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6G networks aim to enable applications like autonomous driving by providing complementary localization services through key technologies such as non-terrestrial networks (NTNs) with low Earth orbit (LEO) satellites and reconfigurable intelligent surfaces (RIS). Prior research in 6G localization using single LEO, multi-LEO, and multi-LEO multi-RIS setups has limitations: single LEO lacks the required accuracy, while multi-LEO/RIS setups demand many visible satellites and RISs, which is not always feasible in practice. This paper explores the novel problem of localization with a single LEO satellite and a single RIS, bridging these research areas. We present a comprehensive signal model accounting for user carrier frequency offset (CFO), clock bias, and fast and slow Doppler effects. Additionally, we derive a low-complexity estimator that achieves theoretical bounds at high signal-to-noise ratios (SNR). Our results demonstrate the feasibility and accuracy of RIS-aided single-LEO localization in 6G networks and highlight potential research directions., Comment: Accepted for publication in IEEE GLOBECOM Workshops 2024

Extremely large-scale antenna array (ELAA) systems emerge as a promising technology in beyond 5G and 6G wireless networks to support the deployment of distributed architectures. This paper explores the use of ELAAs to enable joint localization, synchronization and mapping in sub-6 GHz uplink channels, capitalizing on the near-field effects of phase-coherent distributed arrays. We focus on a scenario where a single-antenna user equipment (UE) communicates with a network of access points (APs) distributed in an indoor environment, considering both specular reflections from walls and scattering from objects. The UE is assumed to be unsynchronized to the network, while the APs can be time- and phase-synchronized to each other. We formulate the problem of joint estimation of location, clock offset and phase offset of the UE, and the locations of scattering points (SPs) (i.e., mapping). Through comprehensive Fisher information analysis, we assess the impact of bandwidth, AP array size, wall reflections, SPs and phase synchronization on localization accuracy. Furthermore, we derive the maximum-likelihood (ML) estimator, which optimally combines the information collected by all the distributed arrays. To overcome its intractable high dimensionality, we propose a novel three-step algorithm that first estimates phase offset leveraging carrier phase information of line-of-sight (LoS) paths, then determines the UE location and clock offset via LoS paths and wall reflections, and finally locates SPs using a null-space transformation technique. Simulation results demonstrate the effectiveness of our approach in distributed architectures supported by radio stripes (RSs) -- an innovative alternative for implementing ELAAs -- while revealing the benefits of carrier phase exploitation and showcasing the interplay between delay and angular information under different bandwidth regimes.

In this paper, we investigate 3-D localization and frequency synchronization with multiple reconfigurable intelligent surfaces (RISs) in the presence of carrier frequency offset (CFO) for a stationary user equipment (UE). In line with the 6G goals of sustainability and efficiency, we focus on a frugal communication scenario with minimal spatial and spectral resources (i.e., narrowband single-input single-ouput system), considering both the presence and blockage of the line-of-sight (LoS) path between the base station (BS) and the UE. We design a generalized likelihood ratio test (GLRT)-based LoS detector, channel parameter estimation and localization algorithms, with varying complexity. To verify the efficiency of our estimators, we compare the root mean-squared error (RMSE) to the Cram\'er- Rao bound (CRB) of the unknown parameters. We also evaluate the sensitivity of our algorithms to the presence of uncontrolled multi-path components (MPC) and various levels of CFO. Simulation results showcase the effectiveness of the proposed algorithms under minimal hardware and spectral requirements, and a wide range of operating conditions, thereby confirming the viability of RIS-aided frugal localization in 6G scenarios., Comment: 13 pages, 11 figures

Ensuring positioning integrity amid faulty measurements is crucial for safety-critical applications, making receiver autonomous integrity monitoring (RAIM) indispensable. This paper introduces a Bayesian RAIM algorithm with a streamlined architecture for snapshot-type 3D cellular positioning. Unlike traditional frequentist-type RAIM algorithms, it computes the exact posterior probability density function (PDF) of the position vector as a Gaussian mixture (GM) model using efficient message passing along a factor graph. This Bayesian approach retains all crucial information from the measurements, eliminates the need to discard faulty measurements, and results in tighter protection levels (PLs) in 3D space and 1D/2D subspaces that meet target integrity risk (TIR) requirements. Numerical simulations demonstrate that the Bayesian RAIM algorithm significantly outperforms a baseline algorithm, achieving over $50\%$ PL reduction at a comparable computational cost., Comment: Submitted to IEEE Transactions on Vehicular Technology

Galileo is the first global navigation satellite system to authenticate their civilian signals through the Open Service Galileo Message Authentication (OSNMA) protocol. However, OSNMA delays the time to obtain a first position and time fix, the Time To First Authentication Fix (TTFAF). Reducing the TTFAF as much as possible is crucial to integrate the technology seamlessly into the current products. In the cases where the receiver already has cryptographic data available, the so-called hot start mode and focus of this article, the currently available implementations achieve an average TTFAF of around 100 seconds in ideal environments. In this work, we explore the TTFAF optimizations available to general OSNMA capable receivers and to receivers with a tighter time synchronization than the required by the OSNMA guidelines. We dissect the TTFAF process, describe the optimizations, and benchmark them in three distinct scenarios with recorded real data (open-sky, soft urban, and hard urban) and the official OSNMA test vectors. The first block of optimizations centers on extracting as much information as possible from broken sub-frames by processing them at page level and combining redundant data from multiple satellites. The second block of optimizations aims to reconstruct missed navigation data by the intelligent use of fields in the authentication tags belonging to the same sub-frame as the authentication key. Combining both optimization ideas improves the TTFAF substantially for all considered scenarios. We obtain an average TTFAF of 60.9 and 68.8 seconds for the test vectors and the open-sky scenario, respectively, with a lowest TTFAF of 44.0 seconds in both. Likewise, the urban scenarios see a drastic reduction of the average TTFAF between the non-optimized and optimized cases. These optimizations have been made available as part of the open-source OSNMAlib library on GitHub., Comment: This work has been submitted to the IEEE for possible publication. 15 pages, 17 figures. Updated version with major changes. New sections II-B, IV-A, IV-C. Updated sections IV-B, VI-D. Acronym changes and fixed typos

The latest assessments of the emerging technologies for reconfigurable intelligent surfaces (RISs) have indicated the concept's significant potential for localization and sensing, either as individual or simultaneously realized tasks. However, in the vast majority of those studies, the RIS state (i.e., its position and rotation angles) is required to be known a priori. In this paper, we address the problem of the joint three-dimensional (3D) localization of a hybrid RIS (HRIS) and a user. The most cost- and power-efficient hybrid version of an RIS is equipped with a single reception radio-frequency chain and meta-atoms capable of simultaneous reconfigurable reflection and sensing. This dual functionality is controlled by adjustable power splitters embedded at each hybrid meta-atom. Focusing on a downlink scenario where a multi-antenna base station transmits multicarrier signals to a user via an HRIS, we propose a multistage approach to jointly estimate the metasurface's 3D position and 3D rotation matrix (i.e., 6D parameter estimation) as well as the user's 3D position. Our simulation results verify the validity of the proposed estimator via extensive comparisons of the root-mean-square error of the state estimations with the Cram\'{e}r-Rao lower bound (CRB), which is analytically derived. Furthermore, it is showcased that there exists an optimal hybrid reconfigurable intelligent surface (HRIS) power splitting ratio for the desired multi-parameter estimation problem. We also study the robustness of the proposed method in the presence of scattering points in the wireless propagation environment., Comment: 14 pages, 6 figures, IEEE Transactions Journal

The contemporary landscape of wireless technology underscores the critical role of precise localization services. Traditional global navigation satellite systems (GNSS)-based solutions, however, fall short when it comes to indoor environments, and existing indoor localization techniques such as electromagnetic fingerprinting methods face challenges of high implementation costs and limited coverage. This article explores an innovative solution that seamlessly blends low Earth orbit (LEO) satellites with reconfigurable intelligent surfaces (RISs), unlocking its potential for realizing uninterrupted indoor and outdoor localization with global coverage. By leveraging the strong signal reception of the LEO satellite signals and capitalizing on the radio environment-reshaping capability of RISs, the integration of these two technologies presents a vision of a future where localization services transcend existing constraints. After a comprehensive review of the distinctive attributes of LEO satellites and RISs, we evaluate the localization error bounds for the proposed collaborative system, showcasing their promising performance on simultaneous indoor and outdoor localization. To conclude, we engage in a discussion on open problems and future research directions for LEO satellite and RIS-enabled localization.

Reconfigurable intelligent surfaces (RISs) are considered as an enabling technology for the upcoming sixth generation of wireless systems, exhibiting significant potential for radio localization and sensing. An RIS is usually treated as an anchor point with known position and orientation when deployed to offer user localization. However, it can also be attached to a user to enable its localization in a semi-passive manner. In this paper, we consider a static user equipped with an RIS and study the RIS localization problem (i.e., joint three-dimensional position and orientation estimation), when operating in a system comprising a single-antenna transmitter and multiple synchronized single-antenna receivers with known locations. We present a multi-stage estimator using time-of-arrival and spatial frequency measurements, and derive the Cram\'er-Rao lower bounds for the estimated parameters to validate the estimator's performance. Our simulation results demonstrate the efficiency of the proposed RIS state estimation approach under various system operation parameters.

The carrier phase of cellular signals can be utilized for highly accurate positioning, with the potential for orders-of-magnitude performance improvements compared to standard time-difference-of-arrival positioning. Due to the integer ambiguities, standard performance evaluation tools such as the Cram\'er-Rao bound (CRB) are overly optimistic. In this paper, a new performance bound, called the mixed-integer CRB (MICRB) is introduced that explicitly accounts for this integer ambiguity. While computationally more complex than the standard CRB, the MICRB can accurately predict positioning performance, as verified by numerical simulations, and hence it serves as a useful guide to choose the system parameters that facilitate carrier phase positioning.

Radio stripes (RSs) is an emerging technology in beyond 5G and 6G wireless networks to support the deployment of cell-free architectures. In this paper, we investigate the potential use of RSs to enable joint positioning and synchronization in the uplink channel at sub-6 GHz bands. The considered scenario consists of a single-antenna user equipment (UE) that communicates with a network of multiple-antenna RSs distributed over a wide area. The UE is assumed to be unsynchronized to the RSs network, while individual RSs are time- and phase-synchronized. We formulate the problem of joint estimation of position, clock offset, and phase offset of the UE and derive the corresponding maximum-likelihood (ML) estimator, both with and without exploiting carrier phase information. To gain fundamental insights into the achievable performance, we also conduct a Fisher information analysis and inspect the theoretical lower bounds numerically. Simulation results demonstrate that promising positioning and synchronization performance can be obtained in cell-free architectures supported by RSs, revealing at the same time the benefits of carrier phase exploitation through phase-synchronized RSs.

A smart city involves, among other elements, intelligent transportation, crowd monitoring, and digital twins, each of which requires information exchange via wireless communication links and localization of connected devices and passive objects (including people). Although localization and sensing (L&S) are envisioned as core functions of future communication systems, they have inherently different demands in terms of infrastructure compared to communications. Wireless communications generally requires a connection to only a single access point (AP), while L&S demand simultaneous line-of-sight propagation paths to several APs, which serve as location and orientation anchors. Hence, a smart city deployment optimized for communication will be insufficient to meet stringent L&S requirements. In this article, we argue that the emerging technologies of reconfigurable intelligent surfaces (RISs) and sidelink communications constitute the key to providing ubiquitous coverage for L&S in smart cities with low-cost and energy-efficient technical solutions. To this end, we propose and evaluate AP-coordinated and self-coordinated RIS-enabled L&S architectures and detail three groups of application scenarios, relying on low-complexity beacons, cooperative localization, and full-duplex transceivers. A list of practical issues and consequent open research challenges of the proposed L&S systems is also provided.

We consider the problem of ESPRIT-oriented precoder design for beamspace angle-of-departure (AoD) estimation in downlink mmWave multiple-input single-output communications. Standard precoders (i.e., directional/sum beams) yield poor performance in AoD estimation, while Cramer-Rao bound-optimized precoders undermine the so-called shift invariance property (SIP) of ESPRIT. To tackle this issue, the problem of designing ESPRIT-oriented precoders is formulated to jointly optimize over the precoding matrix and the SIP-restoring matrix of ESPRIT. We develop an alternating optimization approach that updates these two matrices under unit-modulus constraints for analog beamforming architectures. Simulation results demonstrate the validity of the proposed approach while providing valuable insights on the beampatterns of the ESPRIT-oriented precoders.

We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation. We design appropriate UE-side precoding and combining, to facilitate signal separation. We derive the adaptive detection probabilities of the resolvable signals, based on the geometric channel parameters of the links. Then, we estimate the passive objects using both the double-bounce signals via passive RIS (i.e., RIS-sensing) and the single-bounce multipath direct to the objects (i.e., non-RIS-sensing), based on a mapping filter. Finally, we provide numerical results to demonstrate that effective sensing can be achieved through the proposed framework.

Reconfigurable intelligent surface (RIS) is a promising technological enabler for the 6th generation (6G) of wireless systems with applications in localization and communication. In this paper, we consider the problem of positioning a single-antenna user in 3D space based on the received signal from a single-antenna base station and reflected signal from an RIS by taking into account the mobility of the user and spatial-wideband (WB) effects. To do so, we first derive the spatial-WB channel model under the far-field assumption, for orthogonal frequency-division multiplexing signal transmission with the user having a constant velocity. We derive the Cramer Rao bounds to serve as a benchmark. Furthermore, we devise a low-complexity estimator that attains the bounds in high signal-to-noise ratios. Our estimator neglects the spatial-WB effects and deals with the user mobility by estimating the radial velocities and compensating for their effects in an iterative fashion. We show that the spatial-WB effects can degrade the localization accuracy for large RIS sizes and large signal bandwidths as the direction of arrival or departure deviate from the RIS normal. In particular, for a 64X64 RIS, the proposed estimator is resilient against the spatial-WB effects up to 140 MHz bandwidth. Regarding user mobility, our results suggest that the velocity of the user influences neither the bounds nor the accuracy of our estimator. Specifically, we observe that the state of the user with a high speed (42 m/s) can be estimated virtually with the same accuracy as a static user.

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

The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing. However, to accurately localize a user placed in the area of influence of an RIS, the RIS location needs to be known a priori and its phase profile is required to be optimized for localization. In this paper, we study the problem of the joint localization of a hybrid RIS (HRIS) and a user, considering that the former is equipped with a single reception radio-frequency (RF) chain enabling simultaneous tunable reflections and sensing via power splitting. Focusing on the downlink of a multi-antenna base station, we present a multi-stage approach for the estimation of the HRIS position and orientation as well as the user position. Our simulation results, including comparisons with the Cram\'er-Rao lower bounds, demonstrate the efficiency of the proposed localization approach, while showcasing that there exists an optimal HRIS power splitting ratio for the desired multi-parameter estimation problem.

This letter is part of a two-letter tutorial on radio localization and sensing, with a focus on mobile radio systems, i.e., 5G and beyond. Building on Part I, which focused on the fundamentals, here we go deeper into the state-of-the-art advances, as well as 6G, covering enablers and challenges related to modeling, coverage, and accuracy.

This letter is part of a two-letter tutorial on radio localization and sensing, with focus on mobile radio systems in 5G mmWave and beyond. Part I introduces the fundamentals, covering an overview of the relevant literature, as well as the different aspects of localization and sensing problems. Then, different performance metrics are presented, which are important in the evaluation of methods. Methods are detailed in the last part of this letter. Part I thus provides the necessary background to delve into more forward-looking problems in Part II., Comment: Fixed typo in footnote 6

Due to their shorter operating range and large bandwidth, automotive radars can resolve many reflections from their targets of interest, mainly vehicles. This calls for the use of extended-target models in place of simpler and more widely-adopted point-like target models. However, despite some preliminary work, the fundamental connection between the radar's accuracy as a function of the target vehicle state (range, orientation, shape) and radar properties remains largely unknown for extended targets. In this work, we first devise a mathematically tractable analytical model for a vehicle with arbitrary shape, modeled as an extended target parameterized by the center position, the orientation (heading) and the perimeter contour. We show that the derived expressions of the backscatter signal are tractable and correctly capture the effects of the extended-vehicle shape. Analytical derivations of the exact and approximate hybrid Cram\'er-Rao bounds for the position, orientation and contour are provided, which reveal connections with the case of point-like target and uncover the main dependencies with the received energy, bandwidth, and array size. The theoretical investigation is performed on the two different cases of known and unknown vehicle shape. Insightful simulation results are finally presented to validate the theoretical findings, including an analysis of the diversity effect of multiple radars sensing the extended target., Comment: 16 pages, double-column, 4 figures, 2 tables, journal

A GNSS signal authentication concept named semi-assisted authentication is proposed. It is based on the re-encryption and publication of keystream sequences of some milliseconds from an already existing encrypted signal. Some seconds after the keystreams are transmitted in the signal-in-space, the signal broadcasts the key allowing to decrypt the sequences and the a-posteriori correlation at the receiver. The concept is particularized as Galileo Assisted Commercial Authentication Service, or ACAS, for Galileo E1-B, with OSNMA used for the decryption keys, and E6C, assumed to be encrypted in the near future. This work proposes the ACAS cryptographic operations and a model for signal processing and authentication verification. Semi-assisted authentication can be provided without any modification to the signal plan of an existing GNSS, without the disclosure of signal encryption keys, and for several days of receiver autonomy, depending on its storage capabilities.

Reconfigurable intelligent surfaces (RISs) have attracted enormous interest thanks to their ability to overcome line-of-sight blockages in mmWave systems, enabling in turn accurate localization with minimal infrastructure. Less investigated are however the benefits of exploiting RIS with suitably designed beamforming strategies for optimized localization and synchronization performance. In this paper, a novel low-complexity method for joint localization and synchronization based on an optimized design of the base station (BS) active precoding and RIS passive phase profiles is proposed, for the challenging case of a single-antenna receiver. The theoretical position error bound is first derived and used as metric to jointly optimize the BS-RIS beamforming, assuming a priori knowledge of the user position. By exploiting the low-dimensional structure of the solution, a novel codebook-based robust design strategy with optimized beam power allocation is then proposed, which provides low-complexity while taking into account the uncertainty on the user position. Finally, a reduced-complexity maximum-likelihood based estimation procedure is devised to jointly recover the user position and the synchronization offset. Extensive numerical analysis shows that the proposed joint BS-RIS beamforming scheme provides enhanced localization and synchronization performance compared to existing solutions, with the proposed estimator attaining the theoretical bounds even at low signal-to-noise-ratio and in the presence of additional uncontrollable multipath propagation.

Accurate and ubiquitous localization is crucial for a variety of applications such as logistics, navigation, intelligent transport, monitoring, control, and also for the benefit of communications. Exploiting millimeter-wave (mmWave) signals in 5G and Beyond 5G systems can provide accurate localization with limited infrastructure. We consider the single base station (BS) localization problem and extend it to 3D position and 3D orientation estimation of an unsynchronized multi-antenna user equipment (UE), using downlink multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) signals. Through a Fisher information analysis, we show that the problem is often identifiable, provided that there is at least one multipath component in addition to the line-of-sight (LoS), even if the position of corresponding incidence point (IP) is a priori unknown. Subsequently, we pose a maximum likelihood (ML) estimation problem, to jointly estimate the 3D position and 3D orientation of the UE as well as several nuisance parameters (the UE clock offset and the positions of IPs corresponding to the multipath). The ML problem is a high-dimensional non-convex optimization problem over a product of Euclidean and non-Euclidean manifolds. To avoid complex exhaustive search procedures, we propose a geometric initial estimate of all parameters, which reduces the problem to a 1-dimensional search over a finite interval. Numerical results show the efficiency of the proposed ad-hoc estimation, whose gap to the Cram\'er-Rao bound (CRB) is tightened using the ML estimation.

In this letter, we investigate a robust beamforming problem for localization-aided millimeter wave (mmWave) communication systems. To handle this problem, we propose a novel restriction and relaxation (R&R) method. The proposed R&R method aims at minimizing the total transmit power while the positioning error follows a Gaussian distribution. Specifically, in the restriction phase of R&R, the probabilistic constraint is transformed into the deterministic form by using the Bernsteintype inequality. In the relaxation phase of R&R, the non-convex optimization problem is reformulated into a convex semidefinite program (SDP) by using semidefinite relaxation (SDR) and firstorder Taylor expansion methods. To the best of our knowledge, we first consider the impact of the distribution of the positioning error on the channel state information (CSI), which further influences the data rate. Numerical results present the trade-off of the beamforming between the communication and positioning.

Reconfigurable intelligent surfaces (RISs) are one of the most promising technological enablers of the next (6th) generation of wireless systems. In this paper, we introduce a novel use-case of the RIS technology in radio localization, which is enabling the user to estimate its own position via transmitting orthogonal frequency-division multiplexing (OFDM) pilots and processing the signal reflected from the RIS. We demonstrate that user localization in this scenario is possible by deriving Cramer-Rao lower bounds on the positioning error and devising a low-complexity position estimation algorithm. We consider random and directional RIS phase profiles and apply a specific temporal coding to them, such that the reflected signal from the RIS can be separated from the uncontrolled multipath. Finally, we assess the performance of our position estimator for an example system and show that the proposed algorithm can attain the derived bound at high signal-to-noise ratio values., Comment: Accepted in ICC 2022

We consider the problem of spatial signal design for multipath-assisted mmWave positioning under limited prior knowledge on the user's location and clock bias. We propose an optimal robust design and, based on the low-dimensional precoder structure under perfect prior knowledge, a codebook-based heuristic design with optimized beam power allocation. Through numerical results, we characterize different position-error-bound (PEB) regimes with respect to clock bias uncertainty and show that the proposed low-complexity codebook-based designs outperform the conventional directional beam codebook and achieve near-optimal PEB performance for both analog and digital architectures.

Integrated satellite-terrestrial communications networks aim to exploit both the satellite and the ground mobile communications, thus providing genuine ubiquitous coverage. For 5G integrated low earth orbit (LEO) satellite communication systems, the timing advance (TA) is required to be estimated in the initial random access procedure in order to facilitate the uplink frame alignment among different users. However, due to the inherent characteristics of LEO satellite communication systems, e.g., wide beam coverage and long propagation delays, the existing 5G terrestrial uplink TA scheme is not applicable in the satellite networks. In this paper, we investigate location-based TA estimation for 5G integrated LEO satellite communication systems. We obtain the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements in the downlink timing and frequency synchronization phase, which are made from the satellite at different time instants. We propose to take these measurements for either UE geolocation or ephemeris estimation, thus calculating the TA value. The estimation is then formulated as a quadratic optimization problem whose globally optimal solution can be obtained by a quadratic penalty algorithm. To reduce the computational complexity, we further propose an alternative approximation method based on iteratively performing a linearization procedure on the quadratic equality constraints. Numerical results show that the proposed methods can approach the constrained Cramer-Rao lower bound (CRLB) of the TA estimation and thus assure uplink frame alignment for different users.

Reconfigurable intelligent surfaces (RISs) are set to be a revolutionary technology in the 6th generation of wireless systems. In this work, we study the application of RIS in a multi-user passive localization scenario, where we have one transmitter (Tx) and multiple asynchronous receivers (Rxs) with known locations. We aim to estimate the locations of multiple users equipped with RISs. The RISs only reflect the signal from the Tx to the Rxs and are not used as active transceivers themselves. Each Rx receives the signal from the Tx (LOS path) and the reflected signal from the RISs (NLOS path). We show that users' 3D position can be estimated with submeter accuracy in a large area around the transmitter, using the LOS and NLOS time-of-arrival measurements at the Rxs. We do so, by developing the signal model, deriving the Cramer-Rao bounds, and devising an estimator that attains these bounds. Furthermore, by orthogonalizing the RIS phase profiles across different users, we circumvent inter-path interference.

We consider the problem of estimating the 3D orientation of a user, using the downlink mmWave signals received from multiple base stations. We show that the received signals from several base stations, having known positions, can be used to estimate the unknown orientation of the user. We formulate the estimation problem as a maximum likelihood estimation problem in the the manifold of rotation matrices. In order to provide an initial estimate to solve the non-linear non-convex optimization problem, we resort to a least squares estimation problem that exploits the underlying geometry. Our numerical results show that the problem of orientation estimation can be solved when the signals from at least two base stations are received. We also provide the orientation lower error bound, showing a narrow gap between the performance of the proposed estimators and the bound.

We consider a single-anchor multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system with imperfectly synchronized transmitter (Tx) and receiver (Rx) clocks, where the Rx estimates its position based on the received reference signals. The Tx, having (imperfect) prior knowledge about the Rx location and the surrounding geometry, transmits the reference signals based on a set of fixed beams. In this work, we develop strategies for the power allocation among the beams aiming to minimize the expected Cram\'er-Rao lower bound (CRLB) for Rx positioning. Additional constraints on the design are included to ensure that the line-of-sight (LOS) path is detected with high probability. Furthermore, the effect of clock asynchronism on the resulting allocation strategies is also studied. We also propose a gridless compressed sensing-based position estimation algorithm, which exploits the information on the clock offset provided by non-line-of-sight paths, and show that it is asymptotically efficient., Comment: 30 pages, 6 figures, submitted to IEEE Transactions on Wireless Communications

In the context of positioning a target with a single-anchor, this contribution focuses on the Fisher information about the position, orientation and clock offset of the target provided by single-bounce reflections. The availability of prior knowledge of the target's environment is taken into account via a prior distribution of the position of virtual anchors, and the rank, intensity and direction of provided information is studied. We show that when no prior knowledge is available, single-bounce reflections offer position information in the direction parallel to the reflecting surface, irrespective of the target's and anchor's locations. We provide a geometrically intuitive explanation of the results and present numerical examples demonstrating their potential implications., Comment: 5 pages, 5 figures, submitted to IEEE Wireless Communications Letters

We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cram\'er-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-RIS-UE 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., Comment: Submitted to ICC 2021

MmWave multiple-input single-output (MISO) systems using a single-antenna receiver are regarded as promising solution for the near future, before the full-fledged 5G MIMO will be widespread. However, for MISO systems synchronization cannot be performed jointly with user localization unless two-way transmissions are used. In this paper we show that thanks to the use of a reconfigurable intelligent surface (RIS), joint localization and synchronization is possible with only downlink MISO transmissions. The direct maximum likelihood (ML) estimator for the position and clock offset is derived. To obtain a good initialization for the ML optimization, a decoupled, relaxed estimator of position and delays is also devised, which does not require knowledge of the clock offset. Results show that the proposed approach attains the Cram\'er-Rao lower bound even for moderate values of the system parameters.

Intentional interference, and in particular GNSS spoofing, is currently one of the most significant concerns of the Positioning, Navigation and Timing (PNT) community. With the adoption of Open Service Navigation Message Authentication (OSNMA) in Galileo, the E1B signal component will continuously broadcast unpredictable cryptographic data. This allows GNSS receivers not only to ensure the authenticity of data origin but also to detect replay spoofing attacks for receivers already tracking real signals with relatively good visibility conditions. Since the spoofer needs to estimate the unpredictable bits introduced by OSNMA with almost zero delay in order to perform a Security Code Estimation and Replay (SCER) attack, the spoofer unavoidably introduces a slight distortion into the signal, which can be the basis of a spoofing detector. In this work, we propose five detectors based on partial correlations of GNSS signals obtained over predictable and unpredictable parts of the signals. We evaluate them in a wide set of test cases, including different types of receiver and spoofing conditions. The results show that one of the detectors is consistently superior to the others, and it is able to detect SCER attacks with a high probability even in favorable conditions for the spoofer. Finally, we discuss some practical considerations for implementing the proposed detector in receivers, in particular when the Galileo OSNMA message structure is used.

Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity. With the introduction of reconfigurable intelligent surfaces (RISs), new opportunities arise, in particular when the RIS is functioning as a lens receiver. We investigate the localization of a transmitter using a RIS-based lens in close proximity to a single receive antenna element attached to reception radio frequency chain. We perform a Fisher information analysis, evaluate the impact of different lens configurations, and propose a two-stage localization algorithm. Our results indicate that positional beamforming can lead to better performance when a priori location information is available, while random beamforming is preferred when a priori information is lacking. Our simulation results for a moderate size lens operating at 28 GHz showcased that decimeter-level accuracy can be attained within 3 meters to the lens.

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.

This white paper explores future localization and sensing opportunities for beyond 5G wireless communication systems by identifying key technology enablers and discussing their underlying challenges, implementation issues, and identifying potential solutions. In addition, we present exciting new opportunities for localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Following the trend initiated in the 5G NR systems, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler and angular resolutions, as well as localization to cm-level degree of accuracy. Moreover, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. 6G will be truly intelligent wireless systems that will not only provide ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency and space. This white paper concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust. Addressing these challenges will undoubtedly require an inter-disciplinary and concerted effort from the research community., Comment: 38 pages, 12 figures, white paper

We consider channel estimation for an uplink massive multiple-input multiple-output (MIMO) system where the base station (BS) uses an array with low-resolution (1-2 bit) analog-to-digital converters and a spatial Sigma-Delta ($\Sigma\Delta$) architecture to shape the quantization noise away from users in some angular sector. We develop a linear minimum mean squared error (LMMSE) channel estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. We also analyze the uplink achievable rate with maximal ratio combining (MRC), zero-forcing (ZF), and LMMSE receivers and provide a lower bound for the achievable rate with the MRC receiver. Numerical results show superior channel estimation and sum spectral efficiency performance using the $\Sigma\Delta$ architecture compared to conventional 1- or 2-bit quantized massive MIMO systems., Comment: Revision

The one-bit spatial Sigma-Delta concept has recently been proposed as an approach for achieving low distortion and low power consumption for massive multi-input multi-output systems. The approach exploits users located in known angular sectors or spatial oversampling to shape the quantization noise away from desired directions of arrival. While reducing the antenna spacing alleviates the adverse impact of quantization noise, it can potentially deteriorate the performance of the massive array due to excessive mutual coupling. In this paper, we analyze the impact of mutual coupling on the uplink spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture, and compare the resulting performance degradation to standard one-bit quantization as well as the ideal case with infinite precision. Our simulations show that the one-bit Sigma-Delta array is particularly advantageous in space-constrained scenarios, can still provide significant gains even in the presence of mutual coupling when the antennas are closely spaced., Comment: Presented in SPAWC 2020

Standalone Global Navigation Satellite Systems (GNSS) are known to provide positioning accuracy of a few meters in open sky conditions. This accuracy can drop significantly when the line-of-sight (LOS) paths to some GNSS satellites are obstructed, e.g., in urban canyons or underground tunnels. To overcome this issue, the general approach is usually to augment GNSS systems with other dedicated subsystems to help cover the gaps arising from obscured LOS. Positioning in 5G has attracted some attention lately, mainly due to the possibility to provide cm-level accuracy using 5G signals and infrastructure, effectively imposing no additional cost. In this paper, we study the hybridization of GNSS and 5G positioning in terms of achievable position and velocity error bounds. We focus on scenarios where satellite visibility is constrained by the environment geometry, and where the GNSS and 5G positioning systems fail to perform individually or provide a prohibitively large error., Comment: This paper has been accepted for publications in the proceedings of the IEEE/ION Position Location and Navigation Symposium (PLANS) 2020. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org

We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the Sigma-Delta approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the Sigma-Delta array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the Sigma-Delta approach for both MRC and zero-forcing receivers., Comment: Published in IEEE JSAC

The application of cooperative localization in vehicular networks is attractive to improve accuracy and coverage. Conventional distance measurements between vehicles are limited by the need for synchronization and provide no heading information of the vehicle. To address this, we present a cooperative localization algorithm using posterior linearization belief propagation (PLBP) utilizing angle-of-arrival (AoA)-only measurements. Simulation results show that both directional and positional root mean squared error (RMSE) of vehicles can be decreased significantly and converge to a low value in a few iterations. Furthermore, the influence of parameters for the vehicular network, such as vehicle density, communication radius, prior uncertainty and AoA measurements noise, is analyzed., Comment: Submitted for possible publication to an IEEE conference

In this work, we study optimal transmit strategies for minimizing the positioning error bound in a line-of-sight scenario, under different levels of prior knowledge of the channel parameters. For the case of perfect prior knowledge, we prove that two beams are optimal, and determine their beam directions and optimal power allocation. For the imperfect prior knowledge case, we compute the optimal power allocation among the beams of a codebook for two different robustness-related objectives, namely average or maximum squared position error bound minimization. Our numerical results show that our low-complexity approach can outperform existing methods that entail higher signaling and computational overhead., Comment: accepted for publication at IEEE GLOBECOM 2019

5G new radio (NR) provides new opportunities for accurate positioning from a single reference station: large bandwidth combined with multiple antennas, at both the base station and user sides, allows for unparalleled angle and delay resolution. Nevertheless, positioning quality is affected by multipath and clock biases. We study, in terms of performance bounds and algorithms, the ability to localize a vehicle in the presence of multipath and unknown user clock bias. We find that when a sufficient number of paths is present, a vehicle can still be localized thanks to redundancy in the geometric constraints. Moreover, the 5G NR signals enable a vehicle to build up a map of the environment., Comment: Globecom 2018 paper with corrected figure # 7 (RMSE now significantly higher than CRB)

The paper addresses the problem of determining the unknown position of a mobile station for a mmWave MISO system. This setup is motivated by the fact that massive arrays will be initially implemented only on 5G base stations, likely leaving mobile stations with one antenna. The maximum likelihood solution to this problem is devised based on the time of flight and angle of departure of received downlink signals. While positioning in the uplink would rely on angle of arrival, it presents scalability limitations that are avoided in the downlink. To circumvent the multidimensional optimization of the optimal joint estimator, we propose two novel approaches amenable to practical implementation thanks to their reduced complexity. A thorough analysis, which includes the derivation of relevant Cram\'er-Rao lower bounds, shows that it is possible to achieve quasi-optimal performance even in presence of few transmissions, low SNRs, and multipath propagation effects.

This paper tackles the problem of finding the optimal non-coherent detector for the reacquisition of weak Global Navigation Satellite System (GNSS) signals in the presence of bits and phase uncertainty. Two solutions are derived based on using two detection frameworks: the Bayesian approach and the generalized likelihood ratio test (GLRT). We also derive approximate detectors of reduced computation complexity and without noticeable performance degradation. Simulation results reveal a clear improvement of the detection probability for the proposed techniques with respect to the conventional detectors implemented in high sensitivity GNSS (HS-GNSS) receivers to acquire weak GNSS signals. Finally, we draw conclusions on which is the best technique to reacquire weak GNSS signals in practice considering a trade-off between performance and complexity., Comment: 18 pages, 6 figures

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location of the user terminals are largely undiscovered. In this paper, we propose a novel support detection-based channel training method for frequency selective millimeter-wave (mm-wave) multiple-input-multiple-output system with lens antenna arrays. We show that accurate position and orientation estimation and tracking is possible using signals from a single transmitter with lens antenna arrays. Particularly, the beamspace channel estimation is formulated as two sparse signal recovery problems in the downlink and uplink for the estimation of angle-of-arrival, angle-of-departure, and time-of-arrival. The proposed method offers a higher sparse detection probability compared to the compressed sensing based solutions. Finally, a joint heuristic beamformer design and user position and orientation tracking approach are proposed based on initial estimation of channel parameters obtained in the training phase., Comment: 30 pages, 8 figures, journal paper. Submitted to IEEE Transactions on Wireless Communications