Line Of Sight Path Research Articles

Kinodynamic Model-Based UAV Trajectory Optimization for Wireless Communication Support of Internet of Vehicles in Smart Cities

Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology.

Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

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.

Reflection-enabled HDOP-weighted underwater acoustic passive localization with a small aperture array

This paper addresses the localization problem of a noncooperative source in the multipath-rich environment. Unlike conventional schemes that only use the line-of-sight (LOS) and the once-surface-reflected (S1B0) path, the proposed scheme utilizes the complete spatial diversity of all multipaths by quantifying their positioning confidence. In the proposed scheme, all the measurement metrics (angle, relative delay and amplitude) extracted from multipath arrival structure, are combined to achieve localization with a small aperture array.Specifically, we first propose an algorithm for extracting multipath arrival structure based on spatial time-frequency analysis of underwater multipath signals. In this algorithm, each path is iteratively extracted, and the extraction error is close to the Cramér–Rao lower bound (CRLB). Then, the obtained CRLB is used to derive horizontal dilution of precision (HDOP) to quantify the positioning confidence of each path. Finally, the source location is estimated from an optimal HDOP-weighted multipath localization algorithm that assigns different weights to the LOS path and non-line-of-sight (NLOS) paths.Statistical-channel-model-based simulations and lake trial corroborate that the proposed scheme reduces the localization error by at least 128% compared with angle-only, relative-delay-only, and conventional hybrid metrics localization schemes.Index Terms--Underwater acoustic passive localization; Multipath arrival structure; Horizontal dilution of precision; Hybrid metrics.

Approximate Maximum-Likelihood RIS-Aided Positioning

A reconfigurable intelligent surface (RIS) allows a reflection transmission path between a base station (BS) and user equipment (UE). In wireless localization, this reflection path aids in positioning accuracy, especially when the line-of-sight (LOS) path is subject to severe blockage and fading. In this paper, we develop a RIS-aided positioning framework to locate a UE in environments where the LOS path may or may not be available. We first estimate the RIS-aided channel parameters from the received signals at the UE. To infer the UE position and clock bias from the estimated channel parameters, we propose a fusion method consisting of weighted least squares over the estimates of the LOS and reflection paths. We show that this approximates the maximum likelihood estimator under the large-sample regime and when the estimates from different paths are independent. We then optimize the RIS phase shifts to improve the positioning accuracy and extend the proposed approach to the case with multiple BSs and UEs. We derive Cramér–Rao bound (CRB) and demonstrate numerically that our proposed positioning method approaches the CRB.

The line-of-sight peak detection and tracking of underwater acoustic DSSS communications in the doubly spread channel

In the underwater acoustic (UWA) channel, the line-of-sight (LOS) path has the characteristics of stable magnitude and continuous phase, which is seldom affected by the surface/bottom reflection. Therefore, in the direct sequence spread spectrum (DSSS) communication systems, the receiver usually extracts the LOS path to recover the information bits and achieve the robust communication. However, the UWA channel is typically the doubly spread channel, i.e., multipath fast fading and non-uniform Doppler shifts, which seriously degrades the performance of extracting the LOS path by the conventional maximum energy method. Therefore, this paper proposes a LOS peak detection and tracking algorithm to extract the LOS path in the UWA DSSS communications. In the proposed algorithm, the candidate correlation peaks after de-spreading are firstly selected by the adaptive peak detection threshold. Then, according to the transmitter/receiver motion relationship, the Delay-Doppler joint state equation is established, and thus the nearest neighbor method adopting the Kalman filter is proposed to track the LOS path. Numerical simulation results demonstrate that the proposed algorithm has better processing performance in the doubly spread channels compared with the conventional method. Finally, the performance of the proposed algorithm is confirmed by the shallow water horizontal channel and deep sea vertical channel experiments.

Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.

Vector field-based integral LOS path following and target tracking for underactuated unmanned surface vehicle

The paper focuses on developing a method that enables the unmanned surface vehicle (USV) to effectively track dynamic objects on the water surface. The integral line-of-sight (LOS) guidance law is improved by introducing the concept of vector field for curved paths and the time-varying lookahead distance with a smoothing control parameter for straight paths. These enhancements facilitate faster convergence to the desired path with reduced oscillations and allow for mitigating the impact of constant external disturbances. The research demonstrates that the proposed guidance law exhibits κ-exponential stability when converging towards a desired path composed of both straight lines and curves. The results presented in the paper demonstrate that the proposed method effectively improves the accuracy of tracking dynamic targets for unmanned surface vehicles while ensuring the safety of docking operations.

Joint Precoding and Array Design for Broadcast in the Internet of Unmanned Aerial Vehicles

To promote energy and spectrum efficient communications in multi-antenna channels, broadcast can provide a substantial gain in system throughput. However, the hardware constraints and strong line-of-sight (LoS) limit the implementation and performance of multi-antenna broadcast in the Internet of unmanned aerial vehicles (UAVs). Based on the pseudo-Doppler principle, we propose a joint precoding and antenna array design to reduce the number of radio frequency (RF) chains required by the broadcast and free the LoS path from the inter-stream interference. The reduction of RF chains is realised by designing the precoding matrix that makes at least one of the transmit antennas have null inputs during any broadcast and, moreover, the LoS path is formed to match the obtained precoding matrix through antenna array design at the broadcasting UAV. The algorithms with low computational complexity for optimising this design are developed to minimise the transmit power within the UAV broadcast paradigms. Theoretical formulation and numerical results in the metrics of sum data rate and bit error rate substantiate the validity of our proposed design, specifically in the Internet of UAVs with strong LoS.

Single-base station hybrid positioning algorithm based on LOS identification

In the complex multipath propagation environment, whether there is a line of sight (LOS) path will directly affect the positioning accuracy. Therefore, this paper proposes a single base station hybrid positioning algorithm based on LOS identification. In the algorithm, we first construct multiple features based on channel state information with LOS and without LOS in statistical distribution and use these features and gradient boosting decision tree to determine whether there is a LOS path in the environment. Then for the environment with a LOS path, we proposed a positioning method based on the estimation of signal parameters via rotation invariant technology algorithm which can be used to jointly estimate the angle of arrival and time delay of the LOS path for positioning, while for the environment without LOS path, we propose a positioning method based on adaptive genetic algorithm. Finally, a single base station hybrid positioning algorithm based on LOS identification and the corresponding positioning methods. Simulation results show that the proposed hybrid positioning algorithm can achieve high-precision positioning in the complex multipath propagation environment.

Channel characteristics analysis of millimetre‐wave bands in Hyperloop scenarios based on ray‐tracing

AbstractA reliable train‐to‐ground wireless communication system is proposed for the foundation of the safe operation of Hyperloop. The wireless channel characteristics of Hyperloops at millimetre‐wave (mmWave) bands are analysed based on the ray‐tracing method. Considering the reflection paths and line of sight path, the channel transfer function expression is derived for each multipath and then the channel impulse response is obtained. On this basis, the investigation and analysis of the large‐scale and small‐scale channel characteristics, including the path loss, shadow fading, delay spread, and angular spread of 4 different mmWave frequency carriers, that is, 28/38/45/60 GHz, are conducted. Simulation results show that the path loss increases as the frequency increases while the time delay spread and angular spread almost keep unchanged. The relevant research results will contribute to the design of future Hyperloop wireless communication systems.

Deep Learning-Based Multipath DoAs Estimation Method for mmWave Massive MIMO Systems in Low SNR

To realize the direction-of-arrivals (DoAs) estimation without the prior information about the multipath number, a novel method using the deep learning is introduced for the millimeter (mmWave) massive multiple-input and multiple-output (MIMO) systems. In particular, the DoAs estimation is decomposed into three sub-problems, which are solved by the corresponding convolutional neural network (CNN), respectively. The estimation of multipath number is achieved by a multi-label classification model using the proposed CNN-I. Then, the proposed CNN-II is introduced for the DoA estimation of the line-of-sight (LOS) path. Based on the predicted multipath number obtained by the CNN-I, a regression model using the proposed CNN-III is applied for the DoAs estimation of non-line-of-sight (NLOS) paths. The proposed DoAs estimation method is implemented by learning the non-linear relationship between the sample covariance matrix of the received signal and the angles, thus reconstructing the mapping model. A series of results validate the superiority of the proposed DoAs estimation method in low signal-to-noise-ratio (SNR) regimes. The CNN-I is capable of achieving the good multipath number estimation performance across a range of SNRs. The classification model based on the proposed CNN-II and the regression model using the proposed CNN-III achieve the lower DoAs estimation root mean square error (RMSE) compared with some deep learning-based methods, estimation of signal parameters via rotational invariance techniques (ESPRIT) and Root-MUltiple SIgnal Classification (Root-MUSIC) methods. Remarkably, the proposed method using the CNN-II exhibits the good robustness in the DoA estimation of the LOS path. Furthermore, the low DoAs estimation RMSE is also achieved in the uniform planar array.

Near-Field Channel Estimation in Mixed LoS/NLoS Environments for Extremely Large-Scale MIMO Systems

Accurate channel model and channel estimation are essential to empower extremely large-scale MIMO (XL-MIMO) in 6G networks with ultra-high spectral efficiency. With the sharp increase in the antenna array aperture of the XL-MIMO scenario, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, most of the existing XL-MIMO channel models fail to describe mixed line-of-sight (LoS) and non-line-of-sight (NLoS) path components simultaneously. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to match the practical near-field XL-MIMO scenario, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) and MIMO advanced RD (MIMO-ARD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Moreover, the Cramér-Rao lower bound (CRLB) of the proposed algorithm is derived in this paper. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods in both the theoretical channel model and the QuaDRiGa channel emulation platform.

Three-Dimensional NLOS VLP Based on a Luminance Distribution Model for Image Sensor

Visible light positioning (VLP) is playing a critical role in delivering better location-based services. However, traditional VLP systems rely on line-of-sight (LOS) paths and have a requirement for large numbers of light-emitting diodes (LEDs) and sensors, making them unprepared for various scenarios. This paper proposes a three-dimensional (3D) non-line-of-sight (NLOS) VLP system using a single LED and an image sensor (IS) to address the problem of obstructed LOS paths. On the one hand, an optical camera communication (OCC) subsystem is designed to receive the transmitter’s position based on NLOS links. In contrast, two highlights are visible in the picture when the image sensor captures the reflected light from the floor. Using the proposed luminance distribution model (LDM), it can be demonstrated that the two highlights can be regarded as the projections formed by two virtual LEDs. Based on the projection equations of the two virtual LEDs, an image sensing algorithm can estimate the receiver’s position. Experimental results show that the proposed system can achieve a 90th percentile accuracy of < 22 cm for 3D positioning. To the best of author’s knowledge, this is the first work to demonstrate 3D NLOS VLP using just a single LED and an IS. Additionally, the proposed LDM is the first physically based model for the first reflected light to estimate the channel gain of a NLOS link.

Predictor-Based Fixed-Time LOS Path Following Control of Underactuated USV With Unknown Disturbances

This paper investigates the path following problem of underactuated unmanned surface vehicle systems suffering unknown disturbances. A fixed-time predictor is proposed to approximate the sideslip caused by disturbances, by which the prediction error can converge to zero in a fixed time. By selecting appropriate controller parameters, the upper bound of settling time is identified. And it's independent of initial conditions, compared with the finite-time predictor. In addition, a fixed-time line-of-sight (LOS) guidance law and a fixed-time heading controller are proposed. The cascade system composed of the guidance law and heading controller is fixed-time stable. All states of the Unmanned surface vehicles (USV) system can be stabilized globally in a fixed time, and USVs can follow the desired path accurately. Finally, numerical experiments are given to illustrate the effectiveness and feasibility of the proposed algorithm.

Geometry-Based Stochastic Probability Models for the LoS and NLoS Paths of A2G Channels Under Urban Scenarios

Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time variant due to fast changeable altitudes of unmanned aerial vehicles and scattering environments. Considering the A2G channels under urban scenarios, this article presents three novel stochastic probability models for the Line-of-Sight (LoS) path, ground specular (GS) path, and building scattering (BS) path, respectively. By analyzing the geometric stochastic information of 3-D scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of the Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and non-LoS path probability models show good agreement with the ray-tracing (RT) simulation method.

Reconfigurable Intelligent Surface for mmWave Mobile Communications: What If LoS Path Exists?

Reconfigurable intelligent surface (RIS) has been widely discussed owing to its potential in enhancing the communication performance in a low-cost and energy efficient manner. The existing research on RIS-assisted channel modeling is mainly based on the assumption of obstructed line-of-sight (LoS) path between the transceivers. However, what if the LoS path (re)appears in RIS-assisted mobile communications? This letter aims at answering this question by developing a novel non-stationary channel model for RIS auxiliary millimeter wave (mmWave) mobile systems in the presence of alternately disappearing and reappearing LoS path. Based on the proposed channel model, a transmission mode switch scheme at a base station (BS) is developed based on the time-varying location of the receiver. The mapping relationship between BS transmission mode and receiver location is derived. Numerical results are provided to verify our analysis.

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.

Measurement and Analysis of Local Average Power According to Averaging Length Changes of 3, 6, 10, and 17 GHz in an Indoor Corridor Environment

This study measures and analyzes the local average power for line-of-sight (LOS) and non-line-of-sight (NLOS) paths according to the averaging length in an indoor corridor environment. The indoor corridor comprises multiple offices, laboratory spaces, and lecture rooms. We selected 3, 6, 10, and 17 GHz measurement frequency bands. The measurement system consists of a signal generator, a low-noise amplifier, transmission and receiving antenna, and spectrum analyzer. To obtain an accurate prediction model of propagation due to the multipath effect, we determined the measurement method based on the measurement interval and number of measurements according to changes in the averaging length. 2, 4, 6, 8, and 10 lambdas (λ) were selected for the number of measurements by frequency, and 1.5 cm was set as the measurement interval. We used the close-in (CI) path loss model for the analysis according to changes in the averaging length. The coefficient of determination (R-squared) was applied using a linear regression equation to verify the measurement accuracy. Based on parameter n of the CI path loss model, no large differences were observed in the averaging length at each measurement frequency. However, at 2λ, owing to the multipath effect, R-squared was approximately 0.4–0.7 for the LOS path and 0.6–0.8 for the NLOS path. At 10λ, R-squared was approximately 0.7–0.8 for the LOS path and 0.8–0.9 for the NLOS path. This indicated that as the number of measurements increased by increasing the averaging length, the accuracy of the measurement results improved. The study findings will help determine an optimal averaging length, thus ensuring reliable indoor propagation measurement and contributing to the ITU-R standard.

A Single-Site Vehicle Positioning Method in the Rectangular Tunnel Environment

Due to the satellite signals are blocked, it is difficult to obtain the vehicle position in the tunnels. We propose a single-site vehicle localization scheme for the rectangular tunnel environment, where most satellite-based positioning methods can not provide the required localization accuracy. In the non-line-of-sight (NLOS) scenarios, we make use of the reflection paths as assistants for vehicle positioning. Specifically, first, the virtual stations are established based on the actual geometrical structure of the tunnel. Second, we use the direction-of-arrival (DOA) and time-of-arrival (TOA) information of reflection paths from two tunnel walls to achieve vehicle positioning. Especially, the Cramer-Rao lower bound (CRLB) of the joint TOA and DOA localization for NLOS propagations in a two-dimensional (2D) space is derived. In addition, based on the localization algorithms with and without filters, we assess the localization performance. In the line-of-sight (LOS) scenarios, we use the LOS path and two reflection paths from the tunnel walls to estimate the vehicle location. First, virtual base stations are established. Second, based on the obtained TOA information, different positioning algorithms are used to estimate the vehicle location. Simulation results illustrate that the proposed positioning approach can provide a small root mean square error. The localization algorithms using filters improve the localization accuracy, compared with the positioning algorithm without using filters, namely, the two-stage weighted least squares (TSWLS) algorithm. Moreover, the Unscented Particle Filter (UPF) algorithm achieves better positioning accuracy than other methods (i.e., Unscented Kalman Filter (UKF), Extended Kalman Filter (EKF), TSWLS algorithms).