Accurate and reliable positioning is a cornerstone of pervasive computing. However, GNSS is not always available for pervasive services while cellular-network-based alternatives have proven unsuccessful, due to coarse position accuracy or complex network setup. In this work, we investigate 5G singlecell positioning by leveraging Timing Advance and Angle of Arrival measurements, two key indicators already used in 5G NR for communication. Our approach does not require tight network synchronization among base stations, unlike other 5G positioning techniques. We present the first real-world study of single-cell positioning in a 5G network with a commercial gNB and an off-the-shelf smartphone moving up to 461 meters away from the gNB across four urban trajectories. Our analysis reveals two key challenges: coarse angular and timing resolution, and severe angle and range errors under Non-Line-of-Sight (NLOS) conditions. To overcome these challenges, we present a framework that (i) synthesizes higher-resolution Angle-of-Arrival estimates from real 5G beam patterns, (ii) classifies LOS/NLOS conditions with a Convolutional Neural Network trained on beam signal-strength heatmaps, and (iii) refines angle and ranging using multipath-aware corrections. Our system reduces the median positioning error from $86.8 m$ to $16.5 m$ with a single gNB, without the aid of external sensors.

The Third Generation Partnership Project (3GPP) has introduced support for reduced capability (RedCap) devices in its fifth generation of wireless cellular technology (5G) for internet of things (IoT) use cases such as industrial sensor networks, wearables, and extended reality. These devices support small radio frequency (RF) bandwidth limiting their capability to achieve high positioning accuracy. This article describes the recently completed 3GPP Release 18 features, extending the effective signal bandwidth of RedCap devices by frequency hopping to enable high accuracy positioning. This article also presents simulation results based on the 3GPP channel models for indoor environments that show that RedCap devices supporting the Release 18 enhancements outperform Release 17 RedCap devices and deliver the same high positioning accuracy available to Release 16 devices with large RF bandwidth.

Positioning measurements that are performed in cellular networks under non line-of-sight (NLOS) conditions can degrade the positioning performance heavily. In this paper we show that the problem can be mitigated by robust positioning methods based on the RANSAC algorithm. Starting with a basic implementation of RANSAC for the TDOA positioning problem, we propose several enhancements based on probabilistic analysis. The methods perform particularly well in dense network deployments with multiple transmission/reception points (TRP), even when the fraction of LOS measurements is low. In simulations on standardized 5G test-scenarios for industrial internet-of-things, the best algorithm achieves an accuracy on par with what is achieved when LOS/NLOS conditions are known by the estimator.

This paper investigates the impact of addition/removal/reweighting of edges in a complex networked linear control system. For networks of positive edge weights, we show that when adding edges leads to the creation of new cycles, these in turn may lead to instabilities. Dynamically, these cycles correspond to positive feedback loops. Conditions are provided under which the modified network is guaranteed to be stable. These conditions are related to the steady state value of the transfer function matrix of the newly created positive feedbacks. The tools we develop in the paper can be used to investigate the fragility of a network, i.e., its robustness to structured perturbations.

Positioning with high precision and reliability can be provided by 5G cellular networks in environments where satellite positioning is not available or reliable. The accuracy that can be achieved by classical methods like triangulation and trilateration however degrades significantly under non line of sight (NLOS) conditions. The problem can be mitigated with increasingly dense deployments of network transmission and reception points (TRPs), but that is both impractical and costly. As an alternative, this study investigates if multipath propagation of radio signals can be exploited to improve positioning accuracy and reduce the necessary deployment density. With 3GPP Rel. 17 new signaling support has been introduced to report the propagation delay, corresponding to the length, of multiple paths between the user equipment (UE) and a network TRP. The length of a multipath can, in combination with a partially known map of the environment, give additional information about the UE position. In this study we develop multipath-assisted tracking algorithms and evaluate their performances in realistic simulations using 3GPP standardized positioning reference signals and measurements in an indoor factory environment. Our evaluations show that multipath-assisted algorithms can achieve an accuracy below 0.9 m in 90% of the cases, which is more than tenfold better than a conventional LOS based algorithm. Moreover, one algorithm variant also shows an ability to track a UE using very few TRPs.

This work presents the preliminary simulation and experimental results of a first-of-a-kind testbed for GNSS, 5G networks and sensor positioning, called Hybrid Overlay Positioning with 5G and GNSS (HOP-5G) testbed. This is a proof-of-concept testbed based on the deployment of dedicated ground and aerial 5G base stations (BSs) for enhanced hybrid positioning together with GNSS and sensor technologies. The main entities of the overall testbed are described and preliminary test results are provided. First, the achievable positioning performance is assessed through simulations, showing the importance of the hybrid fusion of GNSS and 5G in critical and harsh environments, especially under 5G network synchronization impairments. Then, the possible gain in integrity performance is examined by fusing real GNSS and simulated 5G observables. Finally, extensive experimental laboratory results are presented to assess the impact of base station (BS) synchronization, by using COTS GNSS and software-defined radio (SDR) equipment. The outcome results suggests the importance of a monitoring unit within the HOP-5G testbed.

Dedicated and aerial fifth generation (5G) networks, here called 5G overlay networks, are envisaged to enhance existing positioning services, when combined with global navigation satellite systems (GNSS) and other sensors. There is a need for accurate and timely positioning in safety-critical automotive and aerial applications, such as advanced warning systems or in urban air mobility (UAM). Today, these high-accuracy demands can partially be satisfied by GNSS, though not in dense urban conditions or under GNSS threats (e.g. interference, jamming or spoofing). Temporary and on-demand 5G network deployments using ground and flying base stations (BSs) are indeed a novel solution to exploit hybrid GNSS, 5G and sensor algorithms for the provision of accurate three-dimensional (3D) position and motion information, especially for challenging urban and suburban scenarios. Thus, this paper first analyzes the positioning technologies available, including signals, positioning methods, algorithms and architectures. Then, design considerations of 5G overlay networks are discussed, by including simulation results on the 5G signal bandwidth, antenna array and network deployment.

The authors describe the recent 3GPP Release 16 specification for positioning in 5G networks. Release 1 6 specifies positioning signals, measurements, procedures, and architecture to meet requirements in regulatory, commercial, and industrial use cases, extending the positioning capability of the 3GPP standard compared to what was possible with LTE. The indicative positioning performance is evaluated in agreed representative 3GPP simulation scenarios, showing a 90 percentile accuracy of a few meters down to a few decimeters depending on scenarios and assumptions.

Combinations of Gramian-based centrality measures are used for driver node selection in complex networks in order to simultaneously take into account conflicting control energy requirements, like minimizing the average energy needed to steer the state in any direction and the energy needed for the worst direction. The selection strategies that we propose are based on a characterization of the network non-normality. We show that the concept is also related to the idea of balanced realization.

Indoor positioning is currently recognized as one of the important features in emergency, commercial and industrial applications. The 5G network enhances mobility, flexibility, reliability, and security to new higher levels which greatly benefit the IoT and industrial applications. Industrial IoT (IIoT) use-cases are characterized by ambitious system requirements for positioning accuracy in many verticals. For example, on the factory floor, it is important to locate assets and moving objects such as forklifts. The deployment design for different IIoT environments has a significant impact on the positioning per-performance in terms of both accuracy and availability of the service. Indoor factory (InF) and indoor open office (IOO) are two available and standardized Third Generation Partnership Project (3GPP) scenarios for evaluation of indoor channel models and positioning performance in IIoT use cases. This paper aims to evaluate the positioning performance in terms of accuracy and availability while considering different deployment strategies. Our simulation-based evaluation shows that deployment plays a vital role when it comes to achieving high accuracy positioning performance. It is for example favorable to deploy the 5G Transmission and Reception Points (TRPs) on the walls of the factory halls than deploying them attached to the ceiling.

This paper investigates the impact of addition/removal of edges in complex networks. Growing a network by the addition of edges has for instance been suggested as a way to improve network robustness to external disturbances. Moreover, when network controllability is considered, designing edge additions is a promising alternative to add more actuation capabilities in order to improve different performance metrics. We quantify the impact of an edge modification with the H∞ and H2 norms. For networks with positive edge weights we show how the H∞ norm can be computed exactly for each possible single edge modification, while for the H2 norm we instead obtain a lower bound. This bound is linked to the trace of the controllability Gramian, hence it can be used for instance to reduce the energy needed for control.

This paper investigates the problem of controlling a complex network with reduced control energy. Two centrality measures are defined, one related to the energy that a control, placed on a node, can exert on the entire network, and the other related to the energy that the network exerts on a node. We show that by combining these two centrality measures conflicting control energy requirements, like minimizing the average energy needed to steer the state in any direction and the energy needed for the worst direction, can be simultaneously taken into account. From an algebraic point of view, the node ranking that we obtain from the combination of our centrality measures is related to the non-normality of the adjacency matrix of the graph.

The control-theoretic notion of controllability captures the ability to guide a systems behavior toward a desired state with a suitable choice of inputs. Controllability of complex networks such as ...

This paper investigates the problem of controlling a complex network with reduced control energy. Two centrality measures are defined, one related to the energy that a control, placed on a node, can exert on the entire network, the other related to the energy that all other nodes exert on a node. We show that by combining these two centrality measures, conflicting control energy requirements, like minimizing the average energy needed to steer the state in any direction and the energy needed for the worst direction, can be simultaneously taken into account. From an algebraic point of view, the node ranking that we obtain from the combination of our centrality measures is related to the non-normality of the adjacency matrix of the graph.

The aim of this paper is to shed light on the problem of controlling a complex network with minimal control energy. We show first that the control energy depends on the time constant of the modes of the network, and that the closer the eigenvalues are to the imaginary axis of the complex plane, the less energy is required for complete controllability. In the limit case of networks having all purely imaginary eigenvalues (e.g. networks of coupled harmonic oscillators), several constructive algorithms for minimum control energy driver node selection are developed. A general heuristic principle valid for any directed network is also proposed: the overall cost of controlling a network is reduced when the controls are concentrated on the nodes with highest ratio of weighted outdegree vs indegree.

In this paper, we study the problem of controlling complex networks with unilateral controls, i.e., controls which can assume only positive or negative values, not both. Given a network with linear dynamics represented by the adjacency matrix A, we seek to understand the minimal number of unilateral controls that renders the network controllable. This problem has structural properties that for instance allows us to establish theoretical bounds and identify key topological properties that makes a network relatively easy to control with unilateral controls as compared to unrestricted controls. We find that the structure of the left null space of A is particularly important to this end. In a computational study we find that the network topology largely determines the number of unilateral controls and that the derived lower bounds often are achieved with heuristic methods.

In this paper, we study the problem of controlling complex networks with unilateral controls, i.e., controls which can assume only positive or negative values, not both. Given a complex network represented by the adjacency matrix A, an algorithm is developed that constructs an input matrix B such that the resulting system (A, B) is controllable with a near minimal number of unilateral control inputs. This is made possible by a reformulation of classical conditions for controllability that casts the minimal unilateral input selection problem into well known optimization problems. We identify network properties that make unilateral controllability relatively easy to achieve as compared to unrestricted controllability. The analysis of the network topology for instance allows us to establish theoretical bounds on the minimal number of controls required. For various categories of random networks as well as for a number of real-world networks these lower bounds are often achieved by our heuristics.

The aim of this paper is to investigate the problem of selecting driver nodes enabling the control of a network with minimal control energy. The networks we are interested in are coupled harmonic oscillators, i.e., networks in which the eigenvalues are all purely imaginary. For them, several criteria for driver node selection are presented, based on the different measures of control energy considered in this context. The constructive algorithms we develop for these criteria are normally solving the problem in a heuristic way, although in one case the exact solution can be computed efficiently regardless of size.

\This paper deals with the problem of controlling linear complex networks in an efficient way, i.e., with limited control energy. A general principle is provided, based on the eigenvalues of the network. It is shown numerically that the cost of controlling a network grows with the (absolute value of the) real part of the eigenvalues of the adjacency matrix. Constructive rules for driver node selection are also provided, based on the (weighted) topology of the network. In particular, we show that the key to have an energetically efficient driver node placement strategy is to use the skewness of the outdegree versus indegree distributions of the network, a topological property not associated before to controllability. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.