LTE NLOS Navigation and Channel Characterization

Navigation with terrestrial wireless infrastructure is appealing to overcome geometrical limitations of satellite navigation for users in environments with limited sky views. However, terrestrial signals are also prone to multipath that can result in angular and range estimates that are not representative of actual transmitter-receiver geometry. In this paper, some of these propagation effects are quantified for a particular urban non line-of-sight (NLOS) scenario, based on measurements of downlink reference symbols transmitted by a commercial Long Term Evolution (LTE) base station (eNodeB) and received by a massive antenna array mounted on a passenger vehicle. Empirical results indicate that large-scale statistics for a user making multiple passes through the same urban environment look similar when represented in terms of angles and delays despite changes in orientation and drive direction. Additionally, it is demonstrated that multipath effects can be utilized advantageously; it is possible to estimate not only user position but also orientation through wireless fingerprinting

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

Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information

Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle’s horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters

Extended FastSLAM Using Cellular Multipath Component Delays and Angular Information

Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle's horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters

Flexible Density-based Multipath Component Clustering Utilizing Ground Truth Pose

Accurate statistical characterization of electromagnetic propagation is necessary for the design and deployment of radio systems. State-of-the-art channel models such as the Enhanced COST 2100 Channel Model utilize the concept of clusters of multipath components, and characterize channels by their inter- and intra-cluster statistics. Automatic clustering algorithms have been proposed in literature, but the subjective nature of the problem precludes any from being deemed objectively correct. In this paper, a new algorithm is proposed, based on density-reachability and ground truth receiver pose, with the explicit focus of extracting clusters for the purpose of channel characterization. Measurements of downlink signals from a commercial LTE base station by a passenger vehicle driving in an urban environment with a massive antenna array on the roof are used to evaluate the repeatability and intuitiveness of the proposed clustering algorithm

High-Resolution Channel Sounding and Parameter Estimation in Multi-Site Cellular Networks

Understanding of electromagnetic propagation properties in real environments is necessary for efficient design and deployment of cellular systems. In this paper, we show a method to estimate high-resolution channel parameters with a massive antenna array in real network deployments. An antenna array mounted on a vehicle is used to receive downlink long-term evolution (LTE) reference signals from neighboring base stations (BS) with mutual interference. Delay and angular information of multipath components is estimated with a novel inter-cell interference cancellation algorithm and an extension of the RIMAX algorithm. The estimated high-resolution channel parameters are consistent with the movement pattern of the vehicle and the geometry of the environment and allow for refined channel modeling and precise cellular positioning

Urban Navigation with LTE using a Large Antenna Array and Machine Learning

Channel fingerprinting entails associating a point in space with measured properties of a received wireless signal. If the propagation environment for that point in space remains reasonably static with time, then a receiver with no knowledge of its own position experiencing a similar channel in the future might reasonably infer proximity to the original surveyed point. In this article, measurements of downlink LTE Common Reference Symbols from one sector of an eNodeB are used to generate channel fingerprints for a passenger vehicle driving through a dense urban environment without line-of-sight to the transmitter. Channel estimates in the global azimuthal-delay domain are used to create a navigation solution with meter-level accuracy around a city block

Bayesian Integrity Monitoring for Cellular Positioning - A Simplified Case Study

Bayesian receiver autonomous integrity monitoring (RAIM) algorithms are developed for the snapshot cellular positioning problem in a simplified one-dimensional (1D) linear Gaussian setting. They allow for position estimation, multi-fault detection and exclusion, and protection level (PL) computation by the efficient and exact computation of the position posterior probabilities via message passing along a factor graph. Numerical simulations show that the proposed Bayesian RAIM algorithms achieve significant performance improvement over a baseline advanced RAIM algorithm by providing tighter protection levels (PLs) that meet the target integrity risk (TIR) requirements

A model for power contributions from diffraction around a truck in Vehicle-to-Vehicle communications

Channel modeling studies in vehicle-to-vehicle (V2V) communications have shown that larger road users act as blocking objects for the communication of surrounding vehicles, dramatically altering the statistical properties of the wireless channel. Without a strong line-of-sight component, packet delivery depends on other propagation mechanisms that are statistically more likely to add destructively so that the signal falls below the noise threshold of the receiver. An analytical model to understand dominant propagation mechanisms in these scenarios is a very powerful tool to predict system performance for important safety scenarios, facilitating both application development and vehicle antenna design. In this paper we present an analytical model for the power contributions from diffraction around a truck. The model is later verified using real life channel measurements from a rural road scenario. A general conclusion is that the channel between the vehicles is highly sensitive to lateral position, and even longitudinal position in the vicinity immediately behind the truck. Finally we conclude that non line-of-sight communication in rural environments is possible using only diffraction as a propagation mechanism, and that antenna diversity significantly increases communication reliability

Impact of a Truck as an Obstacle on Vehicle-to-Vehicle Communications in Rural and Highway Scenarios

Shadowing from other vehicles can degrade the performance of vehicle-to-vehicle communication systems significantly. It is thus important to characterize and model the influence of common shadowing objects like trucks in a proper way. However, the scenario of a truck as an obstacle in highly dynamic rural and highway environments is not yet well understood. In this paper we analyze the distance dependent path loss and the additional shadowing loss due to a truck through dynamic measurements. We further characterize the large scale fading and the delay and Doppler spreads as a measure of the channel dispersion in the time and frequency domains. It has been found that a truck as an obstacle reduces the received power by 12 and 13 dB on average, for roof antenna, in rural and highway scenarios, respectively. Also, the dispersion in time and frequency domains is highly increased when the line-of-sight is obstructed by the truck