proposed. In order to obtain the spatial correlation, a new multi-input multi-output (MIMO) channel model based on rough surface theory is proposed. This model can be used to compute the spatial correlation between the antenna pair separated by any distance. In addition, a new NLOS identifcation technique that exploits the statistics of phase difference across two antenna elements is proposed. This technique assumes the phases received across two antenna elements are uncorrelated. This assumption is validated based on the well-known circular and elliptic scattering models. Next, it is proved that the channel Rician K-factor is a function of the phase difference variance. Exploiting Rician K-factor, techniques to identify NLOS scenarios are proposed. Considering wideband multiple antenna wireless systems which use MIMO-orthogonal frequency division multiplexing (OFDM) signaling, space-time-frequency channel correlation is exploited to attain NLOS identifcation in time-varying, frequency-selective and spaceselective radio channels. Novel NLOS identi?cation measures based on space, time and frequency channel correlation are proposed and their performances are evaluated. These measures represent a better NLOS identifcation performance compared to those that only use space, time or frequency