SISO RIS-Enabled Joint 3D Downlink Localization and Synchronization

We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cram\ue9r-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angle-of-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RISUE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeter-level positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with single-antenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements

Dirichlet process approach for radio-based simultaneous localization and mapping

Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming highly resolvable, enabling accurate vehicle localization and mapping. We propose a novel method of radio simultaneous localization and mapping (SLAM) with the Dirichlet process (DP). The DP, which can estimate the number of clusters as well as clustering, is capable of identifying the locations of reflectors by classifying signals when such 5G signals are reflected and received from various objects. We generate birth points using the measurements from 5G mmWave signals received by the vehicle and classify objects by clustering birth points generated over time. Each time we use the DP clustering method, we can map landmarks in the environment in challenging situations where false alarms exist in the measurements and change the cardinality of received signals. Simulation results demonstrate the performance of the proposed scheme. By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity

5G Synchronization, Positioning, and Mapping from Diffuse Multipath

5G mmWave communication systems have the potential to jointly estimate the positions of user equipment (UE) and mapping their propagation environments using a single base station. But such potential depends on the characteristics of the reflecting surfaces, such as a deterministic specular nature, a stochastic diffuse/scattering nature, or a combination of both. In this letter, we proposed a 5G positioning and mapping algorithm with unknown orientation and clock bias for single-bounce diffuse multipath channel models. The method is able to accurately localize, calibrate and synchronize the UE, even in the absence of line-of-sight and specular components. This enables robust positioning and mapping using only diffuse multipath

Mixture Density Networks for Multipath Assisted Positioning-based Fingerprinting

In multipath assisted positioning schemes, the spatial information contained in multipath propagation of wireless radio systems is exploited for localization of a receiver. However, such schemes suffer from a high computational complexity. We have proposed before a fingerprinting localization system based on multipath assisted positioning, where the fingerprinting database is encoded in a deep neural network (DNN). Within this paper, we propose and evaluate a mixture density network approach in our DNN to analyze ambiguities among fingerprints at different locations. We show that our scheme shows a very good positioning performance with an error of around 2m for the most part, while having a low computational complexity in the online stage and a very low effort compared to traditional fingerprinting schemes

User Tracking with Multipath Assisted Positioning-based Fingerprinting and Deep Learning

Multipath assisted positioning schemes allow localizing a user with only a single physical transmitter by treating multipath components (MPCs) as line-of-sight signals from virtual transmitters. The user position and the locations of the physical and virtual transmitters can be estimated jointly with simultaneous localization and mapping (SLAM). While such approaches often show very good positioning performance, they come at the cost of a high computational complexity. To reduce this complexity, multipath assisted positioning schemes based on SLAM may be combined with fingerprinting, where the fingerprints are features of the wireless radio channel. Within this paper, we present such an approach, where a deep neural network (DNN) is trained on data from a multipath assisted positioning scheme to predict the user position and the corresponding uncertainty from channel information. Based on the DNN, a Kalman filter can accurately and efficiently track the user position. We show by simulations that the positioning performance is improved by a factor of 1.5 while the computational complexity is crucially lower than that of multipath assisted positioning-based SLAM

Entropy of Transmitter Maps in Cooperative Multipath Assisted Positioning

In multipath assisted positioning, multipath components (MPCs) are regarded as line-of-sight (LoS) signals from virtual transmitters. The locations of physical and virtual transmitters are typically unknown, but can be estimated jointly with the location of a mobile terminal using simultaneous localization and mapping (SLAM). When users cooperate by exchanging maps of estimated positions of physical and virtual transmitters, the positioning performance can be improved drastically. Within this paper, we investigate such transmitter maps that are shared among users. We derive an approximation of the entropy of transmitter maps that is based on the unscented transform and analyze the evolution of this entropy over time. Our simulations indicate that the transmitter maps converge quickly

mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

Future cellular networks that utilize millimeter wave signals provide new opportunities in positioning and situational awareness. Large bandwidths combined with large antenna arrays provide unparalleled delay and angle resolution, allowing high accuracy localization but also building up a map of the environment. Even the most basic filter intended for simultaneous localization and mapping exhibits high computational overhead since the methods rely on sigma point or particle-based approximations. In this paper, a first order Taylor series based Gaussian approximation of the filtering distribution is used and it is demonstrated that the developed extended Kalman probability hypothesis density filter is computationally very efficient. In addition, the results imply that efficiency does not come with the expense of estimation accuracy since the method nearly achieves the position error bound.submittedVersionPeer reviewe

Opportunistic channel estimation with LTE signals of limited bandwidth for positioning applications

The positioning problem is interesting in a variety of applications, especially in indoor environments or in urban canyons, where the position information obtainable with traditional Global Navigation Satellite Systems is limited. In this paper, we deal with the problem of estimating, for the purposes of positioning, the time of arrival (TOA) and the angle of arrival (AOA) by processing LTE 3GPP signals, with particular attention to the uplink signals. The main contribution of this paper is the definition of new opportunistic methods to estimate the TOA and the AOA using the upstream demodulation reference signal (DM-RS) instead of the Sounding Reference Signal. We will show that the use of DM-RS and of estimation algorithms such as the Space-Alternating Generalized Expectation-Maximization and the Iterative Adaptive Approach for Amplitude and Phase estimation (IAA-APES) allows an efficient estimate of the parameters, in spite of the small, occupied bandwidth