arXiv subjects

Henk Wymeersch

Publications and source records attributed to Henk Wymeersch.

At least 199 records · Page 11Linked to original sources

arXiv2022

Joint RIS Calibration and Multi-User Positioning

Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform. In most of the research works so far, RIS has been treated as a passive base station (BS) with a known state, in terms of its location and orientation, to boost the communication and/or terminal positioning performance. However, such performance gains cannot be guaranteed anymore when the RIS state is not perfectly known. In this paper, by taking the RIS state uncertainty into account, we formulate and study the performance of a joint RIS calibration and user positioning (JrCUP) scheme. From the Fisher information perspective, we formulate the JrCUP problem in a network-centric single-input multiple-output (SIMO) scenario with a single BS, and derive the analytical lower bound for the states of both user and RIS. We also demonstrate the geometric impact of different user locations on the JrCUP performance while also characterizing the performance under different RIS sizes. Finally, the study is extended to a multi-user scenario, shown to further improve the state estimation performance.

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arXiv2022

3D Localization with a Single Partially-Connected Receiving RIS: Positioning Error Analysis and Algorithmic Design

In this paper, we introduce the concept of partially-connected Receiving Reconfigurable Intelligent Surfaces (R-RISs), which refers to metasurfaces designed to efficiently sense electromagnetic waveforms impinging on them, and perform localization of the users emitting them. The presented R-RIS hardware architecture comprises subarrays of meta-atoms, with each of them incorporating a waveguide assigned to direct the waveforms reaching its meta-atoms to a reception Radio-Frequency (RF) chain, enabling signal/channel parameter estimation. We particularly focus on the scenarios where the user is located in the far-field of all the R-RIS subarrays, and present a three-Dimensional (3D) localization method which is based on narrowband signaling and Angle of Arrival (AoA) estimates of the impinging signals at each single-receive-RF R-RIS subarray. For the AoA estimation, which relies on spatially sampled versions of the received signals via each subarray's phase configuration of meta-atoms, we devise an off-grid atomic norm minimization approach, which is followed by subspace-based root MUltiple SIgnal Classification (MUSIC). The AoA estimates are finally combined via a least-squared line intersection method to obtain the position coordinates of a user emitting synchronized localization pilots. Our derived theoretical Cramér Rao Lower Bounds (CRLBs) on the estimation parameters, which are compared with extensive computer simulation results of our localization approach, verify the effectiveness of the proposed R-RIS-empowered 3D localization system, which is showcased to offer cm-level positioning accuracy. Our comprehensive performance evaluations also demonstrate the impact of various system parameters on the localization performance, namely the training overhead and the distance between the R-RIS and the user, as well as the spacing among the R-RIS's subarrays and its partitioning patterns.

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arXiv2022

Spatial Signal Design for Positioning via End-to-End Learning

This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a multiple-input single-output (MISO) scenario, we propose a novel autoencoder (AE) architecture to estimate user-equipment(UE) position with multiple base-stations (BSs) and demonstrate that end-to-end learning can match model-based design, both for angle of departure (AoD) and position estimation, under ideal conditions without model deficits and outperform it in the presence of hardware impairments.

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arXiv2022

MmWave 6D Radio Localization with a Snapshot Observation from a Single BS

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 Cramér-Rao bound (CRB) is tightened using the ML estimation.

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arXiv2022

MmWave Mapping and SLAM for 5G and Beyond

Device localization and radar-like mapping are at the heart of integrated sensing and communication, enabling not only new services and applications, but can also improve communication quality with reduced overheads. These forms of sensing are however susceptible to data association problems, due to the unknown relation between measurements and detected objects or targets. In this chapter, we provide an overview of the fundamental tools used to solve mapping, tracking, and simultaneous localization and mapping (SLAM) problems. We distinguish the different types of sensing problems and then focus on mapping and SLAM as running examples. Starting from the applicable models and definitions, we describe the different algorithmic approaches, with a particular focus on how to deal with data association problems. In particular, methods based on random finite set theory and Bayesian graphical models are introduced in detail. A numerical study with synthetic and experimental data is then used to compare these approaches in a variety of scenarios.

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arXiv2022

End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models.

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arXiv2022

On the Impact of Phase Noise on Monostatic Sensing in OFDM ISAC Systems

Phase noise (PN) can become a major bottleneck for integrated sensing and communications (ISAC) systems towards 6G wireless networks. In this paper, we consider an OFDM ISAC system with oscillator imperfections and investigate the impact of PN on monostatic sensing performance by performing a misspecified Cramér-Rao bound (MCRB) analysis. Simulations are carried out under a wide variety of operating conditions with regard to SNR, oscillator type (free-running oscillators (FROs) and phase-locked loops (PLLs)), 3-dB bandwidth of the oscillator spectrum, PLL loop bandwidth and target range. The results provide valuable insights on when PN leads to a significant degradation in range and/or velocity accuracy, establishing important guidelines for hardware and algorithm design in 6G ISAC systems.

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arXiv2022

RIS-Enabled SISO Localization under User Mobility and Spatial-Wideband Effects

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.

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arXiv2022

A Tutorial on Terahertz-Band Localization for 6G Communication Systems

Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond. Instead of being a byproduct of the communication system, localization in 6G is indispensable for location-aware communications. Towards this end, we aim to identify the prospects, challenges, and requirements of THz localization techniques. We first review the history and trends of localization methods and discuss their objectives, constraints, and applications in contemporary communication systems. We then detail the latest advances in THz communications and introduce the THz-specific channel and system models. Afterward, we formulate THz-band localization as a 3D position/orientation estimation problem, detailing geometry-based localization techniques and describing potential THz localization and sensing extensions. We further formulate the offline design and online optimization of THz localization systems, provide numerical simulation results, and conclude by providing lessons learned and future research directions. Preliminary results illustrate that under the same transmission power and array footprint, THz-based localization outperforms millimeter wave-based localization. In other words, the same level of localization performance can be achieved at THz-band with less transmission power or a smaller footprint.

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arXiv2022

Bayesian Integrity Monitoring for Cellular Positioning -- A Simplified Case Study

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.

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arXiv2022

Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities

Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio based positioning and sensing. These will be utilized not only for conventional location-aware services and for enhancing communication performance, but also to support new use case families with extreme performance requirements. This paper presents a unified vision from stakeholders across the value chain in terms of both opportunities and challenges for 6G positioning and sensing, as well as use cases, performance requirements, and gap analysis. Combined, this motivates the technical advances in 6G and guides system design.

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arXiv2022

Compressed-Sensing-Based 3D Localization with Distributed Passive Reconfigurable Intelligent Surfaces

In this paper, the programmable signal propagation paradigm, enabled by Reconfigurable Intelligent Surfaces (RISs), is exploited for high accuracy $3$-Dimensional (3D) user localization with a single multi-antenna base station. Capitalizing on the tunable reflection capability of passive RISs, we present a two-stage user localization method leveraging the multi-reflection wireless environment. In the first stage, we deploy an off-grid compressive sensing approach, which is based on the atomic norm minimization, for estimating the angles of arrival associated with each RIS, which is followed, in the second stage, by a maximum likelihood location estimation initialized with a least-squares line intersection technique. The presented numerical results showcase the high accuracy of the proposed 3D localization method, verifying our theoretical Cramér Rao lower bound analysis.

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arXiv2022

Analysis of V2X Sidelink Positioning in sub-6 GHz

Radio positioning is an important part of joint communication and sensing in beyond 5G communication systems. Existing works mainly focus on the mmWave bands and under-utilize the sub-6 GHz bands, even though it is promising for accurate positioning, especially when the multipath is uncomplicated, and meaningful in several important use cases. In this paper, we analyze V2X sidelink positioning and propose a new performance bound that can predict the positioning performance in the presence of severe multipath. Simulation results using ray-tracing data demonstrate the possibility of sidelink positioning, and the efficacy of the new performance bound and its relation with the complexity of the multipath.

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arXiv2022

Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface

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 Cramér-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.

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arXiv2022

RIS-aided Near-Field Localization under Phase-Dependent Amplitude Variations

We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.

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arXiv2022

Monostatic Sensing with OFDM under Phase Noise: From Mitigation to Exploitation

We consider the problem of monostatic radar sensing with orthogonal frequency-division multiplexing (OFDM) joint radar-communications (JRC) systems in the presence of phase noise (PN) caused by oscillator imperfections. We begin by providing a rigorous statistical characterization of PN in the radar receiver over multiple OFDM symbols for free-running oscillators (FROs) and phase-locked loops (PLLs). Based on the delay-dependent PN covariance matrix, we derive the hybrid maximum-likelihood (ML)/maximum a-posteriori (MAP) estimator of the deterministic delay-Doppler parameters and the random PN, resulting in a challenging high-dimensional nonlinear optimization problem. To circumvent the nonlinearity of PN, we then develop an iterated small angle approximation (ISAA) algorithm that progressively refines delay-Doppler-PN estimates via closed-form updates of PN as a function of delay-Doppler at each iteration. Moreover, unlike existing approaches where PN is considered to be purely an impairment that has to be mitigated, we propose to exploit PN for resolving range ambiguity by capitalizing on its delay-dependent statistics (i.e., the range correlation effect), through the formulation of a parametric Toeplitz-block Toeplitz covariance matrix reconstruction problem. Simulation results indicate quick convergence of ISAA to the hybrid Cramér-Rao bound (CRB), as well as its remarkable performance gains over state-of-the-art benchmarks, for both FROs and PLLs under various operating conditions, while showing that the detrimental effect of PN can be turned into an advantage for sensing.

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arXiv2022

Localization Coverage Analysis of THz Communication Systems with a 3D Array

This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations. We derive the Cramér-Rao Bound for the localization problem and define the coverage of the considered system. We compare the error lower bound distributions of the conventional planar array and the 3D array configurations at different user equipment (UE) positions and orientations. Our numerical results obtained for array configurations with an equal number of elements show very limited coverage of the planar-array configuration, especially across the UE orientation range. Conversely, a 3D array configuration offers an overall higher coverage with minor performance loss in certain UE positions and orientations.

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arXiv2022

Wideband mmWave Massive MIMO Channel Estimation and Localization

Spatial wideband effects are known to affect channel estimation and localization performance in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Based on perturbation analysis, we show that the spatial wideband effect is in fact more pronounced than previously thought and significantly degrades performance, even at moderate bandwidths, if it is not properly considered in the algorithm design. We propose a novel channel estimation method based on multidimensional ESPRIT per subcarrier, combined with unsupervised learning for pairing across subcarriers, which shows significant performance gain over existing schemes under wideband conditions.

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