Non-line-of-sight Research Articles

Non-line-of-sight ultraviolet transmission coverage in non-precipitating, foggy, and rainy weather.

Sensitivity to weather conditions is the principal limitation of free-space optical communication. However, for the scattering based ultraviolet (UV) non-line-of-sight (NLOS) communication, the atmospheric scattering effect functions as an attenuation factor and potentially as a performance enhancer. To investigate the UV NLOS transmission coverage under different weather conditions, we employ the Mie Theory in conjunction with classical aerosol and hydrometeor particle models to estimate the absorption coefficient, the scattering coefficient, and the scattering phase function. We then use these atmospheric parameters combined with a range estimation model to determine the coverage of the UV NLOS communication for specified path loss. Simulation results reveal that in non-precipitating weather, poorer visibility correlates with broader coverage. In foggy conditions, the coverage range in light fog exceeds that in fog-free environments; however, as fog intensity increases, the coverage range decreases. Rain enhances the coverage range; and heavier precipitation results in a larger coverage area.

Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.

Curved OLED-based NLOS optical camera communications links.

In this paper, for the first time, to the best of our knowledge, we experimentally demonstrate the use of a curved organic light emitting diode (OLED) as a transmitter (Tx) in the non-line-of-sight (NLOS) optical camera communication (OCC) link for an indoor environment using a camera as a receiver. The proposed NLOS-OCC scheme is evaluated for the signal-to-noise ratio (SNR) and the reception success rates R r s under key photographic and communication parameters, including exposure times t e x p and gain values G v, as well as the transmission frequency f s and the distance L. The SNR analysis is performed using a binary classification procedure based on a Gaussian mixture model for the first time, to the best of our knowledge, for OLED-based NLOS-OCC links. We also derive and demonstrate that the effect of G v on the SNR with respect to L is minimal based on the pixel illumination model. The initial analysis suggests that, for a wall reflector-based NLOS-OCC link that is 2m long, the SNR and R r s increase by 1dB and 4% (83-87%) for f s of 600Hz, with an increase in t e x p of 1000-1500µs and G v of 25-45dB.

Research on None-Line-of-Sight/Line-of-Sight Identification Method Based on Convolutional Neural Network-Channel Attention Module.

None-Line-of-Sight (NLOS) propagation of Ultra-Wideband (UWB) signals leads to a decrease in the reliability of positioning accuracy. Therefore, it is essential to identify the channel environment prior to localization to preserve the high-accuracy Line-of-Sight (LOS) ranging results and correct or reject the NLOS ranging results with positive bias. Aiming at the problem of the low accuracy and poor generalization ability of NLOS/LOS identification methods based on Channel Impulse Response (CIR) at present, the multilayer Convolutional Neural Networks (CNN) combined with Channel Attention Module (CAM) for NLOS/LOS identification method is proposed. Firstly, the CAM is embedded in the multilayer CNN to extract the time-domain data features of the original CIR. Then, the global average pooling layer is used to replace the fully connected layer for feature integration and classification output. In addition, the public dataset from the European Horizon 2020 Programme project eWINE is used to perform comparative experiments with different structural models and different identification methods. The results show that the proposed CNN-CAM model has a LOS recall of 92.29%, NLOS recall of 87.71%, accuracy of 90.00%, and F1-score of 90.22%. Compared with the current relatively advanced technology, it has better performance advantages.

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

Assessing the Performance of a Hybrid Geolocation Algorithm Integrating FP and TOA Techniques across Diverse Environmental Conditions

This paper presents a validation study of indoor geolocation accuracies using a hybrid approach that combines fingerprinting (FP) and time of arrival (TOA) techniques. The investigation focuses on three dense environments, examining the influence of furniture density and multipath components on geolocation accuracy, particularly in non-line-of-sight (NLOS) scenarios. The results indicate that geolocation performance improves in denser environments with higher furniture density due to increased multipath components. Additionally, optimizing the hybrid method with a polygon size of 50 cm and sampling rate of 80 GHz leads to further accuracy enhancements. These findings underscore the significance of furniture density and demonstrate the effectiveness of the hybrid method in addressing NLOS challenges. The research contributes to the advancement of indoor geolocation techniques and provides valuable insights for designing precise indoor positioning systems across various applications.

Weak non-line-of-sight target echoes extraction without accumulation.

Non-line-of-sight (NLOS) technology has been rapidly developed in recent years, allowing us to visualize or localize hidden objects by analyzing the returned photons, which is expected to be applied to autonomous driving, field rescue, etc. Due to the laser attenuation and multiple reflections, it is inevitable for future applications to separate the returned extremely weak signal from noise. However, current methods find signals by direct accumulation, causing noise to be accumulated simultaneously and inability of extracting weak targets. Herein, we explore two denoising methods without accumulation to detect the weak target echoes, relying on the temporal correlation feature. In one aspect, we propose a dual-detector method based on software operations to improve the detection ability for weak signals. In the other aspect, we introduce the pipeline method for NLOS target tracking in sequential histograms. Ultimately, we experimentally demonstrated these two methods and extracted the motion trajectory of the hidden object. The results may be useful for practical applications in the future.

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body-tag-anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

Reliable Data Transmission Scheduling for UAV-Assisted Air-to-Ground Communications

Reliability is significant for unmanned aerial vehicle (UAV)-assisted air-to-ground (A2G) communications. In this letter, we address the reliability optimization problem by properly scheduling data transmissions under Non-Line-of-Sight (NLoS) channel fading. We capture the channel fading effect by the Rayleigh distribution and then formulate the A2G communication reliability from a probabilistic perspective. We propose a constrained optimization model that jointly takes into account the impacts of the aerial and ground nodes' mobility, the stochastic fading characteristics of the A2G channel, and the transmission constraints. From the model, we derive a closed-form reliability-optimal solution for data transmission scheduling and theoretically characterize the optimal reliability that is achievable. Simulation results verify our theoretical results and show the superior performance of the proposed scheduling solution over benchmark methods.

Performance Evaluation of LoRaWAN SX1276 Radio in Non-Line of Sight conditions

The study presents a comprehensive performance evaluation of the SX1276 radio module within LoRaWAN technology under Non-Line of Sight (NLOS) conditions. LoRaWAN's proliferation in IoT applications demands a thorough understanding of its performance in challenging environments. This study investigates NLOS scenarios, identifying factors influencing signal strength, packet loss, and latency. Finally, the performance of the network under LOS condition was compared to NLOS to shed light on NLOS challenges. Results showed that spreading factor and transmission power are the most critical parameters that influences the performance of the network. Results from the study also shows that in LOS condition the spreading factor 7 has the highest data rate but also the least range. Furthermore, the study revealed that using high spreading factor is more beneficial than high transmission power in non-line of sight conditions. Finally, by comparing both conditions, LoRaWAN nodes under LOS condition was found to have a range of up to 5 km as against 1 km at NLOS which further highlight the importance of LOS in LoRa transmissions.

PositionNet: CNN-based GNSS positioning in urban areas with residual maps

Multipath and non-light-of-sight (NLOS) reception greatly deteriorate the GNSS positioning accuracy in urban areas. Currently, most of the attempts using machine learning in this area focus on signal status prediction (LOS, multipath, NLOS). This paper exploits the capability of deep learning in multipath/NLOS mitigation. A new input feature, the single-differenced residual map is proposed, which has a high correlation with the user location and is very effective in multipath/NLOS mitigation. Combining the domain knowledge in GNSS, features of residual maps from different satellites are extracted by the proposed network and generate the heat map to indicate the user location. The proposed network can significantly improve the positioning accuracy of 84% of the epochs in the dense urban to 5-meter level. In addition, our network has a superior generalization ability, reducing the error of 90% of the epochs to 7 m level in a new scenario.

Non-line-of-sight optical camera communications based on CPWM and a convolutional neural network.

Non-line-of-sight (NLOS) optical camera communications (OCC) exhibit greater link availability and mobility than line-of-sight links, which are more susceptible to blocking and shadowing. In this work, we propose an NLOS OCC system, where the data signal is mapped into color pulse width modulation (CPWM) symbols prior to transmission using a red-, green-, and blue light-emitting diode. A convolutional-neural-network-based receiver is used to demodulate the CPWM signal. Based on experimental results, the proposed scheme effectively mitigates the effects of diffuse reflection induced intersymbol interference, resulting in an increased data transmission rate to 7.2kbps over a link span of more than 2m, which is typical for indoor applications.

Non-line-of-sight reconstruction via structure sparsity regularization.

Non-line-of-sight (NLOS) imaging allows for the imaging of objects around a corner, which enables potential applications in various fields, such as autonomous driving, robotic vision, medical imaging, security monitoring, etc. However, the quality of reconstruction is challenged by low signal-to-noise ratio (SNR) measurements. In this study, we present a regularization method, referred to as structure sparsity (SS) regularization, for denoising in NLOS reconstruction. By exploiting the prior knowledge of structure sparseness, we incorporate nuclear norm penalization into the cost function of the directional light-cone transform (DLCT) model for the NLOS imaging system. This incorporation effectively integrates the neighborhood information associated with the directional albedo, thereby facilitating the denoising process. Subsequently, the reconstruction is achieved by optimizing a directional albedo model with SS regularization using the fast iterative shrinkage-thresholding algorithm (FISTA). Notably, the robust reconstruction of occluded objects is observed. Through comprehensive evaluations conducted on both synthetic and experimental datasets, we demonstrate that the proposed approach yields high-quality reconstructions, surpassing the state-of-the-art reconstruction algorithms, especially in scenarios involving short exposure and low-SNR measurements.

Energy-Efficient Emerging Optical Wireless Links

In recent years, the tremendous increase in data traffic carried by wireless communication networks has generated the urgent need for establishing more energy-efficient wireless communication systems. Recent advances in semiconductor and light devices have triggered remarkable research interest to the development of these optical wireless communication (OWC) links. Among them, free-space optical (FSO) links and, more recently, ultraviolet links which operate within the (UV-C) spectral band, have been considered as prime candidates to create both high speed and power effective line-of-sight (LOS) and non-light-of-sight (NLOS) free-air communication links, respectively. Moreover, transdermal optical wireless (TOW) links for telemetry with medical implants minimize the expense of power for the implant. In the current review, a background on the energy efficiency challenges in wireless communication is presented. Each of these OWC technologies is mainly discussed in terms of key energy consumption requirements and major limiting factors that affect their power performance. Energy-efficient modulation formats as well as other powerful techniques for performance enhancement such as diversity and relaying are assessed. The survey is concluded with a discussion regarding their future energy consumption requirements and trends.

Improved Indoor Positioning Model Based on UWB/IMU Tight Combination with Double-Loop Cumulative Error Estimation

With the increasing applications of UWB indoor positioning technologies in industrial areas, to further enhance the positioning precision, the UWB/IMU combination method (UICM) has been considered as one of the most effective solutions to reduce non-line-of-sight (NLOS) errors. However, most conversional UICMs suffer from a high probability of positioning failure due to uncontrollable and cumulative errors from inertial measuring units (IMU). Hence, to address this issue, we improved the extended Kalman filter (EKF) algorithm of an indoor positioning model based on UWB/IMU tight combination with a double-loop error self-correction. Compared with conventional UICMs, this improved model consists of new modules for fixing time desynchronization, optimizing the threshold setting for UWB ranging, data fusion in NLOS, and double-loop error estimation, sequentially. Further, systematic error controllability analysis proved that the proposed model could satisfy the controllability of UWB indoor positioning systems. To validate this improved UICM, inevitable obstacles and atmospheric interferences were regarded as Gaussian white noises to verify its environmental adaptability. Finally, the experimental results showed that this proposed model outperformed the state-of-the-art UWB-based positioning models with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).

Indoor Non‐Line‐of‐Sight Millimeter‐Wave Coverage Enhancements by Field‐Reorganizable Passive Digital Coding Metasurfaces

Digital metasurfaces define a novel methodology for metasurface designs by adopting discrete coding meta‐atoms to engineer electromagnetic waves in programmable ways. Herein, a novel field‐reorganizable digital metasurface (FRDM) that can be used to enhance the indoor non‐line‐of‐sight (NLOS) millimeter‐wave (mmWave) signal coverage is presented. The passive binary supercells which can be reorganized with an in situ optimization characteristic to deflect the incoming mmWaves into the blind area with adjustable angles are proposed. The indoor L‐shaped corridor signal coverage simulated by the ray‐tracing technique confirms that the NLOS blind area is effectively communicated using the passive FRDM. Practical environment experiments using FRDMs with different coding sequences are conducted, indicating that the averaged signal intensity is increased by over 10 dB in the NLOS blind area in a wide operating frequency band from 26 to 30 GHz by reorganizing the passive digital metasurfaces. The results suggest that the proposed FRDM enabled by the reorganizable binary supercells is a highly flexible, low‐cost, and extensible solution for B5G and 6G mmWave wireless communications.

Research on Autonomous and Collaborative Deployment of Massive Mobile Base Stations in High-Rise Building Fire Field.

High-rise building fires pose a serious threat to the lives and property safety of people. The lack of reliable and accurate positioning means is one of the main difficulties faced by rescuers. In the absence of prior knowledge of the high-rise building fire environment, the coverage deployment of mobile base stations is a challenging problem that has not received much attention in the literature. This paper studies the problem of the autonomous optimal deployment of base stations in high-rise building fire environments based on a UAV group. A novel problem formulation is proposed that solves the non-line-of-sight (NLOS) positioning problem in complex and unknown environments. The purpose of this paper is to realize the coverage and deployment of mobile base stations in complex and unknown fire environments. The NLOS positioning problem in the fire field environment is turned into the line-of-sight (LOS) positioning problem through the optimization algorithm. And there are more than three LOS base stations nearby at any point in the fire field. A control law which is formulated in a mathematically precise problem statement is developed that guarantees to meet mobile base stations' deployment goals and to avoid collision. Finally, the positioning accuracy of our method and that of the common method were compared under many different cases. The simulation result showed that the positioning error of a simulated firefighter in the fire field environment was improved from more than 10 m (the positioning error of the traditional method) to less than 1 m.

Dual-Propagation-Feature Fusion Enhanced Neural CSI Compression for Massive MIMO

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

Clutter Effect Investigation on Co-Polarized Chipless RFID Tags and Mitigation Using Cross-Polarized Tags, Analytical Model, Simulation, and Measurement.

Chipless radio frequency identification (RFID) technology is expected to replace barcode technology due to its ability to read in non-line-of-sight (NLOS) situations, long reading range, and low cost. Currently, there is extensive research being conducted on frequency-coded (FC) co-polarized radar cross-section (RCS)-based tags, which are widely used. However, detecting co-polarized chipless RFID tags in cluttered environments is still a challenge, as confirmed by measuring two co-polarized tags in front of a perfect metal reflector (30.5cm×22.5cm). To address this challenge, a realistic mathematical model for a chipless RFID system has been developed that takes into account the characteristics of the reader and the tag, as well as reflections from cluttered objects. This extensive mathematical model developed for linear chipless RFID systems in clutter scenarios holds the potential to greatly assist researchers in their exploration of RCS-based tags. By relying solely on simulations, this model provides a tool to effectively analyze and understand RCS-based tags, ultimately simplifying the process of generating more authentic tag designs. This model has been simulated and verified with measurement results by placing a single flat metal reflector behind two co-polarized one-bit designs: a dipole array tag and a square patch tag. The results showed that the interfering signal completely overlaps the ID of the co-polarized tag, severely limiting its detectability. To solve this issue, the proposed solution involves reading the tag in cross-polarization mode by etching a diagonal slot in the square patch tag. This proposed tag provides high immunity to the environment and can be detected in front of both dielectric and metallic objects.

Improved Strong Tracking Cubature Kalman Filter for UWB Positioning.

For the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate predictive dynamics model in wireless ultra-wideband (UWB) positioning systems, an improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm is proposed in this paper. The main idea of the algorithm is as follows. First, the observations are reconstructed based on the weighted positioning results obtained from the predictive dynamics model and the least squares algorithm. Second, the difference in statistical properties between the observation noise and the NLOS errors is utilized to identify the NLOS observations by the corresponding judgment statistics obtained from the operation between the original observations and the reconstructed observations. The main positioning error of the UWB positioning system at the current moment is then judged by the NLOS identification results, and the corresponding fading factors are calculated according to the judgment results. Finally, the corresponding ISTCKF is constructed based on the fading factors to mitigate the main positioning error and obtain accurate positioning result in the UWB positioning system. In this paper, the reconstructed observations mitigate the observation noise in the original observation, and then the ISTCKF mitigates the main errors in the UWB positioning system. The experimental results show that the ISTCKF algorithm reduces the positioning error by 55.2%, 32.3% and 28.9% compared with STCKF, ACKF and RSTCKF, respectively. The proposed ISTCKF algorithm significantly improves the positioning accuracy and stability of the UWB system.