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![Net position information gain-Narrower beamwidth caused by more transmit antennas (right) allows for larger net position information gain when compared to wider beamwidth (left). inter-element spacing at the transmitter and receiver that consist of N TX and N RX = 25 antennas, respectively. The operating carrier frequency is f = 38 GHz. Regarding the pilot signal, we consider an ideal sinc pulse with B = 125 MHz, E s /T s = 0 dBm, N 0 = −170dBm/Hz, and N s = 16 symbols. We consider a DFT-based beamforming matrix with N B = 50 beams uniformly spaced between [0, π). The complex channel gains for each path are generated according to a geometric model [36]. The gain is proportional to the path loss and the phase is uniformly](profile/Zohair-Abu-Shaban-2/publication/321571535/figure/fig4/AS:631421127364608@1527553936393/Net-position-information-gain-Narrower-beamwidth-caused-by-more-transmit-antennas-right.png)
Net position information gain-Narrower beamwidth caused by more transmit antennas (right) allows for larger net position information gain when compared to wider beamwidth (left). inter-element spacing at the transmitter and receiver that consist of N TX and N RX = 25 antennas, respectively. The operating carrier frequency is f = 38 GHz. Regarding the pilot signal, we consider an ideal sinc pulse with B = 125 MHz, E s /T s = 0 dBm, N 0 = −170dBm/Hz, and N s = 16 symbols. We consider a DFT-based beamforming matrix with N B = 50 beams uniformly spaced between [0, π). The complex channel gains for each path are generated according to a geometric model [36]. The gain is proportional to the path loss and the phase is uniformly
Source publication
In the past, NLOS propagation was shown to be a source of distortion for radio-based positioning systems. Every NLOS component was perceived as a perturbation which resulted from the lack of temporal and spatial resolution of previous cellular systems. Even though 5G is not yet standardized, a strong proposal, which has the potential to overcome th...
Contexts in source publication
Context 1
... First, the geometry of the scenario has a significant impact on the position information gain. The results in Fig. 2 (left) confirm our findings from the analysis on the position information gain in section IV-D. In particular, points of incidence that are close to the transmitter and receiver provide large information gains. In addition, certain angles ∆θ k invoke larger net position information gain than others. This can be deduced from the inhomogeneous color pattern in Fig. 2 ...
Context 2
... First, the geometry of the scenario has a significant impact on the position information gain. The results in Fig. 2 (left) confirm our findings from the analysis on the position information gain in section IV-D. In particular, points of incidence that are close to the transmitter and receiver provide large information gains. In addition, certain angles ∆θ k invoke larger net position information gain than others. This can be deduced from the inhomogeneous color pattern in Fig. 2 ...
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... consider the LOS path and one NLOS path in this example. The reflector, which causes the NLOS path, is moved in the x-y plane between 0 < s x,1 ≤ 10 and 0 < s y,1 ≤ 10. For every location of the point of incidence of the reflector s 1 , we determine the net position information gaiñ λ s 1 , which is depicted in log-scale in Fig. 2. The array of the mobile terminal is shown in black. We consider different transmit array sizes to highlight the effect of the beamwidth on the position information gain. In particular, we choose N TX = 25 ( Fig. 2 -left) and N TX = 150 ( Fig. 2 -right). Wider beams of the former array result in a homogeneous illumination of the plane, which makes it more obvious to point out the location dependency of the point of incidence on the net position information gain. Three main conclusions can be drawn: 11 For the numerical examples, we consider the point of incidence of a reflector as the source of the NLOS ...
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... consider the LOS path and one NLOS path in this example. The reflector, which causes the NLOS path, is moved in the x-y plane between 0 < s x,1 ≤ 10 and 0 < s y,1 ≤ 10. For every location of the point of incidence of the reflector s 1 , we determine the net position information gaiñ λ s 1 , which is depicted in log-scale in Fig. 2. The array of the mobile terminal is shown in black. We consider different transmit array sizes to highlight the effect of the beamwidth on the position information gain. In particular, we choose N TX = 25 ( Fig. 2 -left) and N TX = 150 ( Fig. 2 -right). Wider beams of the former array result in a homogeneous illumination of the plane, which makes it more obvious to point out the location dependency of the point of incidence on the net position information gain. Three main conclusions can be drawn: 11 For the numerical examples, we consider the point of incidence of a reflector as the source of the NLOS ...
Context 5
... consider the LOS path and one NLOS path in this example. The reflector, which causes the NLOS path, is moved in the x-y plane between 0 < s x,1 ≤ 10 and 0 < s y,1 ≤ 10. For every location of the point of incidence of the reflector s 1 , we determine the net position information gaiñ λ s 1 , which is depicted in log-scale in Fig. 2. The array of the mobile terminal is shown in black. We consider different transmit array sizes to highlight the effect of the beamwidth on the position information gain. In particular, we choose N TX = 25 ( Fig. 2 -left) and N TX = 150 ( Fig. 2 -right). Wider beams of the former array result in a homogeneous illumination of the plane, which makes it more obvious to point out the location dependency of the point of incidence on the net position information gain. Three main conclusions can be drawn: 11 For the numerical examples, we consider the point of incidence of a reflector as the source of the NLOS ...
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... observe that the presence of the reflector reduces the PEB by up to 25%. Narrower beams (Fig. 3 -right) result in larger reductions of the PEB. Note that the patterns in Fig. 3 closely resemble the patterns of the net position information gain in Fig. 2, i.e. when the net position information gain is large, the reduction of the PEB is also large. ...
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Background
Single user Massive Multiple Input Multiple Output (MIMO) can be used to increase the spectral efficiency since the data is transmitted simultaneously from a large number of antennas located at both the base station and mobile. It is feasible to have a large number of antennas in the mobile, in the millimeter wave frequencies. However, t...
Citations
... Furthermore, these effects can be mitigated by filtering paths with delays exceeding a specific value. Therefore, our focus is primarily on the zero-order and the first-order reflection paths [24], [25]. ...
This paper introduces an indoor localization method using fixed reflector objects within the environment, leveraging a base station (BS) equipped with Angle of Arrival (AoA) and Time of Arrival (ToA) measurement capabilities. The localization process includes two phases. In the offline phase, we identify effective reflector points within a specific region using significantly fewer test points than typical methods. In the online phase, we solve a maximization problem to locate users based on BS measurements and offline phase information. We introduce the reflectivity parameter (\(n_r\)), which quantifies the typical number of first-order reflection paths from the transmitter to the receiver, demonstrating its impact on localization accuracy. The log-scale accuracy ratio (\(R_a\)) is defined as the logarithmic function of the localization area divided by the localization ambiguity area, serving as an accuracy indicator. We show that in scenarios where the Signal-to-Noise Ratio (SNR) approaches infinity, without a line of sight (LoS) link, \(R_a\) is upper-bounded by \(n_r \log_{2}\left(1 + \frac{\mathrm{Vol}(\mathcal{S}_A)}{\mathrm{Vol}(\mathcal{S}_{\epsilon}(\mathcal{M}_s))}\right)\). Here, \(\mathrm{Vol}(\mathcal{S}_A)\) and \(\mathrm{Vol}(\mathcal{S}_{\epsilon}(\mathcal{M}_s))\) represent the areas of the localization region and the area containing all reflector points with a probability of at least \(1 - \epsilon\), respectively.
... MP-SLAM can detect and localize VAs and jointly estimate the time-varying mobile terminal (MT) position [1]- [5]. The availability of VA location information makes it possible to leverage multiple propagation paths of RF signals for MT localization, thus significantly improving localization accuracy and robustness [6]- [9]. ...
... is determined based on the Fisher information with β 2 bw denoting the mean square bandwidth of S (j) bs (f ) [7], [9], [28], [29]. The LHF of the AOA and AOD measurements are given by 1 ...
... The prediction message for legacy PVAs is based on (8) and (9). For each MT, a state transition is performed according to f (x i,n |x i,n−1 , c i,n−1 ). ...
Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach in wireless networks for obtaining position information of transmitters and receivers as well as information on the propagation environment. MP-SLAM models specular reflections of radio frequency (RF) signals at flat surfaces as virtual anchors (VAs), the mirror images of base stations (BSs). Conventional methods for MP-SLAM consider a single mobile terminal (MT) which has to be localized. The availability of additional MTs paves the way for utilizing additional information in the scenario. Specifically enabling MTs to exchange information allows for data fusion over different observations of VAs made by different MTs. Furthermore, cooperative localization becomes possible in addition to multipath-based localization. Utilizing this additional information enables more robust mapping and higher localization accuracy.
... Furthermore, the text does not mention the process of mitigating the issue. To analyses the fisher information, Mendrzik et al. [9] have stated a 5G millimetre-wave MIMO system to position the NLOS components. The equivalent Fisher information matrix (EFIM) is derived from a large number of antennas to transmit and receive data over a large bandwidth. ...
... The state-of-art analysis of the identification error rate is depicted in Fig. 8. The EFIM [9], MWT-CNN [11], CNN [12], and proposed CNN-HASSA methods are considered as the state of art methods to validate the performance of identification error rate. The results confirmed that when compared to the existing methods, the proposed CNN-HASSA method offers less identification error rate with respect to all the sets that described table 2. Analyzing the location error before and after NLOS mitigation can be accomplished by examining the Cumulative Distribution Function (CDF) alongside the location error measured in meters. ...
The widespread adoption of wireless communication has led to a rapid increase in the utilization of Multiple-Input-Multiple-Output (MIMO) technology. This advancement enables the simultaneous transmission of multiple data streams through the use of multiple transmitters and receivers. MIMO leverages the radio wave phenomenon known as multipath, where transmitted signals encounter various obstacles, arriving at the antenna at different angles and times. In 5G networks, an inherent challenge in Massive MIMO Localization arises from the non-line-of-sight (NLOS) problem. This issue significantly hampers positioning accuracy, emphasizing the need for innovative solutions. This work proposes an intelligent localization technique based on NLOS identification and mitigation. To achieve this, we first propose a Convolutional Neural network (CNN) based hybrid Archimedes based Salp Swarm Algorithm (HASSA) technique to detect the NLOS and LOS and therein estimate the location accuracy. The accuracy can be analyzed by considering the angle of arrival of signals (AOA), Threshold-based Time of arrival (TOA), Time difference of arrival (TDOA) from different antennas. Henceforth, a novel Reinforcement Learning based optimization approach is used for the mitigation of NLOS in the radio wave propagation path. We use Ensemble Deep Deterministic Policy Gradient-based approach (EDDPG) based Honey Badger algorithm (HBA) for the aforementioned process. This also reduces the computational complexity. Simulation of this approach deems different scenarios and considers different parameters and compared with different state-of-art works. From the simulation results, we observed that our proposed approach can be used for the identification and detection of the LOS and NLOS components and also precisely enhance the localization than the other approaches.
... In millimeter wave systems, the high time resolution can be achieved due to large bandwidth, and high spatial resolution can be considered in the angular domain by using a large antenna array with a very narrow beam [127][128][129]. Therefore, millimeter wave and massive systems were widely studied in signal localization [130][131][132]. ...
The development of the fifth-generation (5G) mobile communication systems has entered the commercialization stage. 5G has a high data rate, low latency, and high reliability that can meet the basic demands of most industries and daily life, such as the Internet of Things (IoT), intelligent transportation systems, positioning, and navigation. The continuous progress and development of society have aroused wide concern. Positioning accuracy is the core demand for the applications, especially in complex environments such as airports, warehouses, supermarkets, and basements. However, many factors also affect the accuracy of positioning in those environments, for example, multipath effects, non-line-of-sight, and clock synchronization errors. This paper provides a comprehensive review of the existing works about positioning for the future wireless network and discusses its key techniques and algorithms, as well as the current development and future directions. We first outline the current traditional positioning technologies and algorithms, which are discussed and analyzed with the relevant literature. In addition, we also discuss application scenarios for wireless localization. By comparing different positioning systems, the challenges and future development directions of existing wireless positioning systems are prospected.
... Localization of mobile agents using radio signals is still a challenging task in indoor or urban scenarios [2], [3]. Here, the environment is characterized by strong multipath propagation (commonly referred to as "non-line-of-sight (NLOS) propagation") and frequent obstructed line-of-sight (OLOS) situations, which can prevent the correct extraction of the information contained in the line-of-sight (LOS) component (see Fig. 1). ...
... Examining Fig. 8a, we even observe the RMSE of MP-SLAM to fall below the P-CRLB in the first part of the OLOS situation. This is possible due to the additional position information provided by the MPCs measurements (associated to the jointly inferred VAs) as investigated in [3], [68]. However, as visible in Figs. ...
We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent’s position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.
... A key aspect of this literature is the possibility of localization using a single anchor, and this research direction is termed single anchor localization. Although non-line-of-sight (NLOS) propagation provides valuable information for location awareness in single anchor localization [33], the overall performance is heavily dependent on the presence of a LOS path from the transmitter to the receiver [31]- [33]. However, an LOS path is not guaranteed at high operating frequencies, such as millimeter wave (mmWave) or terahertz, because of their high susceptibility to blockages [31]. ...
... A key aspect of this literature is the possibility of localization using a single anchor, and this research direction is termed single anchor localization. Although non-line-of-sight (NLOS) propagation provides valuable information for location awareness in single anchor localization [33], the overall performance is heavily dependent on the presence of a LOS path from the transmitter to the receiver [31]- [33]. However, an LOS path is not guaranteed at high operating frequencies, such as millimeter wave (mmWave) or terahertz, because of their high susceptibility to blockages [31]. ...
... For example, multipath signals are separable both by using orthogonal phases in the RIS elements or by using a large number of receive antennas in the single-anchor setup. Moreover, the techniques used to derive the closed-form expression of the FIM and analyze the impact of nuisance parameters are transferable from single anchor localization [31]- [33] to RIS-aided localization [41]. In [30], an alternative to two-step metrics-based AOA localization is provided by jointly processing the LOS and NLOS signals from multiple transmitters. ...
This paper presents a rigorous Bayesian analysis of the information in the signal (consisting of both the line-of-sight (LOS) path and reflections from multiple reconfigurable intelligent surfaces (RISs)) that originate from a single base station (BS) and is received by a user equipment (UE). For a comprehensive Bayesian analysis, both near and far field regimes are considered. The Bayesian analysis views both the location of the RISs and previous information about the UE as
a priori
information for UE localization. With outdated
a priori
information, the position and orientation offsets of the RISs become parameters that need to be estimated and fed back to the BS for correction. We first show that when the RIS elements have a half wavelength spacing, this RIS orientation offset is a factor in the pathloss of the RIS paths. Subsequently, we show through the Bayesian equivalent Fisher information matrix (EFIM) for the channel parameters that the RIS orientation offset cannot be corrected when there is an unknown phase offset in the received signal in the far-field regime. However, the corresponding EFIM for the channel parameters in the received signal observed in the near-field shows that this unknown phase offset does not hinder the estimation of the RIS orientation offset when the UE has more than one receive antenna. Furthermore, we use the EFIM for the UE location parameters to present bounds for UE localization in the presence of RIS uncertainty. We rigorously show that regardless of size and propagation regime, the RISs are only helpful for localization when there is
a priori
information about the location of the RISs. Finally, through numerical analysis of the EFIM and its smallest eigenvalue, we demonstrate the loss in information when the far-field model is
incorrectly
applied to the signals received at a UE experiencing near-field propagation.
... There are existing works that exploit both LoS and NLoS paths for localization, e.g., [16], [40], [41]. However, in this paper, for the sake of tractability/simplicity, we only focus on extracting the angular parameters of the LoS path for the 3D localization. ...
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.
... The implementation of high-accuracy angular measurements relies on millimeter wave massive Multiple-Input Multiple-Output (MIMO) antennas, and [8,9] showed with numerical examples that using 5G/6G millimeter wave MIMO with sufficiently high temporal and spatial resolution, it is possible to estimate position and orientation information even in NLOS environments when the corresponding reflector or scatterer is illuminated by a narrow beam. In addition, deploying RIS to improve the signal propagation environment can similarly enhance the accuracy and reliability of localization [10,11]. ...
To meet the demand for high-precision positioning in commercial industrial internet scenarios, 3GPP introduced the Positioning Reference Signal (PRS) in the 5G standard. However, the PRS signal occupies specific time and frequency resources for transmission in 5G systems, limiting the efficiency of communication signal transmission to some extent. In this regard, we propose a 5G NR Co-Band PRS model that allows for the superimposition of PRS signals on communication signals in a low-power manner, without requiring additional communication resources or causing too much interference to the communication signal. Since orthogonal frequency division multiplexing (OFDM) signals are sensitive to synchronization errors, we have developed a three-stage Co-Band PRS-based reception scheme. First, an innovative weighted window coarse synchronization method is proposed to enhance the performance of capturing communication signals at a low signal-to-noise ratio (SNR). Next, the interference cancellation technique is utilized to remove the communication signals, and the synchronization error is corrected through multipath delay estimation. Finally, to further improve the ranging accuracy, we propose an iterative delay-locked loop (DLL) algorithm that can achieve a tracking accuracy of one percent sampling. Simulation and real environment tests confirm that the proposed Co-Band PRS reception scheme can achieve a ranging accuracy of 0.16 m@90%.
... Recently, due to the ubiquitous deployment of multi-antenna base stations, single-anchor localization has been proposed and studied with [1], [2], [3] and without a reconfigurable intelligent surface (RIS) [4], [5], [6]. Localization is usually performed under the assumption that the anchor location (position and orientation) is perfectly known [7]. ...
... These bounds are extended to the case of 3D localization of an agent in [2]. In [3], the amount of information in the non-line of sight (NLOS) paths and their usefulness for localization is analyzed. The bounds of single-anchor localization with a RIS have been studied in [4]. ...
It is well known that a single anchor can be used to determine the position and orientation of an agent communicating with it. However, it is not clear what information about the anchor or the agent is necessary to perform this localization, especially when the agent is in the near-field of the anchor. Hence, in this paper, to investigate the limits of localizing an agent with some uncertainty in the anchor location, we consider a wireless link consisting of source and destination nodes. More specifically, we present a Fisher information theoretical investigation of the possibility of estimating different combinations of the source and destination's position and orientation from the signal received at the destination. To present a comprehensive study, we perform this Fisher information theoretic investigation under both the near and far field propagation models. One of the key insights is that while the source or destination's $3$D orientation can be jointly estimated with the source or destination's $3$D position in the near-field propagation regime, only the source or destination's $2$D orientation can be jointly estimated with the source or destination's $2$D position in the far-field propagation regime. Also, a simulation of the FIM indicates that in the near-field, we can estimate the source's $3$D orientation angles with no beamforming, but in the far-field, we can not estimate the source's $2$D orientation angles when no beamforming is employed.
... Some recent studies have used NLOS paths for localization and environment mapping in special reflecting scenarios [13]. Furthermore, NLOS components can improve the localization accuracy of user equipment (UE) [14], [15]. ...
... Specifically, the best UE location and velocity estimation accuracy is achieved by combining TDOA/FDOA/AOA measurements in a one-stage WLS estimator [34]. The effects of NLOS paths on UE localization have been analyzed in [14], [15]; however, the utilization of NLOS paths with WLS estimators for UE localization is still rare. In addition, localization and environment sensing are typically studied independently, where NLOS paths are used to estimate scatterer locations and velocities with known UE locations in [34] or to locate the UE with estimated scatterer locations in [40]. ...
... Although NLOS paths are proven to contain certain information about surrounding environments [12], [13] and assist in UE localization [14], [15], large pathloss in mmWave frequency bands prevents them from practical use. By contrast, the use of RISs with tunable phase shifts can enhance NLOS paths and improve localization accuracy. ...
This study explores the use of non-line-of-sight (NLOS) components in millimeter-wave (mmWave) communication systems for joint localization and environment sensing. The radar cross section (RCS) of a reconfigurable intelligent surface (RIS) is calculated to develop a general path gain model for RISs and traditional scatterers. The results show that RISs have a greater potential to assist in localization due to their ability to maintain high RCSs and create strong NLOS links. A one-stage linear weighted least squares estimator is proposed to simultaneously determine user equipment (UE) locations, velocities, and scatterer (or RIS) locations using line-of-sight (LOS) and NLOS paths. The estimator supports environment sensing and UE localization even using only NLOS paths. A second-stage estimator is also introduced to improve environment sensing accuracy by considering the nonlinear relationship between UE and scatterer locations. Simulation results demonstrate the effectiveness of the proposed estimators in rich scattering environments and the benefits of using NLOS paths for improving UE location accuracy and assisting in environment sensing. The effects of RIS number, size, and deployment on localization performance are also analyzed.
