13 research outputs found
Novel Algorithms for High-Accuracy Joint Position and Orientation Estimation in 5G mmWave Systems"
We propose a method for accurate estimation of the User Equipment (UE) position and antenna orientation. For this, we exploit the sparsity of the mm-wave channel, and employ a compressive sensing approach with iterative refinement steps for accurate estimation of the channel parameters, including the departure and arrival angles as well as the time-of-arrival for each observed propagation path. Based on the estimated channel parameters, we formulate an iterative Gibbs sampler to obtain statistical descriptions for the unknown UE position and orientation along with the unknown scatterer positions, even in the absence of a Line-Of-Sight path
Joint Localization and Mapping through Millimeter Wave MIMO in 5G Systems
Millimeter wave signals with multiple transmit and receive antennas are considered as enabling technology for enhanced mobile broadband services in 5G systems. While this combination is mainly associated with achieving high data rates, it also offers huge potential for radio-based positioning. Recent studies showed that millimeter wave signals with multiple transmit and receive antennas are capable of jointly estimating the position and orientation of a mobile terminal while mapping the radio environment simultaneously. To this end, we present a message passing-based estimator which jointly estimates the position and orientation of the mobile terminal, as well as the location of reflectors or scatterers in the absence of the line-of-sight path. We provide numerical examples showing that our estimator can provide considerably higher estimation accuracy compared to a state-of-the-art estimator. Our examples demonstrate that our message passing-based estimator neither requires the presence of a line-of-sight path nor prior knowledge regarding any of the parameters to be estimated
Joint Localization and Mapping through Millimeter Wave MIMO in 5G Systems - Extended Version
Millimeter wave signals with multiple transmit and receive antennas are
considered as enabling technology for enhanced mobile broadband services in 5G
systems. While this combination is mainly associated with achieving high data
rates, it also offers huge potential for radio-based positioning. Recent
studies showed that millimeter wave systems with multiple transmit and receive
antennas are capable of jointly estimating the position and orientation of a
mobile terminal while mapping the radio environment simultaneously. To this
end, we present a message passing-based estimator which jointly estimates the
position and orientation of the mobile terminal, as well as the location of
reflectors or scatterers. We provide numerical examples showing that this
estimator can provide considerably higher estimation accuracy compared to a
state-of-the-art estimator. Our examples demonstrate that our message
passing-based estimator neither requires the presence of a line-of-sight path
nor prior knowledge regarding any of the parameters to be estimated
A Complexity-Efficient High Resolution Propagation Parameter Estimation Algorithm for Ultra-Wideband Large-Scale Uniform Circular Array
Millimeter wave (mm-wave) communication with large-scale antenna array
configuration is seen as the key enabler of the next generation communication
systems. Accurate knowledge of the mm-wave propagation channels is fundamental
and essential. In this contribution, a novel complexity-efficient high
resolution parameter estimation (HRPE) algorithm is proposed for the mm-wave
channel with large-scale uniform circular array (UCA) applied. The proposed
algorithm is able to obtain the high-resolution estimation results of the
spherical channel propagation parameters. The prior channel information in the
delay domain, i.e., the delay trajectories of individual propagation paths
observed across the array elements, is exploited, by combining the
high-resolution estimation principle and the phase mode excitation technique.
Fast initializations, effective interference cancellations and reduced
searching spaces achieved by the proposed schemes significantly decrease the
algorithm complexity. Furthermore, the channel spatial non-stationarity in path
gain across the array elements is considered for the first time in the
literature for propagation parameter estimation, which is beneficial to obtain
more realistic results as well as to decrease the complexity. A mm-wave
measurement campaign at the frequency band of 28-30 GHz using a large-scale UCA
is exploited to demonstrate and validate the proposed HRPE algorithm.Comment: Single column, 28 pages. In review process with IEEE Transactions on
Communication
Downlink Single-Snapshot Localization and Mapping with a Single-Antenna Receiver
5G mmWave MIMO systems enable accurate estimation of the user position and
mapping of the radio environment using a single snapshot when both the base
station (BS) and user are equipped with large antenna arrays. However, massive
arrays are initially expected only at the BS side, likely leaving users with
one or very few antennas. In this paper, we propose a novel method for
single-snapshot localization and mapping in the more challenging case of a user
equipped with a single-antenna receiver. The joint maximum likelihood (ML)
estimation problem is formulated and its solution formally derived. To avoid
the burden of a full-dimensional search over the space of the unknown
parameters, we present a novel practical approach that exploits the sparsity of
mmWave channels to compute an approximate joint ML estimate. A thorough
analysis, including the derivation of the Cram\'er-Rao lower bounds, reveals
that accurate localization and mapping can be achieved also in a MISO setup
even when the direct line-of-sight path between the BS and the user is severely
attenuated
A survey on 5G massive MIMO Localization
Massive antenna arrays can be used to meet the requirements of 5G, by exploiting different spatial signatures of users. This same property can also be harnessed to determine the locations of those users. In order to perform massive MIMO localization, refined channel estimation routines and localization methods have been developed. This paper provides a brief overview of this emerging field
Short-Term Vehicle Traffic Prediction for Terahertz Line-of-Sight Estimation and Optimization in Small Cells
Significant efforts have been made and are still being made on short-term traffic prediction methods, specially for highway traffic based on punctual measurements. Literature on predicting the spatial distribution of the traffic in urban intersections is, however, very limited. This work presents a novel data-driven prediction algorithm based on Random Forests regression over spatio-temporal aggregated data of vehicle counts inside a grid. The proposed approach aims to estimate future distribution of V2X traffic demand, providing a valuable input for a dynamic management of radio resources in small cells. Radio Access Networks (RAN) working in the terahertz band and deployed in small cells are expected to meet the high-demanding data rate requirements of connected vehicles. However, terahertz frequency propagation has important limitations in outdoor scenarios, including distance propagation, high absorption coefficients values and low reflection properties. More concretely, in settings such as complex road intersections, dynamic signal blockage and shadowing effects may cause significant power losses and compromise the quality of service for some vehicles. The forthcoming network demand, estimated from the regression algorithm is used to compute the losses expected due to other vehicles potentially located between the transmitter and the receiver. We conclude that our approach, which is designed from a grid-like perspective, outperforms other traffic prediction methods and the combined result of these predictions with a dynamic reflector orientation algorithm, as a use case application, allows reducing the ratio of vehicles that do not receive a minimum signal power
Collaborative Sensor Network Localization: Algorithms and Practical Issues
Emerging communication network applications including fifth-generation (5G) cellular and the Internet-of-Things (IoT) will almost certainly require location information at as many network nodes as possible. Given the energy requirements and lack of indoor coverage of Global Positioning System (GPS), collaborative localization appears to be a powerful tool for such networks. In this paper, we survey the state of the art in collaborative localization with an eye toward 5G cellular and IoT applications. In particular, we discuss theoretical limits, algorithms, and practical challenges associated with collaborative localization based on range-based as well as range-angle-based techniques