Reconfigurable Intelligent Surfaces (RISs) are announced as a truly transformative technology, capable of smartly shaping wireless environments to optimize next generation communication networks. Among their numerous foreseen applications, Reflective RISs (RRISs) have been shown theoretically beneficial not only to enable wireless localization through controlled multipath in situations where conventional systems would fail (e.g., with too few available base stations (BSs) and/or under radio blockages) but also to locally boost accuracy on demand (typically, in regions close to the surface). In this paper, leveraging a dedicated frequency-domain mmWave indoor channel sounding campaign, we present the first experimental evidences of such benefits, by emulating offline simple RIS-aided single-BS positioning scenarios including line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, single-RIS and multi-RIS configurations, as well as several user equipment (UE) locations. Various combinations of estimated multipath parameters (e.g., delays, Angles of Departure (AoDs) or power gains) are also fed as inputs into basic Least Squares (LS) positioning solvers. Despite their simplicity, these preliminary proof-of-concept validations show concretely how and when RIS-reflected paths could contribute to enhance localization performance.

The integration of sensing capability in the design of wireless communication systems is foreseen as a key enabler for efficient radio resource management in next-generation networks. This paper focuses on millimeter-wave communications, which are subject to severe attenuation due to blockages, ultimately detrimental to system performance. In this context, the sensing functionality can allow measuring or even imaging the wireless environment allowing anticipation of possible link failures, thus enabling proactive resource reallocation such as handover. This work proposes a novel mechanism for opportunistic environment sensing, which leverages existing network infrastructure with low complexity. More specifically, our approach exploits the fluctuations of interference, perceived in antenna side lobes, to detect local activity due to a moving blocker around the reference communication link. Numerical evaluations show that the proposed method is promising as it allows effective assessment of the blocker direction, trajectory and possibly, its location, speed, and size.

Connected and Automated Vehicles (CAV) can leverage both physical sensing and wireless communications to improve road safety and efficiency. While standalone perception is practically limited by the range of on-board sensors and/or by the presence of obstacles that prevent seeing through, collective perception indeed allows vehicles to exchange -and even beyond, to fuse- information related to the obstacles they detect independently. In this paper, by means of system-level simulations, we analyze the performance of Vehicle-to-Network (V2N) connectivity based on the 5th Generation - New Radio (5GNR) as a support to Cooperative, Connected and Automated Mobility (CCAM), in the light of scalability concerns (as a function of the number of connected users). Focusing on a canonical centralized Cooperative Collision Avoidance (CoCA) use case, we assess the link reliability and the End-to-End latency of all the critical messages involved. Simulation results emphasize the potential of 5GNR connectivity to enable cooperative obstacles detection and thus accordingly, to improve vehicle’s/driver’s awareness (regarding distant vehicles, vulnerable road users, etc.).

By means of system-level simulations, we analyze in this paper the performance of Vehicle-to-Network (V2N) connectivity based on the 5th Generation - New Radio (5GNR) as a support to Cooperative, Connected and Automated Mobility (CCAM), in light of both network and Multi-access Edge Computing (MEC) deployments. Focusing on a canonical centralized Cooperative Lane Change (CLC) use case that involves three vehicles in a cross-border highway environment, we assess the link reliability and the End-to-End (E2E) latency of all the messages involved in the CLC negotiation phase (from/to interconnected MECs hosting the centralized maneuvering application), while assuming different deployment configurations and the coexistence with a second demanding vehicular service running over the same radio resources. On this occasion, we illustrate possible benefits from Bandwidth Partitioning (BWP) on Uplink (UL) latency, as well as from an hypothetically tight cooperation between Mobile Network Operators (MNOs) on reliability and continuity, leveraging low-latency inter-MEC transactions and seamless cross-border handover capabilities.

The rising concept of reconfigurable intelligent surface (RIS) has promising potential for Beyond 5G localization applications. We herein investigate different phase profile designs at a reflective RIS, which enable non-line-of-sight positioning in nearfield from downlink single antenna transmissions. We first derive the closed-form expressions of the corresponding Fisher information matrix (FIM) and position error bound (PEB). Accordingly, we then propose a new localization-optimal phase profile design, assuming prior knowledge of the user equipment location. Numerical simulations in a canonical scenario show that our proposal outperforms conventional RIS random and directional beam codebook designs in terms of PEB. We also illustrate the four beams allocated at the RIS (i.e., one directional beam, along with its derivatives with respect to space dimensions) and show how their relative weights according to the optimal solution can be practically implemented through time sharing (i.e., considering feasible beams sequentially).

In this paper, we present a techno-economic analysis of a Cooperative, Connected and Automated Mobility (CCAM) use case in a specific cross-border environment, namely Cooperative Lane Merging (CLM). The latter is assumed to rely on Vehicle-to-Infrastructure (V2I) connectivity with respect to a set of inter-connected Road Side Units (RSUs). In order to feed the techno-economic framework with the required data in terms of road infrastructure, extensive system-level simulations have been performed using a connectivity oriented key performance indicator (KPI), while considering three different deployment scenarios and realistic road traffic densities. The proposed model identifies the minimum additional RSUs required to satisfy the CLM KPI with respect to the number of simultaneous connected cars. First results show the beneficial impact from densifying the road network infrastructure on the CLM service availability, especially under the highest road traffic conditions. In terms of Total Cost of Ownership (TCO), cost results of a set of scenarios considering the variation of both the number of connected cars and the RSUs to be deployed are discussed as well.

In this paper, we present a low-latency direction assisted channel estimation algorithm suitable for millimeter wave (mm-Wave) systems. First, during the beam training procedure, we perform angle of departure (AoD) and angle of arrival (AoA) measurements with their corresponding variances and based on them, we design both downlink and uplink beams. We then perform signal measurements with these beams and accordingly design the sensing matrix, while also iteratively refining the angle estimation and the beams for the next measurements accordingly. Finally, exploiting both the sparseness and the intrinsic geometric nature of the mm-Wave channel, we apply compressive sensing tools so as to complete the estimation procedure. Simulations show that the corresponding estimation error decreases rapidly in comparison with other conventional approaches.

Cooperative Intelligent Transport Systems (C-ITS) are expected to fulfill the needs of various emerging road safety applications. In this work, we focus on improving C-ITS vulnerable road users safety by considering a Cooperative Collision Avoidance (CoCA) system. In this system, vehicles rely on on-board sensors in order to generate local occupancy maps that are transmitted by means of LTE-V2X connectivity to a fusion center. The latter executes the fusion of successfully received local maps, in order to generate a global occupancy map, which can reveal obstacles that could not be perceived based uniquely on standalone vehicle's perception means. The resulting map is broadcast to all connected vehicles in transmission range (i.e., even those who have not contributed to the global map) in order to announce a risk of collision. The main objective of this work is to evaluate the impact of LTE-V2X connectivity performance on the fusion result and to define the best compromises between the communication configuration, the definition of occupancy maps and obstacle detection capabilities.

In this paper, we explore the potential, as well as a few limitations, of Cellular - Vehicle-to-Everything (C-V2X) sidelink connectivity (a.k.a., PC5-Mode4) for Cooperative Lane Merging (CLM) services in a specific cross-border highway context. Based on a dedicated simulation flow, we assess both the successful completion of the CLM negotiation procedure from the standpoint of messages delivery (service availability) and the accuracy of position awareness, as perceived by the manoeuvre management application out of collected status messages (service reliability). These indicators are evaluated as a function of road traffic, overall system latency and infrastructure deployment. Simulation results suggest that both Vehicle-to-Infrastructure and Vehicle-to-Vehicle communications based on C-V2X PC5-Mode4, which benefit respectively from information redundancy and short-range links, could advantageously complement Vehicle-to-Network connectivity to locally guarantee the seamless continuity of latency-critical services in challenging environments.

We study a millimeter wave (mm-wave) wireless network deployed along the roads of an urban area, to support localization and communication services simultaneously for outdoor mobile users. In this network, we propose a mm-wave initial beam-selection scheme based on localization-bounds, which greatly reduces the initial access delay as compared to traditional initial access schemes for standalone mm-wave small cell base station (BS). Then, we introduce a downlink transmission protocol, in which the radio frames are partitioned into three phases, namely, initial access, data, and localization, respectively. We establish a trade-off between the localization and communication performance of mm-wave systems, and show how enhanced localization can actually improve the data-communication performance. Our results suggest that dense BS deployments enable to allocate more resources to the data phase while still maintaining appreciable localization performance. Furthermore, for the case of sparse deployments and large beam dictionary size (i.e., with thinner beams), more resources must be allotted to the localization phase for optimizing the rate coverage. Based on our results, we provide several system design insights and dimensioning rules for the network operators that will deploy the first generation of mm-wave BSs.

5G radio positioning exploits information in both angle and delay, by virtue of increased bandwidth and large antenna arrays. When large arrays are embedded in surfaces, they can passively steer electromagnetic waves in preferred directions of space. Reconfigurable intelligent surfaces (RIS), which are seen as a transformative “beyond 5G” technology, can thus control the physical propagation environment. Whereas such RIS have been mainly intended for communication purposes so far, we herein state and analyze a RIS-aided downlink positioning problem from the Fisher Information perspective. Then, based on this analysis, we propose a two-step optimization scheme that selects the best RIS combination to be activated and controls the phases of their constituting elements so as to improve positioning performance. Preliminary simulation results show coverage and accuracy gains in comparison with natural scattering, while pointing out limitations in terms of low signal to noise ratio (SNR) and inter-path interference.

The interest for communications between vehicles and the infrastructure or other vehicles (V2X) has recently increased towards connected vehicle applications, and particularly cooperative collision avoidance (CoCA). In this paper, we evaluate the performance of LTE-V2X networks in the context of Intelligent Transportation Systems for traffic collision avoidance applications based on sharing occupancy maps between the infrastructure and the vehicles. We compare by simulation different LTE-V2X configurations under realistic conditions in an intersection scenario. Then, we evaluate every type of communication link (V2I and V2V) as a function of the density of vehicles. The results show the potential of the concept for V2X and the trade-offs in terms of reliability, capacity and latency.

In this paper, we propose optimal beamforming strategies for a millimeter wave (mmWave) system consisting of multiple users based on the localization performance bounds. We consider a single base station (BS) with prior coarse knowledge of the users' positions and formulate the optimal beamforming problem in order to minimize the localization error consisting of Cramer Rao Lower Bounds (CRLBs) of delay, angle of departure (AoD) and angle of arrival (AoA) estimation at the mobile users. We first formulate the simplified CRLB of estimation parameters, taking advantage of multiple sub-carriers, and then formulate the localization error for optimization of the beamformer. Finally, we evaluate the resulting position and orientation error bounds after optimization for several fairness strategies through Monte Carlo simulations.

We analyze a millimeter wave network, deployed along the streets of a city, in terms of positioning and downlink data-rate performance, respectively. First, we present a transmission scheme where the base stations provide jointly positioning and data-communication functionalities. Accordingly, we study the trade- off between the localization and the data rate performance based on theoretical bounds. Then, we obtain an upper bound on the probability of beam misalignment based on the derived localization error bound. Finally, we prescribe the network operator a scheme to select the beamwidth and the power splitting factor between the localization and communication functions to address different quality of service requirements, while limiting cellular outage.

This positioning paper provides an overview on an envisioned platform, intended as a set of technologies, protocols and algorithms, to achieve highly accurate localization for cooperative-intelligent transport systems. This is the result of a three years investigation conducted within the scope of the EU H2020 HIGHTS project and offers an insight on the envisioned hybrid service platform to enable vehicular positioning services for highly automated driving (HAD) scenarios. This paper reviews the main components of such a platform, drafting the guidelines for the seamless integration (i.e. hybridization) and field validation of multiple localization solutions to support robust HAD functionalities.

The Internet of Things (IoT) has accelerated the innovations in the connected car domain. Together with Intelligent Transportation Systems (ITS), the IoT technologies envision unleashing efficient and more sustainable transportation systems. A connected car is already considered as an IoT resource whose capabilities can be exposed through an IoT platform, but few testbeds provide full-stack, cloud-based environment for connected car services experimentation. This paper formulates several research and engineering challenges for developing such testbed. Our proposed testbed exploits the IoT and microservices to provide advanced functionalities such as (i) the integration of heterogeneous sensors and vehicular communication technologies in a complete end-to-end stack and (ii) an Edge Server for vehicular data annotation, local processing with actuation. The testbed exposes the vehicular resources securely through a Cloud based framework and open APIs. They are supported by The Web of Things (WoT) based interoperable descriptions of vehicular devices and interactions among relevant actors. Finally, the Cloud system deploys the connected car services using microservices philosophy. In addition to that, we present a functional IoT architecture of the testbed, describe its operational phases, novel aspects, prototyping and experimentation details.

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSSs) combined with on-board sensors and high-resolution maps. In cooperative intelligent transportation systems, the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an implicit cooperative positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights, or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, and a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.

This paper addresses Particle Filter (PF)-based hybrid Cooperative Localization (CLoc) strategies consisting of fusing absolute position information from embedded Global Navigation Satellite System (GNSS) with relative distance-dependent estimates using Impulse Radio - Ultra WideBand (IR-UWB) technology. Such hybrid GNSS/IR-UWB CLoc yet cannot benefit from the high precision estimates from the IR-UWB due to the disparity between GNSS position and IR-UWB V2V ranging noises, leading to a divergence in CLoc accuracy. This paper first investigates the source of such counter-intuitive effect, and second proposes a novel adaptive Bayesian dithering technique to improve the efficiency of GNSS/IR-UWB fusion-based CLoc. This strategy increases the probability to reach a 20 cm accuracy from 50% (conventional IR-UWB and WiFi PF) to 90%.

In an effort to improve positioning accuracy in Vehicular Ad hoc NETworks (VANETs), Cooperative Localization (CLoc) has been proposed to fuse relative observations from Vehicle-to-Vehicle (V2V) communication devices with absolute observations from on-board resources such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU), and Wheel Speed Sensors (WSSs). In challenging but common tunnel environments, prolonged GNSS outages and unsustainable error accumulation of inertial sensors over time (e.g., gyroscopes) lead to the fast divergence of position estimates. In this paper, we aim at resolving this problem by relying on additional Vehicle-to-Infrastructure (V2I) measurements, making use of RoadSide Units (RSUs) internally to the tunnel. In particular, we explore practical trade-offs between V2I technology (i.e., Impulse Radio - Ultra WideBand (IR-UWB) or ITS-G5) and RSUs deployment costs (i.e., in terms of density and geometric configuration), while improving CLoc absolute accuracy. We also consider combining this solution with lane detection capabilities (e.g., camera-based) and associated road information, while comparing it with a more conventional approach based on GNSS repeaters.

Impulse Radio - Ultra Wideband (IR-UWB) ranging based on first detected path's Time-of-Arrival (ToA) theoretically enables high-accuracy localization in Line of Sight (LoS) contexts. However, performance can be severely degraded in practical indoor environments due to both dense multipath propagation and signal obstructions. To mitigate these effects, the space-time correlation of tracked multipath components (MPCs) under mobility can be beneficial to collectively reconstruct the missing direct path's ToA in case of detected NLoS situation. This paper aims at improving further the robustness of such Multipath-aided Direct Path Reconstruction (MDPR) in very dense multipath and generalized NLoS with respect to anchors, while considering constraints of real integrated IR-UWB receivers. More specifically, new strategies are introduced to better approximate and dynamically adjust the link between direct and secondary paths for longer-term correction validity, as well as to handle MPCs appearance/disappearance over NLoS portions of trajectory. The proposed solution is validated by means of semi-deterministic dynamic simulations based on IEEE 802.15.4a channel models.

Precise location services are seen as key enablers to future Intelligent Transport Systems (ITSs). Relying on Vehicle-to-Vehicle (V2V) communication links, one promising solution consists in performing distributed Cooperative Positioning (CP). More specifically, Cooperative Awareness Message (CAM) broadcasts from neighboring vehicles (seen as “virtual anchors”) are used to exchange positional information and to measure V2V radiolocation metrics such as the Received Signal Strength Indicator (RSSI). For the sake of fusing these nonlinear hybrid data, Particle Filters (PFs) represent the required positional information by a set of particles with associated weights. However, in a jointly cooperative and distributed context, the transmission of explicit particle clouds (required by receiving neighbors to update their own location estimates) is hardly affordable under limited V2V channel capacity with typical numbers of particles. In this paper we thus combine and compare several solutions in terms of message representation and adaptive transmission policy so as to reduce simultaneously CAM overhead, channel congestion and computational complexity. Proposals are made at both signal processing level (parametric density approximation) and protocol level (jointly adaptive transmission payload, power and rate), showing no impact on channel load in congested scenarios and negligible CP accuracy degradation in comparison with standard CAM transmission at critical rates.

In wireless decentralized networks, security and privacy become more challenging due to stringent architecture and sometimes low-complexity constraints. Confidentiality of the transmissions is usually achieved by symmetric cryptography, which implies the distribution of identical secret keys to the legitimate nodes. Classical solutions for symmetric key distribution can be too complex from a computational or infrastructural point of view. An alternative would be physical layer key generation, which exploits the radio channel as a source of common information. This paper looks into the generation of secret keys from Impulse Radio - Ultra Wideband (IR-UWB) channels using low-power integrated devices. We investigate the possibility to generate identical secret bit sequences in practical environments (e.g., possibly occupied indoor rooms) and operating conditions (e.g., mobility) under specific hardware limitations inherent to device integration (e.g., time resolution and signal dynamics). The main challenge is to obtain reciprocal and random bits at an acceptable rate. Taking into account these three criteria (i.e., reciprocity, randomness, and key length), we describe and evaluate a new quantization scheme adapted to the channel impulse responses acquired by real devices.

In the specific context of wearable wireless networks, this work aims at improving the accuracy and the robustness of radiolocation solutions based on standard narrow-band radio technologies for user-centric mobile applications. The proposed solution takes benefits from off-body or body-to-body radio links, with respect to fixed elements of infrastructure or to other mobile equipped subjects, respectively. The main idea is to infer relative angular information between the carrying body's heading and the median direction of arrival of received signals. For this sake, we rely on differential received power measurements issued at judiciously placed on-body nodes. Light calibration procedures (e.g., based on the full-scale dynamics of observed measurements) and joint absolute position/orientation estimation algorithms then enable to cover both individual/non-cooperative and collective/cooperative navigation needs. The performance is assessed by means of field experiments with IEEE 802.15.4-compliant integrated devices operating at 2.4 GHz and an optical motion capture system delivering the ground truth reference. On this occasion, our proposal is shown to be resilient against mobile Non-line of Sight (NLoS) situations (e.g., in crowded environments).

This paper describes a new analytical model based on stochastic geometry, which predicts the Inter-cell Interference (ICI) level suffered by User Equipments (UEs) in downlink (DL) Heterogeneous Networks (HetNets) according to their locations. The HetNet is modeled with stochastic geometry. More specifically, the proposed model relies on the Poisson Point Process (PPP) formalism (and its large arsenal of tools) to derive closed forms for location-dependent ICI under realistic propagation channel assumptions, including both fast fading and shadowing. Since the ICI level depends on the deployment type, both cochannel allocation and a separate frequency band allocation are investigated. Analytical models are thus derived for/regarding the median value of the co-channel interference and the cotier interference. The latter predictions are used to build 2D interference maps (i.e. with estimated ICI level at each pixel of the area of interest), which could be subsequently beneficial to location-based Radio Resource Management (RRM) or ICI coordination/cancellation algorithms in a real system. We provide herein a few illustrations in LTE-Advanced (LTE-A) standardcompliant scenarios, while discussing the “spatial reliability” of this model depending on key input parameters and working assumptions.

This paper investigate the accuracy of the location-dependent Inter-Cell Interference (ICI) level predicted taking into account shadowing effect in downlink heterogeneous cellular networks. The model accuracy is studied with respect to the distance range estimation reliability, where the distance between the User Equipement (UE) and its most interfering eNB is estimated according to the Reference Signal Strength (RSS) ranging method. The log-normal shadowing path loss model is considered, where three distance estimators are used: mean, median and mode. The reliability of these ones is discussed in macro and small cell scenarios. Then, the impact of distance error estimation in both scenarios is evaluated.

In this paper we introduce methods that strengthen the identity of end-devices in order to prevent impersonation attacks in wireless ad hoc networks. Pseudonyms are locally generated from Received Signal Strength Indicators (RSSI) for narrow band IEEE 802.15.4 standard or Round Trip - Time of Flight (RT-ToF) measurements (optionally, along with relative clock drift estimates) for Impulse Radio - Ultra Wideband (IR-UWB) technology. These radiolocation metrics are converted into range measurements, quantized and then fed into hash functions to produce pseudonyms. For the two benchmarked radio technologies, practical trade- offs are illustrated depending on the input measurement accuracy under different channel assumptions. The evaluated solution enables to securely guess the pseudonyms of trusted neighbors with no information leakage. It also achieves advantageous low probability of successful attacks based on brute-force or statistics-aided strategies or compared to other impersonation detection strategies (e.g. RSSI history monitoring).

This paper provides an overview of the project Critical and Rescue Operations using Wearable Wireless sensor networks (CROW2) and discusses the related research challenges and upcoming objectives. The project objective is to provide ubiquitous wireless communication and monitoring systems enabling life-critical and rescue operations. In this context, it is envisioned that wearable Wireless Sensor Networks (WSNs) will play an important role, and opportunistic exploitation of both On-Body and Body-to-Body communications will enable beyond state-of-the-art and cross-layer communication architecture for wearable WSNs.

Symmetric secret key generation from physical layer measurements has recently emerged as an alternative to computationally secure protocols or predistribution techniques for key sharing. This new approach takes advantage of the intrinsic properties of radio channels (e.g. reciprocity and spatial decorrelation) by quantizing measurable channel characteristics into a suite of codewords. However, noise or other transmission artifacts can degrade the link reciprocity and lead to bit errors between the users' codewords, which are strongly related to the quantization thresholds. In this paper, we present a framework for quantization in the context of key generation. In order to find a trade-off between reciprocity (an indicator of robustness) and codeword diversity (an indicator of randomness) under a fixed-length constraint, we investigate the non-uniform quantization and we show the interest of optimizing the quantization thresholds as a function of the signal-to-noise ratio.

This paper investigates the issues related to the link quality estimation and routing performance in highly dynamic WBSNs, where the inherent body mobility introduces frequent time and space variations in the observed link reliability and network connectivity. A new Opportunistic and Mobility Aware Routing (OMAR) protocol is proposed, which optimizes the routing process by taking benefit from captured long-term and short-term link behavior. Extensive simulations are performed using realistic WBSNs mobility and channel modeling, and representative MAC constraints. The obtained results show that the proposed OMAR approach outperforms conventional routing protocols in terms of energy consumption and routing path length, while ensuring a stable packet delivery ratio.

The importance of graphs as the fundamental structure underpinning many real world applications is no longer to be proved. Large graphs have emerged in various fields such as biological, social and transportation networks. The sheer volume of these networks poses challenges to traditional techniques for storage and analysis of graph data. In particular, OLAP analysis requires access to large portions of data to extract key information and to feed strategic decision making. OLAP provides multilevel, multiperspective views of the data. Most of the current techniques are optimized for centralized graph processing. A distributed approach providing horizontal scalability is required in order to handle the analysis workload. In this paper, we focus on applying OLAP analysis on large, distributed graph data. We describe Distributed Graph Cube, our distributed framework for graph-based OLAP cubes computation and aggregation. Experimental results on large, real-world datasets demonstrate that our method significantly outperforms its centralized counterparts. We also evaluate the performance of both Hadoop and Spark for distributed cubes computations.

Generating secret keys in mobile wireless networks is considered a challenging problem where a key management infrastructure is not always available. Recent security methods have shown that secret keys can be generated using Ultra Wide Band (UWB) channels. These solutions rely on relevant channel properties such as reciprocity and spatial decorrelation. Accordingly, the radio channel responses can be used as common information to derive secret keys shared by legitimate parties. However, novel studies in the field of UWB channel prediction have demonstrated that channel profiles could be reliably inferred using for instance Ray-Tracing tools. This paper explores this technique to perform attacks and to evaluate the security of UWB secret key generation methods. The main observation here is that it is difficult for a third party to obtain the exact channel responses; thus to retrieve the secret keys. The robustness of UWB key generation methods then depends on the complexity for attackers to describe precisely the physical environment and on the post processing methods to agree on the same key (i.e., quantization, erroneous bits detection, etc.).

This paper investigates the impact of realistic antennas on joint anchor-less localization and indoor characterization based on Impulse Radio (IR) Ultra Wideband (UWB) communications. In this frame, the Maximum Averaged Likelihood (MAL) algorithm and its extended version are considered, both relying on a tree approach consisting in two stages. The first part of the process, which is common to both algorithms, exploits the cross-correlation between received and locally predicted paths. The second stage calculates the averaged likelihood of measured path parameters obtained in the previous step, but different measurements are used for MAL and extended MAL (eMAL). In the first algorithm, only the Angle of Incidence (AoI) and the Time of Arrival (ToA) are considered, whereas the eMAL tree algorithm also accounts for two couples of Angles of Departure (AoDs) and Angles of Arrival (AoAs). The estimation errors of both nodes coordinates and room dimension obtained with the two algorithms are compared for three realistic UWB antennas. Finally, the remaining algorithm-independent ambiguities (i.e. resulting from scenario and geometry) are discussed.

This paper presents an algorithm that enables anchor-less localization for pairs of static Impulse Radio - Ultra Wide Band (IR-UWB) devices in a room. The estimation progresses through a tree, as the most significant received paths are successively considered. Unlikely path parameters estimates are progressively rejected, relying on cross-correlation (i.e. with respect to predicted waveforms), as well as on a Maximum Averaged Likelihood (MAL) criterion. The latter is based on the measurements of Angle of Incidence (AoI) and Time of Arrival (ToA) for each detected multipath component, in addition to one range measurement. Once intrinsic mirror and transmitter/receiver ambiguities are solved, in the lack of antennas array and reference landmarks, a positioning error lower than 0:6 m and a room dimension error lower than 2% are obtained for 95% of estimates.

The Impulse Radio - Ultra Wideband (IR-UWB) technology, which benefits from unprecedented ranging capabilities through precise Time of Arrival (TOA) estimation, is now widely considered as a credible solution for Location and Tracking (LT) applications in Wireless Sensor Networks (WSN). In this context, low-complexity receiver architectures such as Energy Detectors (ED) have been favored for the past years. Recent methods have also been proposed to determine ED thresholds for optimal leading-edge detection under simplified channel assumptions. Nevertheless, more realistic indoor multipath profiles still make threshold-based TOA estimation challenging with such receivers. In this paper, we present two new Bayesian TOA estimators that rely on the overall energy profile available at the output of the ED, as well as on realistic channel statistics (typically that of the IEEE 802.15.4a standard). Simulation results are provided for performance comparison with two popular threshold-based estimators, showing the robustness of the new methods at practical Signal to Noise Ratio (SNR) values.

Popular linearized estimators such as the Extended Kalman Filter (EKF) or the one-step linearized Least Squares (LS) estimator, which have been used for years to solve positioning and tracking problems in location-enabled wireless networks, require that available observations are locally linearized (as functions of estimated variables) around the predicted state or initial optimization guess. In most of practical cases however, using measured radiolocation metrics as direct observations (e.g. Time of Arrival (TOA) or Time Differential of Arrival (TDOA)) might lead to poor linearization conditions and hence alter estimation precision and robustness accordingly. In this paper, we present an optimization-based method that enables to adaptively build quasi-linear observations as combinations of raw measurements. The main idea is to choose the coefficients of the linear combinations that minimize the Mean Square Error (MSE) of location estimators, while taking into account both bias errors due to linearization and measurement noise effects.

A few years ago, IEEE 802.15.4a channel models t were initially introduced to assess the performance of Low Data Rate (LDR) Impulse Radio - Ultra Wideband (IR-UWB) peer-to-peer communications in a variety of indoor and outdoor environments, including Line of Sight (LOS) and Non Line of Sight (NLOS) propagation configurations. However, recent r wireless applications such as indoor target tracking or environ- i ment characterization necessitate simulating realistic location-dependent channel profile variability under node mobility. As an alternative to computationally demanding and/or proprietary deterministic prediction tools (e.g. Ray-Tracing), we propose in this paper a self-contained UWB channel model that preserves the statistical features of static IEEE 802.15.4a models as well as physical consistency (e.g. vanishing or appearing paths) over large-scale trajectories, while accounting for the spatial correlation of multipath components.

This paper presents an algorithm that interprets the waveforms associated with received mutipath components in order to estimate their Direction of Arrival (DoA), Direction of Departure (DoD) and Incident Angle to the Indoor Surface (IAIS) in the context of Impulse Radio - Ultra Wide Band (IR-UWB) communications. The proposed solution combines a deterministic description of the filtering effects of antennas and materials with a simple statistical model for IAIS. Iteratively, a set of jointly estimated path parameters is chosen according to the cross- correlation values obtained with currently resolved paths and predicted waveforms. Following an estimation tree approach, impossible sets are progressively discarded. An Estimation Quality Indicator is defined, in order to handle aberrant configurations made up of five single-bounce estimated paths. We show here that the IR-UWB transmission technology, which benefits from fine resolution capabilities, looks particularly suitable to the discussed algorithm. Assuming known walls material, simulation results are provided to illustrate the performance of such a technique in a simplified peer-to-peer multipath scenario. The proposed solution discloses new perspectives for context-awareness through classical communication links.

Reliable range-based tracking using ultra-wideband (UWB) radio signals is traditionally problematic in indoor environments. It is indeed widely admitted that non line-of-sight (NLOS) channel configurations introduce significant biases on measured temporal metrics such as times of arrival (TOA), and hence, alter position estimates accordingly. In order to enhance the tracking performance, we propose a specific extended Kalman filter (EKF) formulation, which enables to estimate as state variables the NLOS biases affecting the measured distances between the mobile station (MS) and the fixed base stations (BSs). The described solution is mainly based on the modeling of the deterministic angular-dependent bias variation experienced with MS mobility. It is shown that this approach is robust to harmful situations where all the links between the MS and the BSs simultaneously suffer from NLOS. Simulation results are provided for performance assessment in a few canonical scenarios.

In this paper, we describe an adaption of prioritised medium access techniques for high-precision real-time tracking applications in ultra wideband (UWB) low data rate (LDR) wireless networks. In comparison with classical time division multiple access (TDMA) schemes, the proposed uncoordinated distributed prioritised solution is compliant with critical tracking requirements in large-scale networks. More particularly, the described protocol tends to favor high-speed targets as regards to the success rate of ranging packets issued at anchor nodes, as well as to the refreshment rate and the precision of range measurements. Finally, the performance of this solution is evaluated in comparison with TDMA schemes.

This paper presents an algorithm that interprets the specificities of the received waveforms associated with well-isolated multipath echoes, depending on the type of interaction and on the direction-dependent signal deformations due to antennas. The goal is to jointly estimate Direction of Arrival (DoA), Direction of Departure (DoD) and reflecting material for simple-bounce interaction in a peer-to-peer Impulse Radio - Ultra Wide Band (IR-UWB) communication context. The proposed technique is mainly based on prior antenna characterisation and on prediction of site-specific filtering effects due to simple electromagnetic interactions with environment (i.e. simple-bounce reflections). Simulation results are provided to illustrate the expected performance of such a system. The proposed technique discloses new perspective for context-aware services in wireless networks.

In this paper, we evaluate the performance of impulse radio-ultra wideband (IR-UWB) communications at the physical layer (PHY) level in a multi-user low data rate (LDR) context. Focusing more specifically on the IEEE 802.15.4a standard, this study aims at characterizing and quantifying the potential interference effects resulting from simultaneous competing transmissions. For the sake of simplicity, we deliberately restrict the investigation to a generic coherent Rake receiver in typical illustrating scenarios (in terms of nominal bit rate, pulse repetition period, and channel configuration). On this occasion, we provide original semi-analytical bit error rate (BER) models and simulation results, along with realistic figures for the corresponding tolerated SIR and SNR levels. The derived interference specifications, which properly abstract the IEEE 802.15.4a PHY layer in the presence of MUI, can be considered as relevant inputs for further system, medium access control (MAC), or cross-layer studies.

This paper analyzes and compares the performance of non-coherent and differential receivers under realistic channels for both synchronization and communication. The well-known error probabilities are reminded and the receiver operating characteristics (ROC) are calculated. It also provides some recommendations to system designers, proposes and discusses critical concepts for a low complexity implementation of the proposed schemes, in compliance with the ranging functionality

Energy detection receiver is one of the most interesting solution for non-coherent low data rate and low complexity UWB systems. In order to keep the complexity of the receiver structure low the same approach should be used also for ToA (time of arrival) estimation purposes. In this paper, after showing that a generalized maximum likelihood estimator for ToA is a sliding window energy based estimator, an improved version of it that uses partial channel state information is proposed in order to improve the accuracy of the estimation overcoming the hardware limitation related to the minimum integration time of the integration circuit and to the available number of integrators