arXiv subjects

Benjamin J. B. Deutschmann

Publications and source records attributed to Benjamin J. B. Deutschmann.

At least 19 recordsLinked to original sources

arXiv2026

Posterior Cramér-Rao Bounds on Localization and Mapping Errors in Distributed MIMO SLAM

Radio-frequency simultaneous localization and mapping (RF-SLAM) methods jointly infer the position of mobile transmitters and receivers in wireless networks, together with a geometric map of the propagation environment. An inferred map of specular surfaces can be used to exploit non-line-of-sight components of the multipath channel to increase robustness, bypass obstructions, and improve overall communication and positioning performance. While performance bounds for user location are well established, the literature lacks performance bounds for map information. This paper derives the mapping error bound (MEB), i.e., the posterior Cramér-Rao lower bound on the position and orientation of specular surfaces, for RF-SLAM. In particular, we consider a very general scenario with single- and double-bounce reflections, as well as distributed anchors. We demonstrate numerically that a state-of-the-art RF-SLAM algorithm asymptotically converges to this MEB. The bounds assess not only the localization (position and orientation) but also the mapping performance of RF-SLAM algorithms in terms of global features.

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arXiv2026

Coherent Direct D-MIMO Localization: Analysis of Coherence Levels

Distributed multiple-input multiple-output (D-MIMO) offers favorable geometry for localization and sensing, with its greatest potential arising from joint coherent processing across panels. However, stringent frequency-synchronization and phase-calibration requirements, together with multimodal likelihood functions, complicate the estimation problem. Consequently, most existing methods process the panels noncoherently, potentially sacrificing localization accuracy. We present a unified family of Bayesian state filters based on concentrated Type-I and marginal Type-II likelihoods for wideband near-field D-MIMO systems. The Type-I filters realize (i) noncoherent, (ii) coherent, and (iii) carrier-phase-based processing. We show that a zero-mean Type-II model is inherently noncoherent under distributed processing, whereas observation stacking restores coherence. A recently proposed nonzero-mean Type-II model adapts to the coherence available in the data, a property termed ``soft coherence.'' We derive posterior Cramér--Rao lower bounds (PCRLBs) for all three coherence levels and show that each level is fundamentally tied to the number of phase parameters used for positioning or treated as nuisance parameters. Numerical results show that the filters closely approach their respective PCRLBs and that coherent processing substantially outperforms noncoherent processing. Our particle-based implementations parallelize over particles and panels, scale linearly with the observed data, and achieve runtimes of tens of milliseconds per time step under GPU acceleration.

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arXiv2026

Spatiotemporal Synchronization of Distributed Arrays using Particle-Based Loopy Belief Propagation

Sensing and imaging with distributed radio infrastructures (e.g., distributed MIMO, wireless sensor networks, multistatic radar) rely on knowledge of the positions, orientations, and clock parameters of distributed apertures. We extend a particle-based loopy belief propagation (BP) algorithm to cooperatively synchronize distributed agents to anchors in space and time. Substituting marginalization over nuisance parameters with approximate but closed-form concentration, we derive an efficient estimator that bypasses the need for preliminary channel estimation and operates directly on noisy channel observations. Our algorithm demonstrates scalable, accurate spatiotemporal synchronization on simulated data.

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arXiv2026

XL-MIMO Channel Modeling and Prediction for Wireless Power Transfer

Massive antenna arrays form physically large apertures with a beam-focusing capability, leading to outstanding wireless power transfer (WPT) efficiency paired with low radiation levels outside the focusing region. However, leveraging these features requires accurate knowledge of the multipath propagation channel and overcoming the (Rayleigh) fading channel present in typical application scenarios. For that, reciprocity-based beamforming is an optimal solution that estimates the actual channel gains from pilot transmissions on the uplink. But this solution is unsuitable for passive backscatter nodes that are not capable of sending any pilots in the initial access phase. Using measured channel data from an extremely large-scale MIMO (XL-MIMO) testbed, we compare geometry-based planar wavefront and spherical wavefront beamformers with a reciprocity-based beamformer, to address this initial access problem. We also show that we can predict specular multipath components (SMCs) based only on geometric environment information. We demonstrate that a transmit power of 1W is sufficient to transfer more than 1mW of power to a device located at a distance of 12.3m when using a (40x25) array at 3.8GHz. The geometry-based beamformer exploiting predicted SMCs suffers a loss of only 2dB compared with perfect channel state information.

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arXiv2026

Physically Large Apertures for Wireless Power Transfer: Performance and Regulatory Aspects

Wireless power transfer (WPT) is a promising service for the Internet of Things, providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side from a conventional transmit antenna with a physically small aperture decays rapidly with the distance. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a physically large aperture, i.e., large with respect to the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. Physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety.

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arXiv2026

Soft-Coherent Direct Multipath SLAM

Challenging indoor and urban environments with severe multipath propagation and obstructed line-of-sight degrade classical radio positioning. Multipath-based simultaneous localization and mapping (MP-SLAM) addresses this by building and exploiting propagation maps for robust localization. Emerging distributed multiple-input multiple-output (D-MIMO)/extremely large-scale MIMO (XL-MIMO) infrastructures provide large spatial apertures and high-resolution sensing, especially when phase coherence is maintained across base stations, subarrays, or distributed arrays. We propose a scalable Bayesian direct MP-SLAM method for coherent data fusion in D-MIMO/XL-MIMO systems that jointly infers the environment while performing robust, high-accuracy localization directly from raw radio signals. While commonly used zero-mean Type-II likelihood functions inherently lead to noncoherent processing across distributed arrays and thus to aperture loss, the proposed phase-preserving nonzero-mean Type-II likelihood shares a complex mean across distributed arrays. This enables coherent fusion and preserves the distributed aperture gain, while the variance captures noncoherent signal power. The method is combined with a surface model that enables map-feature fusion across the distributed infrastructure and supports near-field propagation and visibility effects. Bayesian inference is performed using belief propagation by means of the sum-product algorithm on a factor graph with particle-based messages. Parallelizing over particles and arrays, the GPU-accelerated implementation achieves millisecond-level runtimes even in large or distributed infrastructures. Simulation results show that the proposed method achieves performance gains over existing noncoherent methods and approaches the corresponding posterior CRLB, highlighting the potential of coherent processing for high-resolution sensing and localization.

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arXiv2026

Adaptive Multipath-Based SLAM for Distributed MIMO Systems

Localizing users and mapping the environment using radio signals is a key task in emerging applications such as low-latency communications and safety-critical navigation. Recently introduced multipath-based SLAM methods can jointly localize a mobile agent and map reflective surfaces in radio frequency (RF) environments. Most existing methods assume that map features and their corresponding RF propagation paths are statistically independent. This assumption neglects inherent dependencies that arise when a single reflective surface contributes to multiple propagation paths or when an agent communicates with multiple base stations. Existing approaches that aim to fuse information across propagation paths are further limited by their inability to perform ray tracing in RF environments with nonconvex geometries. In this paper, we propose a Bayesian multipath-based SLAM method for distributed MIMO systems that addresses these limitations. We exploit amplitude statistics to establish adaptive, time-varying detection probabilities. Based on the resulting 'soft' ray-tracing strategy, the proposed method can fuse information across propagation paths in RF environments with nonconvex geometries. A Bayesian estimation framework for the joint estimation of map features and agent state is developed by applying the message passing rules of the sum-product algorithm to a factor graph representation of the proposed statistical model. We further introduce a new initialization procedure for reflective surfaces that enables the introduction of new surface states even when measurements arise solely from double-bounce paths. The proposed method is validated using both synthetic and real RF measurements obtained in challenging scenarios with nonconvex geometries and OLoS conditions. The results demonstrate that it provides accurate localization and mapping performance and approaches the posterior CRLBs.

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arXiv2025

Geometry-Based Channel Estimation, Prediction, and Fusion

Reciprocity-based beamforming-most commonly employed in time-division duplexing-uses noisy, estimated (i.e., measured) channel state information (CSI) acquired on the uplink. While computationally efficient, reciprocity-based beamforming suffers severe losses under (i) low signal-to-noise ratio (SNR) and (ii) user mobility because it ignores the underlying physics of the radio channel beyond its reciprocity. Based on a physics-driven geometry-based channel model, we propose a method that jointly infers the mobile user's position and environment map on the uplink. It then leverages the estimated user position and environment map to predict CSI on the downlink. We demonstrate significant efficiency gains under both (i) low SNR and (ii) user mobility on measured data. While the user position may allow efficient beamforming in strong line-of-sight (LoS) channels, inferring an environment map allows bypassing obstructed LoS conditions using non-LoS beamforming via multipath components. We further propose "channel fusion," a probabilistic (Bayesian) combination of estimated and predicted CSI, which increases the beamforming robustness, particularly when either source of CSI is unreliable. Notably, this approach shares similarities with the minimum mean square error (MMSE) channel estimator, with geometry-based prior parameters inferred from the data.

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arXiv2024

Experimental Study on the Effect of Synchronization Accuracy for Near-Field RF Wireless Power Transfer in Multi-Antenna Systems

Wireless power transfer (WPT) technologies hold promise for enhancing device autonomy, particularly for energy-limited IoT systems. This paper presents experimental results on coherent and non-coherent transmit diversity approaches for WPT, tested in the near field using the Techtile testbed. We demonstrate that a fully synchronized beamfocusing system achieves a 14 dB gain over non-coherent transmission, consistent with the theoretical 14.9 dB gain for a 31-element array. Additionally, phase alignment errors below 20° result in less than 1 dB of gain loss, while errors exceeding 40° lead to losses over 3 dB. These findings suggest that phase coherency requirements for WPT can be relaxed, and that scaling the number of antennas is a promising strategy for improving power transfer efficiency.

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arXiv2024

Single versus Multi-Tone Wireless Power Transfer with Physically Large Array

Distributed beamforming is a key enabler to provide power wirelessly to a massive amount of energy-neutral devices (ENDs). However, without prior information and fully depleted ENDs, initially powering these devices efficiently is an open question. This work investigates and assesses the feasibility of harvesting sufficient energy to transmit a backscatter pilot signal from the END, which can be then used for coherent downlink transmission. We experimentally evaluated adaptive single-tone and multi-tone signals during initial charging. The results indicate that the response time for ENDs with unknown locations can extend to several tens of seconds. Notably, the adaptive single-tone excitation shows, among others, better performance at lower transmit power levels, providing a faster response. These findings underscore the potential of adaptive single-tone signals in optimizing power delivery to END in future networks.

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arXiv2024

Joint Localization, Synchronization and Mapping via Phase-Coherent Distributed Arrays

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.

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arXiv2024

Accurate Direct Positioning in Distributed MIMO Using Delay-Doppler Channel Measurements

Distributed multiple-input multiple-output (D-MIMO) is a promising technology for simultaneous communication and positioning. However, phase synchronization between multiple access points in D-MIMO is challenging and methods that function without the need for phase synchronization are highly desired. Therefore, we present a method for D-MIMO that performs direct positioning of a moving device based on the delay-Doppler characteristics of the channel state information (CSI). Our method relies on particle-filter-based Bayesian inference with a state-space model. We use recent measurements from a sub-6 GHz D-MIMO OFDM system in an industrial environment to demonstrate near-centimeter accuracy under partial line-of-sight (LoS) conditions and decimeter accuracy under fully obstructed LoS.

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arXiv2024

Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment

Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths available in the higher bands have inspired much work regarding sensing in the mmWave and sub-THz bands; however, the sub-6 GHz cellular bands will still be the main provider of wide cellular coverage due to the more favorable propagation conditions. In this paper, we present a measurement system and results of sub-6 GHz distributed MIMO measurements performed in an industrial environment. From the measurements, we evaluated the diversity for both large-scale and small-scale fading and characterized the link reliability. We also analyzed the possibility of multistatic sensing and positioning of users in the environment, with the initial results showing a mean-square error below 20 cm on the estimated position. Further, the results clearly showed that new channel models are needed that are spatially consistent and deal with the nonstationary channel properties among the antennas.

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arXiv2024

Keeping Energy-Neutral Devices Operational: a Coherent Massive Beamforming Approach

Keeping the batteries on the shelf: this is the holy grail for low-cost Internet of Things (IoT) nodes. In this paper we study the potential of radio frequency (RF)-based wireless power transfer implementing coherent beamforming with many antennas to realize this ambitious target. We optimize the deployment of the antennas to charge electronic shelf labels (ESLs), considering actual regulatory constraints. The results confirm the feasibility to create power spots that are sufficient to keep the high density of battery-less devices operational.

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arXiv2023

An Open Dataset Storage Standard for 6G Testbeds

The emergence of sixth-generation (6G) networks has spurred the development of novel testbeds, including sub-THz networks, cell-free systems, and 6G simulators. To maximize the benefits of these systems, it is crucial to make the generated data publicly available and easily reusable by others. Although data sharing has become a common practice, a lack of standardization hinders data accessibility and interoperability. In this study, we propose the Dataset Storage Standard (DSS) to address these challenges by facilitating data exchange and enabling convenient processing script creation in a testbed-agnostic manner. DSS supports both experimental and simulated data, allowing researchers to employ the same processing scripts and tools across different datasets. Unlike existing standardization efforts such as SigMF and NI RF Data Recording API, DSS provides a broader scope by accommodating a common definition file for testbeds and is not limited to RF data storage. The dataset format utilizes a hierarchical structure, with a tensor representation for specific experiment scenarios. In summary, DSS offers a comprehensive and flexible framework for enhancing the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) in 6G testbeds, promoting open and efficient data sharing in the research community.

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arXiv2023

Location-based Initial Access for Wireless Power Transfer with Physically Large Arrays

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for 6G use cases. It enables a massive, yet sustainable deployment of batteryless energy neutral (EN) devices at unprecedented scale. Recent research on 6G is exploring high operating frequencies up to the THz spectrum, where antenna arrays with large apertures are capable of forming narrow, "laser-like" beams. At sub-10 GHz frequencies, physically large antenna arrays are considered that are operating in the array near field. Transmitting spherical wavefronts, power can be focused in a focal point rather than a beam, which allows for efficient and radiation-safe WPT. We formulate a multipath channel model comprising specular components and diffuse scattering to find the WPT power budget in a realistic indoor scenario. Specular components can be predicted by means of a geometric model. This is used to transmit power via multiple beams simultaneously, increasing the available power budget and expanding the initial access distance. We show that exploiting this "beam diversity" reduces the required fading margin for the initial access to EN devices.

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arXiv2023

Propagation Modeling for Physically Large Arrays: Measurements and Multipath Component Visibility

This paper deals with propagation and channel modeling for physically large arrays. The focus lies on acquiring a spatially consistent model, which is essential, especially for positioning and sensing applications. Ultra-wideband, synthetic array measurement data have been acquired with large positioning devices to support this research. We present a modified multipath channel model that accounts for a varying visibility of multipath components along a large array. Based on a geometric model of the measurement environment, we analyze the visibility of specular components. We show that, depending on the size of the reflecting surface, geometric visibility and amplitude estimates obtained with a super-resolution channel estimation algorithm show a strong correspondence. Furthermore, we highlight the capabilities of the developed synthetic array measurement system.

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arXiv2023

Bistatic MIMO Radar Sensing of Specularly Reflecting Surfaces for Wireless Power Transfer

Geometric environment information aids future distributed radio infrastructures in providing services, such as ultra-reliable communication, positioning, and wireless power transfer (WPT). An a priori known environment model cannot always be assumed in practice. This paper investigates the capabilities of detecting specularly reflecting surfaces in a bistatic multiple-input multiple-output (MIMO) radar setup operating at sub-10 GHz frequencies. While rough surfaces generate diffuse reflections originating from their actual position, flat surfaces act like "mirrors," causing directive reflections that virtually originate "behind" them. Despite these propagation characteristics, we can estimate the locations of flat metal walls from reflections at their surface using synthetic aperture (SA) measurements. The performance gain achievable by exploiting this environment information is analyzed by evaluating WPT capabilities in a geometry-based beamforming setup. We show that it is possible to predict channel state information (CSI) with a geometric channel model. Our geometry-based beamformer suffers an efficiency loss of only 1.1dB compared with a reciprocity-based beamformer given perfect CSI.

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