Non-line-of-sight Research Articles

A Non-Stationary Model With Time-Space Consistency for 6G Massive MIMO mmWave UAV Channels

In this paper, a novel unmanned aerial vehicle (UAV) space-time-frequency (S-T-F) non-stationary channel model with time-space consistency for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. In the proposed model, the line-of-sight (LoS) transmission and non-LoS (NLoS) transmission through ground reflection, single-clusters, and twin-clusters are modeled. Meanwhile, the three-dimensional (3D) continuously arbitrary trajectory and the self-rotation of UAV are imitated. To capture the time-space consistency and S-T-F non-stationarity simultaneously, a new UAV-related non-stationary modeling algorithm that integrates the visibility region (VR), frequency-dependent path gain, and survival probability is developed for the first time. In this algorithm, the UAV-related parameters are considered, including UAV’s height, 3D moving velocity, and self-rotation angles. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the cluster density index influenced by communication scenarios, frequency, UAV’s height, and the distance between transceivers. Some important channel statistical properties, such as S-T-F correlation function (STF-CF), Doppler power spectral density (DPSD), and stationary interval, are derived. Finally, simulation results match well with ray-tracing-based results, which verifies the utility of the proposed model.

Reliably Route IoT Packets in Software Defined mmWave Mesh Networks

Although millimeter wave (mmWave) mesh networks provide large bandwidth to deliver IoT packets, link failure caused by non-light-of-sight (NLoS) condition degrades network reliability. With the assistance of software defined networking (SDN), we propose a Fast link failure Recovery Routing (FastReRoute) framework to instantly re-route packets from the failed links to another ones minimizing packet loss. Upon a link fails, FastReRoute first re-routes packets via the pre-configured local backup path. FastReRoute conditionally re-routes packets again according to the management using the SDN controller. We integrate FastReRoute into Mininet for performance evaluation. This demonstrates FastReRoute effectively sustains the network reliability.

THz Band Channel Measurements and Statistical Modeling for Urban D2D Environments

THz band is envisioned to be used in 6G systems to meet the ever-increasing demand for data rate. However, before an eventual system design and deployment can proceed, detailed channel sounding measurements are required to understand key channel characteristics. In this paper, we present a first extensive set of channel measurements for urban outdoor environments that are ultra-wideband, and double-directional where both transmitter and receiver are at the same height. In all, we present measurements at 38 locations, consisting of nearly 50,000 impulse responses, representing both line-of-sight (LoS) and non-line-of-sight (NLoS) cases in the 1-100 m range between 145-146 GHz. We provide modeling for path loss, shadowing, delay spread, angular spread, multipath component (MPC) power distribution and Qtapnumber. We find, among other things, that outdoor communication over tens of meters is feasible in this frequency range even in NLoS scenarios, that omni-directional delay spreads of up to 100 ns, and directional delay spreads of up to 10 ns are observed, while angular spreads are also quite significant, and a surprisingly large number of MPCs are observed for 1 GHz bandwidth and 13° beamwidth. These results constitute an important first step towards better understanding the wireless channel in the THz band.

NN-LCS: Neural Network and Linear Coordinate Solver Fusion Method for UWB Localization in Car Keyless Entry System.

Nowadays, ultra-wideband (UWB) technology is becoming a new approach to localize keyfobs in the car keyless entry system (KES), because it provides precise localization and secure communication. However, for vehicles the distance ranging suffers from great errors because of none-line-of-sight (NLOS) which is raised by the car. Regarding the NLOS problem, efforts have been made to mitigate the point-to-point ranging error or to estimate the tag coordinate by neural networks. However, it still suffers from some problems such as low accuracy, overfitting, or a large number of parameters. In order to address these problems, we propose a fusion method of a neural network and linear coordinate solver (NN-LCS). We use two FC layers to extract the distance feature and received signal strength (RSS) feature, respectively, and a multi-layer perceptron (MLP) to estimate the distances with the fusion of these two features. We prove that the least square method which supports error loss backpropagation in the neural network is feasible for distance correcting learning. Therefore, our model is end-to-end and directly outputs the localization results. The results show that the proposed method is high-accuracy and with small model size which could be easily deployed on embedded devices with low computing ability.

Performance of Multipulse Pulse-Position Modulation in NLOS Ultraviolet Communications

The performance of multipulse pulse-position modulation (MPPM) in non-line-of-sight (NLOS) ultraviolet communications (UVC) with output signals of detectors following Poisson distribution and Poisson-triggered Gaussian (PTG) distribution has been investigated. Specifically, the probabilities of a given number of incorrectly identified pulsed or empty slots in an MPPM symbol have been derived with output signals Poisson and PTG distributed. Based on those probabilities, the expressions of symbol-error rate and achievable data rate of MPPM in the NLOS UVC systems have been further obtained. Finally, Monte-Carlo simulations are presented to verify the theoretical results.

Active IRS-Assisted Integrated Sensing and Communication in C-RAN

This letter investigates an active intelligent reflecting surface (IRS)-aided integrated sensing and communication (ISAC) system in the scenario of cloud radio access network (C-RAN). In the system, the active IRS is deployed to amplify and reflect the joint communication and sensing signals from the remote radio heads (RRHs). Our goal is to form the desired reflective beam pattern from the active IRS towards the sensing targets located in non-line-of-sight (NLoS) areas for the RRHs, while satisfying the signal-to-interference-plus-noise ratio (SINR) requirement for the communication users and the capacity limit for the fronthaul links between the baseband unit (BBU) pool and RRHs. Specifically, we propose an efficient joint design for the fronthaul compression and the beamforming of the RRHs and the active IRS. Via numerical simulations, the effectiveness of the proposed design is verified.

Event-based dual photography for transparent scene reconstruction.

Light transport contains all light information between a light source and an image sensor. As an important application of light transport, dual photography has been a popular research topic, but it is challenged by long acquisition time, low signal-to-noise ratio, and the storage or processing of a large number of measurements. In this Letter, we propose a novel hardware setup that combines a flying-spot micro-electro mechanical system (MEMS) modulated projector with an event camera to implement dual photography for 3D scanning in both line-of-sight (LoS) and non-line-of-sight (NLoS) scenes with a transparent object. In particular, we achieved depth extraction from the LoS scenes and 3D reconstruction of the object in a NLoS scene using event light transport.

UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter.

Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system.

5G V2X Performance Comparison for Different Channel Coding Schemes and Propagation Models

Channel coding is a fundamental procedure in wireless telecommunication systems and has a strong impact on the data transmission quality. This effect becomes more important when the transmission must be characterised by low latency and low bit error rate, as in the case of vehicle-to-everything (V2X) services. Thus, V2X services must use powerful and efficient coding schemes. In this paper, we thoroughly examine the performance of the most important channel coding schemes in V2X services. More specifically, the impact of use of 4th-Generation Long-Term Evolution (4G-LTE) turbo codes, 5th-Generation New Radio (5G-NR) polar codes and low-density parity-check codes (LDPC) in V2X communication systems is researched. For this purpose, we employ stochastic propagation models that simulate the cases of line of sight (LOS), non-line of sight (NLOS) and line of sight with vehicle blockage (NLOSv) communication. Different communication scenarios are investigated in urban and highway environments using the 3rd-Generation Partnership Project (3GPP) parameters for the stochastic models. Based on these propagation models, we investigate the performance of the communication channels in terms of bit error rate (BER) and frame error rate (FER) performance for different levels of signal to noise ratio (SNR) for all the aforementioned coding schemes and three small V2X-compatible data frames. Our analysis shows that turbo-based coding schemes have superior BER and FER performance than 5G coding schemes for the vast majority of the considered simulation scenarios. This fact, combined with the low-complexity requirements of turbo schemes for small data frames, makes them more suitable for small-frame 5G V2X services.

LOS Signal Identification Based on Common Chord Intersection Point Position Deviation from MS in Target Localization

Target localization has been a popular research topic in recent years since it is the basis of all kinds of location-based applications. For GNSS-denied urban or indoor environments, the localization method based on time-of-arrival (TOA) is one of the most popular localization methods due to its high accuracy and simplicity. However, the Non-line-of-sight (NLOS) error is the major cause that degrades the accuracy of the TOA-based localization method. Identifying whether a received signal at a base station (BS) is due to a line-of-sight (LOS) transmission or NLOS is the key to TOA-based localization methods. In the popular LOS signal identification methods, compared with statistic signal methods and machine learning methods, the geometric constraint method has the advantages of simplicity and without requiring priori knowledge of signals and large amounts of training datasets. In this paper, we propose a geometric constraint two-step LOS signal identification method based on common chord intersection point position deviation from mobile stations (MS). In the first step, all BSs are divided into multiple BS combinations with every three BSs, the TOA distance error of each BS combination is estimated based on common chord intersection point position deviation from MS, the BS combinations whose TOA distance error satisfy Gaussian distribution are roughly identified as LOS BS combination and enter the second step, the other BS combinations are discarded as NLOS BS combination. In the second step, based on mutual distance threshold and discrimination result matrix, common chord intersection points of LOS BS combination, and corresponding LOS BS combinations are identified. The BSs of LOS BS combinations are identified as LOS BS and the signals received at LOS BS are identified as LOS signal ultimately. Compared with the other two geometric constraint methods, the proposed algorithm has better identification accuracy, and the setting of the identification threshold value has a theoretical basis, which facilitates the application of the proposed algorithm.

Radar Target Localization with Multipath Exploitation in Dense Clutter Environments

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.

A robust indoor localization algorithm based on polynomial fitting and Gaussian mixed model

Wireless sensor network (WSN) positioning has a good effect on indoor positioning, so it has received extensive attention in the field of positioning. Non-line-of sight (NLOS) is a primary challenge in indoor complex environment. In this paper, a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error. Firstly, fitting polynomials are used to predict the measured values. The residuals of predicted and measured values are clustered by Gaussian mixture model (GMM). The LOS probability and NLOS probability are calculated according to the clustering centers. The measured values are filtered by Kalman filter (KF), variable parameter unscented Kalman filter (VPUKF) and variable parameter particle filter (VPPF) in turn. The distance value processed by KF and VPUKF and the distance value processed by KF, VPUKF and VPPF are combined according to probability. Finally, the maximum likelihood method is used to calculate the position coordinate estimation. Through simulation comparison, the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper. And it shows strong robustness in strong NLOS environment.

On-Body NLoS Radio Channel at Millimeter-Wave Frequencies

This article presents an analysis of on-body radio channels at millimeter-wave frequencies and possible solutions to reduce the path loss, particularly, at non-line of sight (NLoS) locations. We have chosen two specific channels around the wrist and waist, and a comprehensive study has been conducted at the frequency of 94 GHz. It is observed that in the presence of textile, a slow wave mode can be excited tangentially near the surface of human body. Clothing and the thin air between the clothing and the human body surface may attribute to the reduction of path loss at NLoS. Our findings show that the presence of different garment materials is linked to the variation of path loss in on-body radio channels. Moreover, we demonstrate numerically that by attaching thin metallic sheet underneath clothing may further reduce the path loss. In our parametric study, a maximum of 15 dB drop occurs in path loss at only 30 cm distance. A robust theoretical model based on the knife edge diffraction (KED) method has been applied to give physical insights of such a unique radio propagation mechanism, that can lead to abundant applications in 5G communications and beyond.

Channel State Identification in Complex Indoor Environments With ST-CNN and Transfer Learning

Accurate channel state identification can improve the accuracy of ultrawideband (UWB) indoor positioning. In complex indoor environments, existing channel state identification methods suffer from high costs. Meanwhile, these methods pay little attention to the different localization errors caused by weak -non-line-of-sight (WLOS) and non-line-of-sight (NLOS). In this letter, a channel state identification method that can identify line-of-sight (LOS), WLOS, NLOS is proposed for use in a single indoor scenario based on the Stockwell transform and convolutional neural networks (ST-CNN). Moreover, combined with transfer learning (TL), ST-CNN can reduce the cost of identification in complex indoor environments. Simulations based on open source datasets show that ST-CNN can achieves F2 scores of 97.17% and 97.14% in NLOS and WLOS. For complex indoor environments consisting of multiple indoor scenarios, compared with the ST-CNN, the ST-CNN+TL method can reduce the training time by 62% and the training data by 40%.

The Degrees-of-Freedom in Monostatic ISAC Channels: NLoS Exploitation vs. Reduction

The degrees of freedom (DoFs) attained in monostatic integrated sensing and communications (ISAC) are analyzed. Specifically, monostatic sensing aims for extracting target-orientation information from the line of sight (LoS) channel between the transmitter and the target, since the Non-LoS (NLoS) paths only contain clutter or interference. By contrast, in wireless communications, typically, both the LoS and NLoS paths are exploited for achieving diversity or multiplexing gains. Hence, we shed light on the NLoS exploitation vs. reduction tradeoffs in a monostatic ISAC scenario. In particular, we optimize the transmit power of each signal path to maximize the communication rate, while guaranteeing the sensing performance for the target. The non-convex problem formulated is firstly solved in closed form for a single-NLoS-link scenario, then we harness the popular successive convex approximation (SCA) method for a general multiple-NLoS-link scenario. Our simulation results characterize the fundamental performance tradeoffs between sensing and communication, demonstrating that the available DoFs in the ISAC channel should be efficiently exploited in a way that is distinctly different from that of communication-only scenarios.

UWB Sensor-Based Indoor LOS/NLOS Localization With Support Vector Machine Learning

Ultrawideband (UWB) sensor technology is known to achieve high-precision indoor localization accuracy in line-of-sight (LOS) environments, but its localization accuracy and stability suffer detrimentally in non-LOS (NLOS) conditions. Current NLOS/LOS identification based on channel impulse response’s (CIR) characteristic parameters (CCPs) improves location accuracy, but most CIR-based identification approaches did not sufficiently exploit the CIR information and are environment specific. This article derives three new CCPs and proposes a novel two-step identification/classification methodology with dynamic threshold comparison (DTC) and the fuzzy credibility-based support vector machine (FC-SVM). The proposed support vector machine (SVM)-based classification methodology leverages the derived CCPs obtained from the waveform and its channel analysis, which are more robust to environment and obstacles dynamic. This is achieved in two-step with a coarse-grained NLOS/LOS identification with the DTC strategy followed by FC-SVM to give the fine-grained result. Finally, based on the obtained identification results, a real-time ranging error mitigation strategy is then designed to improve the ranging and localization accuracy. Extensive experimental campaigns are conducted in different LOS/NLOS scenarios to evaluate the proposed methodology. The results show that the mean LOS/NLOS identification accuracy in various testing scenarios is 93.27%, and the LOS and NLOS recalls are 94.27% and 92.57%, respectively. The ranging errors in LOS (NLOS) conditions are reduced from 0.106 (1.442 m) to 0.065 (0.739 m), demonstrating an improvement of 38.85% (48.74%) with 0.041 (0.703 m) error reduction. In contrast, the average positioning accuracy is also reduced from 0.250 to 0.091 m with an improvement of 63.49% (0.159 m), which outperforms the state-of-the-art approaches of the least-squares SVM (LS-SVM) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithms.

A SOM-CNN Algorithm for NLOS Signal Identification

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

Multiband Sub-THz Double Angular Characterization in Indoor Scenario

In this article, we present a multiband channel characterization at sub-THz. Three measurement campaigns were conducted in an indoor environment, for different frequency ranges: 125–155, 235–265, and 270–300 GHz, by exploiting a single (vertical) polarization antenna and performing only the azimuth plane. Line-of-sight (LOS) and nonline-of-sight (NLOS), given by human body obstructions and walls, are considered. From directional measurements, the omnidirectional power angular delay profile was extracted. Modeling of large-scale parameters and cluster study are presented.

159-GHz Propagation Measurement and Analysis in Urban Microcellular Environment for 6G

This letter presents our 159 GHz measurements collected in an urban microcellular (UMi) environment and provides synthesized beamwidth-related propagation characteristics. Our directional antenna measurements include line-of-sight (LoS) scenarios (distances up to 170 m) and non-LoS (NLoS) (distances up to 97 m). To validate the synthesized beamwidth characteristics, an omnidirectional antenna was also used. So far, to the best of our knowledge, two organizations (NYU and USC) have reported their measurement-based propagation characteristics for UMi, especially including NLoS, at frequencies above 100 GHz. We compare our data with the two organizations’ data as well as the frequency-extrapolated 3GPP model.

A Dynamic Window-Based UWB-Odometer Fusion Approach for Indoor Positioning

The positioning system based on ultrawideband (UWB) can achieve centimeter-level positioning accuracy, which is now widely used for indoor positioning. However, UWB is subject to multipath effects and nonline-of-sight (NLOS), which can cause positioning errors. And a UWB positioning system with a minimum of three anchors is costly and difficult to deploy, which is not an economical option. This article presents a low-cost UWB-odometer fusion method for mobile robot localization, which enables global localization using only one UWB anchor. To improve the positioning accuracy and eliminate the influence of cumulative odometer error, we propose a dynamic window-based particle filter (DWBPF). It solves the problem of particle convergence and track loss. It also adaptively modifies the score weights according to the power difference of the UWB to achieve a more robust and accurate positioning. The experimental result shows that the system has a high positioning accuracy of 0.061 m.