Non-line-of-sight Research Articles

Hybrid TOA/AOA Localization for Indoor Multipath-assisted Next-Generation Wireless Networks

The next-generation wireless communication systems (NGWCs) necessitate integrating effective communication and localization techniques. However, the multipath badly affects both communication and localization, especially in indoor NGWCs. Therefore, we propose a novel hybrid localization method based on time and angle-based ranging estimation to address this issue. This method employs an improved matrix enhancement matrix pencil algorithm (IMEMP) to obtain the time of arrival (TOA) and corresponding angle of arrival (AOA) of each path. Two distinct localization methods are presented: First arrival path-based maximum likelihood (ML) and scatterer-assisted. Both methods are designed to mitigate the adverse effects of serious non-line-of-sight (NLOS) scenarios. The proposed scatterer-assisted method demonstrates a noteworthy 20% improvement in performance compared to scenarios where only the first path is considered, particularly in specific areas. These findings highlight the potential of the proposed approach to enhance localization accuracy in challenging wireless environments, offering valuable insights for the advancement of NGWCs.

Динамическое использование спектра в LTE и NR для развертывания 5G

Both Static allocation of the frequency spectrum as deployed in 4G-LTE as well as dynamically configured 5G NR physical layer channels does not satisfy the demand of current exponential growth of wireless users. As 5G NR will perform better in Line of Sight (LOS) scenarios while 4G-LTE provides remarkable performance in non-Line of sight (NLOS) scenarios. Dynamic Spectrum Sharing (DSS) allows operators to run LTE and 5G NR on the same band simultaneously. This paper illustrates the relationship and working theory of Dynamic Spectrum Sharing to comprehend how the latest 5G standard will result in a substantial improvement in average user throughput and improved network efficiencies. This is an effort to propose a generic framework to initial Deployment of 5G network.

Attitude solving aided UWB/IMU integrated algorithm with real-time NLOS suppression

In response to the challenges posed by non-line-of-sight (NLOS) errors and inadequate attitude estimation accuracy in ultra-wideband and inertial measurement unit (UWB/IMU) integrated navigation algorithms in the complex environment, a robust UWB/IMU integrated positioning scheme is proposed. On one hand, the utilization of the robust local weighted regression algorithm (RLWR) is employed to mitigate the impact of NLOS errors on UWB data. RLWR incorporates information from nodes with known pseudo-range within local time intervals into the regression model, enhancing the identification of NLOS errors and improving positioning accuracy. On the other hand, the variational Bayesian filter algorithm based on adaptive conjugate gradient descent (ACGD) is proposed to improve the accuracy of IMU attitude calculation. The algorithm leverages an ACGD approach to optimize the attitude output of the accelerometer and magnetometer. The output is then incorporated into the variational Bayesian filtering system alongside the gyroscopic attitude output compensated by integrated positioning. Compared to conventional quaternion calculation and gradient descent linear filtering methods, the approach exhibits superior precision and stability. The experimental findings demonstrate that the amalgamation of the proposed NLSO identification suppression algorithm and the enhanced attitude computation algorithm confers significant advantages in terms of both localization accuracy and attitude estimation precision in complex environments. Moreover, the robust solution presented in the paper ensures the preservation of filter performance in the event of UWB measurement failure.

A Combined UWB/IMU Localization Method with Improved CKF.

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg-Marquardt algorithm (L-M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.

Channel modeling for NLoS visible light networks with integrated sensing and communication

Inspired by the advanced integrated sensing and communication (ISAC), in this Letter, we explore the non-line-of-sight (NLoS) optical channels formed by reflections from the ground or objects to establish an integrated channel model for simultaneous communication and sensing. The integrated channel model can, on the one hand, perceive the changes in the surrounding environment and, on the other hand, determine whether these changes positively or negatively affect the quality of communication simultaneously. To validate the effectiveness of the proposed model, from sensing, we analyze the impact of various floor materials and visible light communication (VLC) users on the integrated channel; from communication, we characterize the influence of perceived environmental changes on communication performance by calculating throughput. Experimental results confirm the capability of the derived model, which can support the design and deployment of VL-based ISAC networks.

Plug-and-Play Algorithms for Dynamic Non-line-of-sight Imaging

Plug-and-Play Algorithms for Dynamic Non-line-of-sight Imaging

MDE and LLM Synergy for Network Experimentation: Case Analysis of Wireless System Performance in Beaulieu-Xie Fading and κ-µ Co-Channel Interference Environment with Diversity Combining.

Channel modeling is a first step towards the successful projecting of any wireless communication system. Hence, in this paper, we analyze the performance at the output of a multi-branch selection combining (SC) diversity receiver in a wireless environment that has been distracted by fading and co-channel interference (CCI), whereby the fading is modelled by newer Beaulieu-Xie (BX) distribution, and the CCI is modelled by the κ-µ distribution. The BX distribution provides the ability to include in consideration any number of line-of-sight (LOS) useful signal components and non-LOS (NLOS) useful signal components. This distribution contains characteristics of some other fading models thanks to its flexible fading parameters, which also applies to the κ-µ distribution. We derived here the expressions for the probability density function (PDF) and cumulative distribution function (CDF) for the output signal-to-co-channel interference ratio (SIR). After that, other performances are obtained, namely: outage probability (Pout), channel capacity (CC), moment-generating function (MGF), average bit error probability (ABEP), level crossing rate (LCR), and average fade duration (AFD). Numerical results are presented in several graphs versus the SIR for different values of fading and CCI parameters, as well as the number of input branches in the SC receiver. Then, the impact of parameters on all performance is checked. From our numerical results, it is possible to directly obtain the performance for all derived and displayed quantities for cases of previously known distributions of fading and CCI by inserting the appropriate parameter values. In the second part of the paper, a workflow for automated network experimentation relying on the synergy of Large Language Models (LLMs) and model-driven engineering (MDE) is presented, while the previously derived expressions are used for evaluation. Due to the aforementioned, the biggest value of the obtained results is the applicability to the cases of a large number of other distributions for fading and CCI by replacing the corresponding parameters in the formulas for the respective performances.

Confidential multiple-input multiple-output optical camera communication aided by a two-dimensional pilot.

Optical camera communication (OCC) has attracted increased attention for its inherent security advantage. However, there still exists the risk of eavesdropping on the broadcasting channel of OCC. To achieve confidential communication, we propose the confidentiality-interference dual light-emitting diode (LED) communication (CIDLC) scheme at the transmitter (TX) and elimination of interference (EI) scheme at the receiver (RX). Meanwhile, interference signals refer to the bit shift of confidential signals. Further, we propose the two-dimensional pilot-aided channel estimation (2D-PACE) scheme to enhance the reliability of multiple-input multiple-output (MIMO) OCC. Experiment results validate the effectiveness of our schemes, which guarantee confidentiality while performing well at a 2 m non-line-of-sight (NLOS) distance. Finally, the communication-illumination integration OCC is constructed via the energy equalization coding (EEC) scheme.

A novel LiDAR-GNSS-INS Two-Phase Tightly Coupled integration scheme for precise navigation

Abstract. Recent advances in precise navigation have extensively utilized the integration of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), particularly in the domain of intelligent vehicles. However, the efficacy of such navigation systems is considerably compromised by the reflection and multipath disruptions of non-light-of-sight (NLOS) signals. Light Detection and Ranging (LiDAR)-based odometry, an active perception-based sensor known for its precise 3D measurements, has become increasingly prevalent in augmenting navigation systems. Nonetheless, the assimilation of LiDAR odometry with GNSS/INS systems presents substantial challenges. Addressing these challenges, this study introduces a two-phase sensor fusion (TPSF) approach that synergistically combines GNSS positioning, LiDAR odometry, and IMU pre-integration through a dual-stage sensor fusion process. The initial stage employs an Extended Kalman Filter (EKF) to amalgamate the GNSS solution with IMU Mechanization, facilitating the estimation of IMU biases and system initialization. Subsequently, the second stage integrates scan-to-map LiDAR odometry with IMU mechanization to support continuous LiDAR factor estimation. Factor graph optimization (FGO) is then utilized for the comprehensive fusion of LiDAR factors, IMU pre-integration, and GNSS solutions. The efficacy of the proposed methodology is corroborated through rigorous testing on a demanding trajectory from an urbanized open-source dataset, with the system demonstrating a notable enhancement in performance compared to the state-of-the-art algorithms, achieving a translational Standard Deviation (STD) of 1.269 meters.

CoopeRIS: A framework for the simulation of reconfigurable intelligent surfaces in cooperative driving environments

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

NLOS Identification and Mitigation for Time-based Indoor Localization Systems: Survey and Future Research Directions

One hurdle to accurate indoor localization using time-based networks is the presence of Non-Line-Of-Sight (NLOS) and multipath signals, affecting the accuracy of ranging in indoor environments. NLOS identification and mitigation have been studied over the years and applied to different time-based networks, with most works considering NLOS links with WiFi and UWB channels. In this paper, we discuss the effects and challenges of NLOS conditions on indoor localization and present current state-of-the-art approaches to NLOS identification and mitigation in literature. We survey these approaches and classify them under different categories together with their merits and demerits. We further categorize approaches to tackle NLOS effects into single and hybrid measurement-based approaches in this work. Lessons learnt from the survey with future directions are also presented in this paper.

The application of gated recurrent unit algorithm with fused attention mechanism in UWB indoor localization

Amidst the increasing reliance on indoor positioning in areas such as large shopping malls, airports, and train stations, the precision of ultra-wideband (UWB) technology stands out for its centimeter-level accuracy. However, non-line-of-sight (NLOS) errors caused by people’s movements significantly reduce indoor positioning accuracy. In response, this paper presents an innovative blend of gated recurrent unit algorithm with fused attention mechanism (GRU_Attention). This algorithm adeptly captures the dynamism in UWB signals and intelligently focuses on pivotal features to actualize accurate three-dimensional positioning while effectively reducing NLOS error. It has been demonstrated that GRU_Attention algorithm can reach 5.6 cm accuracy level through experiments, which improved by 74.47 %, 87.44 %, 14.46 %, 43.82 %, and 29.07 % respectively compared with the backpropagation (BP), least square (LS), GRU, convolutional neural network (CNN), and fused attention mechanism convolutional neural network (CNN_Attention) algorithms. This algorithm charts a new pathway to overcome NLOS errors in indoor personnel movement scenes.

Fingerprint-Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface-Assisted Networks

The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment.

Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoors and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appear due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, the effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes an NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 m in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

Fast and Fault-Tolerant Passive Hyperbolic Localization Using Sensor Consensus

The accuracy of passive hyperbolic localization applications using Time Difference of Arrival (TDOA) measurements can be severely compromised in non-line-of-sight (NLOS) situations. Consensus functions have been successfully used to provide robust and accurate location estimates in such challenging situations. In this paper, a fast branch-and-bound computational method for finding the global maximum of consensus functions is proposed and the global convergence property of the algorithm is mathematically proven. The performance of the method is illustrated by simulation experiments and real measurements.

Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems

Hybrid beamforming (HBF) has been regarded as one of the most promising technologies in millimeter Wave (mmWave) communication systems. In order to guarantee the communication quality in non-line-of-sight (NLOS) scenarios, joint HBF design for the mmWave amplify-and-forward (AF) relay communication system is studied in this paper. The ideal case is first considered where the mmWave half-duplex (HD) AF relay system operates with channel state information (CSI) accurately known. In order to tackle the non-convex problem, a manifold optimization (MO)-based alternating optimization algorithm is proposed, where an optimization problem containing only constant modulus constraints in Euclidean space can be converted to an unconstrained optimization problem in a Riemann manifold. Furthermore, considering more practical cases with estimation errors of CSI, we investigate the robust joint HBF design with the system operating in full-duplex (FD) mode to obtain higher spectral efficiency (SE). A null-space projection (NP) based self-interference cancellation (SIC) algorithm is developed to attenuate the self-interference (SI). Different from the traditional SI suppression algorithm, there’s no limit on the number of RF chains. Numerical results reveal that our proposed algorithms has a good convergence and can effectively deal with the influence of different CSI estimation errors. A significant performance improvement can be achieved in contrast with other approaches.

Investigation of Characteristics of Ultraviolet Light Pulse Weak Signal Communication System Based on Fourth-Order Frequency-Shift Keying Modulation

In ultraviolet (UV) communication, On–Off Keying (OOK) is the primary modulation technique. Compared to OOK, frequency modulation offers stronger resistance to path attenuation. Currently, research on frequency modulation demodulation schemes for UV communication is limited, mainly employing waveform detection and laser pulse response methods, which require high detection sensitivity to light. This study presents a novel frequency modulation communication scheme using discrete Poisson channel distribution and optical pulse signal processing algorithms, enhancing the signal processing sensitivity of the existing frequency modulation scheme to the level of photons. The proposed system model is rigorously evaluated through theoretical derivations and simulations. Additionally, a hardware system integrating optical pulse counting, frequency detection, and clock data recovery algorithms is developed. Experimental results show the system achieving a 5 kbps transmission rate under frequency modulation. In non-line-of-sight (NLOS) scenarios, communication reaches up to 65 m, with the receiver elevation angle ranging from 10° to 25° and the bit error rate (BER) stabilized at 10−4, while in line-of-sight (LOS) situations, the BER remains at 10−5 up to 400 m and 10−4 up to 700 m, achieving the farthest distance and fastest communication rate achievable in the current FSK modulation scheme of ultraviolet communication systems. The integrated components enhance its applicability in communication systems. This study offers a valuable addition to UV communication technology.

A Post-Processing Multipath/NLoS Bias Estimation Method Based on DBSCAN.

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth.

A Pseudo-Satellite Fingerprint Localization Method Based on Discriminative Deep Belief Networks

Pseudo-satellite technology has excellent compatibility with the BDS satellite navigation system in terms of signal systems. It can serve as a stable and reliable positioning signal source in signal-blocking environments. User terminals can achieve continuous high-precision positioning both indoors and outdoors without any modification to the navigation module. As a result, pseudo-satellite indoor positioning has gradually emerged as a research hotspot in the field. However, due to the complex and variable indoor radio propagation environment, signal propagation is interfered with by noise, multipath, non-line-of-sight (NLOS) propagation, etc. The geometric relation-based localization algorithm cannot be applied in indoor non-line-of-sight environments. Therefore, this paper proposes a pseudo-satellite fingerprint localization method based on the discriminative deep belief networks (DDBNs). The method acquires the model parameters of pseudo-satellite multi-carrier noise density signal strength in non-line-of-sight indoor spaces through a greedy unsupervised learning method and gradient descent-supervised learning method. It establishes a mapping relationship between the implied features of the pseudo-satellite multi-carrier noise density signal strength and indoor location, enabling pseudo-satellite fingerprint matching localization in indoor non-line-of-sight environments. In this paper, the performance of the positioning algorithm is verified in dynamic and static scenarios through numerous experiments in a laboratory environment. Compared to the commonly used localization algorithms based on fingerprint library matching, the results demonstrate that, in indoor non-line-of-sight test conditions, the system’s 2D static positioning has a maximum error of less than 0.24 m, an RMSE better than 0.12 m, and a 2σ (95.4%) positioning error better than 0.19 m. For 2D dynamic positioning, the maximum error is less than 0.36 m, the average error is 0.23 m, and the 2σ positioning error is better than 0.26 m. These results effectively tackle the challenge of pseudo-satellite indoor positioning in non-line-of-sight environments.