Specular Multipath Components Research Articles

Automatic Indoor Radio Map Construction and Localization via Multipath Fingerprint Extrapolation

For fingerprint-based localization, the time-consuming and labor-intensive construction of offline radio map is the bottleneck which hinders its large-scale implementation. In this paper, we propose a radio map extrapolation and localization algorithm by exploiting the angles and delays of specular multipath components. First, using the concept of virtual anchor nodes (VANs), we calculate the virtual transmitter (VT) of each multipath component according to angle and delay information measured at a known point. By estimating the positions of reflector with these VTs, the angles and delays of the uplink multipath components transmitted at other locations in the indoor environment are extrapolated. Then, a channel fingerprint composed of the extrapolated angles and delays is proposed to represent the channel response in the angle-delay domain, which is spatially unique and discriminative. Thus, the radio map constructed such can significantly reduce the labor and time costs. Lastly, a convolutional neural network (CNN) is applied for indoor localization. The performance of the proposed fingerprint extrapolation and localization method is validated through extensive simulations with a ray-tracing channel model, which exhibits promising localization performance for our proposed scheme with reduced construction costs.

Channel Propagation Characteristics for Massive Multiple-Input/Multiple-Output Systems in a Tunnel Environment [Measurements Corner

The effect of delay spread (DS), the specular multipath components (SMCs), and the impact of dense multipath components (DMCs) on multiple-input/multiple-output (MIMO) system performance in a tunnel environment are studied by considering a Richter’s maximum-likelihood (RiMAX)-based estimator. The path loss (PL), angular spread, interuser spatial correlation, and capacity of a massive MIMO framework are analyzed at the 3.5-GHz frequency band through a measurement campaign inside a subway tunnel to implement the latest 5G standards. The radio channel consists of a uniform rectangular array of 32 transmitting elements and a uniform cylindrical array of 64 receiving elements with horizontal and vertical polarizations. The power ratio of the DMCs is found to be distance dependent, as the DMCs and SMCs behave differently in tunnels. Moreover, the study shows that the channel capacity, angular spread, and DS are reduced as the distance between the transmitting and receiving antenna arrays increases, whereas the PL exponent increases with the distance. This research highlights the importance of considering spatial characteristics in 5G massive MIMO system models and provides guidelines to enhance the networks’ accuracy in underground mobile communications.

Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization.

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers.

Robust Multipath-Assisted SLAM with Unknown Process Noise and Clutter Intensity

In multipath-assisted simultaneous localization and mapping (SLAM), the geometric association of specular multipath components based on radio signals with environmental features is used to simultaneously localize user equipment and map the environment. We must contend with two notable model parameter uncertainties in multipath-assisted SLAM: process noise and clutter intensity. Knowledge of these two parameters is critically important to multipath-assisted SLAM, the uncertainty of which will seriously affect the SLAM accuracy. Conventional multipath-assisted SLAM algorithms generally regard these model parameters as fixed and known, which cannot meet the challenges presented in complicated environments. We address this challenge by improving the belief propagation (BP)-based SLAM algorithm and proposing a robust multipath-assisted SLAM algorithm that can accommodate model mismatch in process noise and clutter intensity. Specifically, we describe the evolution of the process noise variance and clutter intensity via Markov chain models and integrate them into the factor graph representing the Bayesian model of the multipath-assisted SLAM. Then, the BP message passing algorithm is leveraged to calculate the marginal posterior distributions of the user equipment, environmental features and unknown model parameters to achieve the goals of simultaneous localization and mapping, as well as adaptively learning the process noise variance and clutter intensity. Finally, the simulation results demonstrate that the proposed approach is robust against the uncertainty of the process noise and clutter intensity and shows excellent performances in challenging indoor environments.

Gaussian Process Modeling of Specular Multipath Components

The consideration of ultra-wideband (UWB) and mm-wave signals allows for a channel description decomposed into specular multipath components (SMCs) and dense/diffuse multipath. In this paper, the amplitude and phase of SMCs are studied. Gaussian Process regression (GPR) is used as a tool to analyze and predict the SMC amplitudes and phases based on a measured training data set. In this regard, the dependency of the amplitude (and phase) on the angle-of-arrival/angle-of-departure of a multipath component is analyzed, which accounts for the incident angle and incident position of the signal at a reflecting surface—and thus for the reflection characteristics of the building material—and for the antenna gain patterns. The GPR model describes the similarities between different data points. Based on its model parameters and the training data, the amplitudes of SMCs are predicted at receiver positions that have not been measured in the experiment. The method can be used to predict a UWB channel impulse response at an arbitrary position in the environment.

Measurement‐based geometrical characterisation of the vehicle‐to‐vulnerable‐road‐user communication channel

Vehicle-to-vulnerable-road-user (V2VRU) communications have the ability to provide of awareness to both vehicles and vulnerable road users to prevent accidents. An accurate V2VRU channel model in critical accident scenarios is essential to develop a reliable communications system. Therefore, extensive wideband single-input and single-output channel measurement campaigns at 5.2 GHz were carried out in open-field and urban environments. Accident prone scenarios between a vehicle and a cyclist as well as between a vehicle and a pedestrian are considered. In this study, locations of the scatterers in the propagation environment are estimated. The authors propose a method to extract specular multipath components (MPCs) from the estimated time-variant channel impulse response based on the density of neighbouring MPCs. The specular MPCs are then tracked using a novel tracking algorithm based on the multipath component distance approach. Each path is then related to a physical scatterer in the propagation environment by employing a joint delay-Doppler estimation. According to the results, single and double bounce reflections from buildings and parked vehicles are identified in line-of-sight (LoS) situation. In non-LoS(NLoS) situation, scattering from nearby trees as well as reflections from traffic signs and lampposts beneath the trees canopy are identified.

Single-Anchor Positioning: Multipath Processing With Non-Coherent Directional Measurements

High-accuracy indoor radio positioning can be achieved by using (ultra) wideband (UWB) radio signals. Multiple fixed anchor nodes are needed to compute the position or alternatively, specular multipath components (SMCs) extracted from radio signals can be exploited. In this work, we study a multipath-based, single-anchor positioning system that acquires directional measurements non-coherently. These non-coherent measurements can be obtained, e.g., from a single-chain mm-wave transceiver with analog beam steering or from a low-complexity ultra-wideband transceiver with switched directional antennas. The directional antennas support the separation of SMCs and the suppression of the undesired diffuse multipath component (DMC) with the benefit that the required signal bandwidth can be drastically reduced. The paper analyzes the Cramer-Rao lower bound (CRLB) on the position estimation error to gain insight in the influence of the system design parameters as well as the impact of the DMC on the position error. The CRLB is compared between the non-coherent antenna setup, a conventional array with coherent processing, and a single-antenna setup. A maximum-likelihood position estimation algorithm is formulated. Its performance is evaluated with synthetically generated data as well as with UWB measurements. We show that the accuracy and robustness are significantly improved due to the processing of angular information. Analyzing the measured data for a line-of-sight link, the median error decreases from 22 down to 7 cm, the measurements better than 20 cm increase from 46 to 95 %, and outliers above 50 cm reduce from 12 to 0%.

On the Contribution of Dense Multipath Components in an Intrawagon Environment for 5G mmW Massive MIMO Channels

In this letter, the dependence of the specular multipath components (SMC) and dense multipath components (DMC) to the interuser spatial correlation and sum-rate capacity of a massive multiuser multiple-input--multiple-output (MU-MIMO) setup is evaluated from measurements conducted inside an underground subway wagon at the 25–40 GHz candidate frequency bands for 5G systems. The radio channel consists in a 7 × 7 uniform rectangular array (URA) acting as access point for eight users uniformly distributed in the wagon. The DMC power ratio is observed to be distance- and frequency-dependent as the SMC and DMC exhibit different propagation mechanisms. Remarkably, it is reported that the interuser spatial correlation computed with DMC offers the best favorable propagation for a massive MIMO setup, whereas SMC contribute to the users correlation. Hence, correlation is found to be strongly dependent on the DMC ratio frequency characteristics. In addition, better interuser correlation and sum-rate capacity values are obtained as the frequency is increased. These results highlight the need to include DMC in 5G massive MIMO channel models and emulators to improve their accuracy at the system level.

A Belief Propagation Algorithm for Multipath-Based SLAM

We present a simultaneous localization and mapping (SLAM) algorithm that is based on radio signals and the association of specular multipath components (MPCs) with geometric features. Especially in indoor scenarios, robust localization from radio signals is challenging due to diffuse multipath propagation, unknown MPC-feature association, and limited visibility of features. In our approach, specular reflections at flat surfaces are described in terms of virtual anchors (VAs) that are mirror images of the physical anchors (PAs). The positions of these VAs and possibly also of the PAs are unknown. We develop a Bayesian model of the SLAM problem and represent it by a factor graph, which enables the use of belief propagation (BP) for efficient marginalization of the joint posterior distribution. The resulting BP-based SLAM algorithm detects the VAs associated with the PAs and estimates jointly the time-varying position of the mobile agent and the positions of the VAs and possibly also of the PAs, thereby leveraging the MPCs in the radio signal for improved accuracy and robustness of agent localization. The algorithm has a low computational complexity and scales well in all relevant system parameters. Experimental results using both synthetic measurements and real ultra-wideband radio signals demonstrate the excellent performance of the algorithm in challenging indoor environments.

Massive MIMO-Based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multiple-output (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited bandwidth. The specular MPC parameters along with the parameters of the noise and the dense multipath component (DMC) are tracked using an extended Kalman filter (EKF), which enables to preserve the distance-related phase changes of the MPC complex amplitudes. The DMC comprises all non-resolvable MPCs, which occur due to finite measurement aperture. The estimation of the DMC parameters enhances the estimation quality of the specular MPCs and, therefore, also the quality of localization and mapping. The estimated MPC propagation distances are subsequently used as input to a distance-based localization and mapping algorithm. This algorithm does not need prior knowledge about the surrounding environment and base station position. The performance is demonstrated with real radio-channel measurements using an antenna array with 128 ports at the base station side and a standard cellular signal bandwidth of 40MHz. The results show that the high accuracy localization is possible even with such a low bandwidth.

Accuracy Bounds for Array-Based Positioning in Dense Multipath Channels.

The accuracy of radio-based positioning systems will be limited by multipath interference in realistic application scenarios. This paper derives closed-form expressions for the Cramér–Rao lower bound (CRLB) on the achievable time-of-arrival (ToA) and angle-of-arrival (AoA) estimation-error variances, considering the presence of multipath radio channels, and extends these results to position estimation. The derivations are based on channel models comprising deterministic, specular multipath components as well as stochastic, diffuse/dense multipath. The derived CRLBs thus allow an evaluation of the influence of channel parameters, the geometric configuration of the environment, and system parameters such as signal bandwidth and array geometry. Our results quantify how the ToA and AoA accuracies decrease when the signal bandwidth is reduced, because more multipath will then interfere with the useful LoS component. Antenna arrays can (partly) compensate this performance loss, exploiting diversity among the multipath interference. For example, the AoA accuracy with a 16-element linear array at bandwidth is similar to a two-element array at , in the magnitude order of one degree. The ToA accuracy, on the other hand, still scales by a factor of 100 from the cm-regime to the m-regime because of the dominating influence of the signal bandwidth. The position error bound shows the relationship between the range and angle information under realistic indoor channel conditions and their different scaling behaviors as a function of the anchor–agent placement. Specular multipath components have a maximum detrimental influence near the walls. It is shown for an L-shaped room that a fairly even distribution of the position error bound can be achieved throughout the environment, using two anchors equipped with -array antennas. The accuracy limit due to multipath increases from the 1–10-cm-range at bandwidth to the 0.5–1-m-range at .

Modeling the Power Angular Profile of Dense Multipath Components Using Multiple Clusters

In this paper, the power angular profile (PAP) of the dense multipath components (DMCs) was analyzed and characterized based on a channel sounding measurement campaign at 11 GHz in an indoor environment. The specular multipath components (SMCs) were estimated with the SAGE algorithm, and the RiMAX framework was applied for the estimation of the DMC in both the time-delay and the angular domain. After careful inspection of the spectrum of the residual signal components in the angular domain, we found that the DMC should be modeled by taking into account multiple angular clusters at different angles, to better characterize the diffuse scattering between transmitter and receiver, originating from multiple reflections in an environment. Therefore, we propose to extend the maximum likelihood estimation of the angular DMC parameters in the RiMAX framework so that the PAP of the DMC can be modeled with a multimodal von Mises distribution. We also validate our proposed method with the results of the measured channel sounding data.

Quantum-Assisted Indoor Localization for Uplink mm-Wave and Downlink Visible Light Communication Systems

With the proliferation of millimeter-Wave (mm-Wave) systems and visible light communications (VLCs), indoor localization may find multiple applications. When high localization accuracy is required and triangulation is not possible due to the infrastracture and scenario limitations, the computational complexity of searching on a virtual grid may become excessive. In this paper, we amalgamate uplink mm-Wave-based and downlink VLC-based localization. We employ quantum search algorithms for reducing the computational complexity required for achieving the optimal full-search-based performance. Regarding the uplink mm-Wave-based localization, we employ a single anchor equipped with multiple antenna elements and we exploit the specular multipath components created by the room’s walls. The proposed solutions outperform the state-of-the-art algorithms. Furthermore, various channel models are considered based on real indoors mm-Wave measurements. By using the VLC-based triangulation for downlink and the proposed mm-Wave-based localization algorithm for uplink, there was an average positioning error of 5.6 cm in the room considered, while requiring 261 database queries on average.

Polarimetric Distance-Dependent Models For Large Hall Scenarios

A comprehensive polarimetric distance-dependent model of the power delay profile (PDP) and path gain is proposed. The model includes both specular multipath components (SMCs) and dense multipath components (DMC), the latter being modeled with an exponential and power law. The parameters of the model were estimated from polarimetric measurements of a large hall radio channel under line-of-sight (LOS) conditions at 1.3 GHz with a dedicated procedure. The validity and robustness of the proposed approach are provided by the good agreement between the polarimetric data and models for the investigated transmitter–receiver distance range. Furthermore, the description of the radio channel with path loss models is discussed for cases where the DMC is included, and a two-step method to compute the path loss characteristics directly from the measured data is developed. The results of this contribution highlight the fact that a complete polarimetric description of all propagation mechanisms and related path loss models is desired to design faithful polarimetric radio channel models.

Evaluation of Position-Related Information in Multipath Components for Indoor Positioning

Location awareness is a key factor for a wealth of wireless indoor applications. Its provision requires the careful fusion of diverse information sources. For agents that use radio signals for localization, this information may either come from signal transmissions with respect to fixed anchors, from cooperative transmissions between agents, or from radar-like monostatic transmissions. Using a priori knowledge of a floor plan of the environment, specular multipath components can be exploited, based on a geometric-stochastic channel model. In this paper, a unified framework is presented for the quantification of this type of position-related information, using the concept of equivalent Fisher information. We derive analytical results for the Cramer–Rao lower bound of multipath-assisted positioning, considering bistatic transmissions between agents and fixed anchors, monostatic transmissions from agents, cooperative measurements between agents, and combinations thereof, including the effect of clock offsets. Awareness of this information enables highly accurate and robust indoor positioning. Computational results show the applicability of the framework for the characterization of the localization capabilities of a given environment, quantifying the influence of different system setups, signal parameters, and the impact of path overlap.

Robustness of high-resolution channel parameter estimators in presence of dense multipath components

The estimation accuracy of specular multipath components in radio channels that include dense multipath is investigated. Classical multipath estimation algorithms such as ESPRIT and SAGE do not include dense multipath in their signal model whereas recent ones, such as RiMAX, do. These estimation algorithms are applied to a priori known synthetic channels which include both specular components (SCs) and dense multipath components (DMCs). The estimation errors of the SCs are computed as a function of the DMC power to evaluate the estimator's robustness. The results of this work clearly indicate large estimation errors for the SC parameters when the estimator does not include DMCs in its data model.

Specular and diffuse measurements of multipath from various terrain surfaces at 35 GHz

Measurements of height–gain interference patterns at 35 GHz are carried out in order to obtain multipath interference data over various types of terrain viz. grass, road surface and concrete. The antenna with a beam width of 21° is used both at transmitting and receiving ends to ensure full illumination of Fresnel zones. The transmitting antenna height is kept at 1 m and receiving antenna height is vertically translated from 1 through 4 m, with a horizontal range separation of 74 m. The polarisation combination is vertical transmit and vertical receive. The system geometry provides low-grazing angle incidence. The recorded height–gain curves indicate the presence of specular and diffuse multipath components. Specular and diffuse multipath components are separated out using discrete fourier transform-based filtering technique and reflection coefficients are computed for various types of terrain covers. The results can be vitally useful for millimetre wave radar, guidance, remote sensing and communication systems operating near the surface in arid conditions.

New analytical models and probability density functions for fading in wireless communications

This paper presents new envelope probability density functions (PDFs) that describe small-scale, local area fading experienced by narrow-band wireless receivers. The paper also develops novel PDFs that describe the local area fading of two specular multipath components in the presence of other diffusely propagating waves. These PDFs are studied in the context of classical fading PDFs such as the Rayleigh, Rician, and other distributions.

Millimeter-wave specular and diffuse multipath components of terrain

Multipath interference data were obtained at a frequency of 95 GHz over pathlengths of 100-250 m by measuring height-gain interference patterns over various types of terrain. Data were collected over grass, lake, ice, snow, concrete, asphalt, and gravel surfaces. The transmit antenna was kept fixed, while the receive antenna translated vertically resulting in grazing angles between approximately 0.5-2.0°. Full illumination of the Fresnel zones was accomplished by broad-beamwidth horn antennas at both ends. Measured interference patterns indicate the presence of both specular and diffuse multipath components. A technique was developed to separate these components by filtering in the spatial Fourier-transform domain by appropriate choice of the notch frequency for the specular component and the bandpass-filter bandwidth for the diffuse components. The notch frequency is chosen according to system geometry considerations. Using this unique separation technique, specular and diffuse reflection coefficients were deduced for various terrain types. The separation technique developed in this paper can be readily applied to existing height-gain data if the system geometry is known.

Maximum-likelihood receiver for low-angle tracking radar. Part 1: The symmetric case

A new maximum-likelihood processor is described for estimating the elevation angle of a target in the combined presence of specular multipath and receiver noise, as encountered in a low-angle tracking-radar environment. The processor makes special use of the symmetry of the incident direct and specular multipath components with respect to the normal to the receiving array. The theory of the processor is supported by both computer simulation and experimental results. The effect of deviation from perfect symmetry is also examined.