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IEEE Transactions on Intelligent Vehicles
Number of Followers: 2  Hybrid journal (It can contain Open Access articles)ISSN (Online) 2379-8858 Published by IEEE  [228 journals]
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- IEEE Transactions on Intelligent Vehicles Publication Information
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Pages: C2 - C2
Abstract: null
PubDate: MON, 13 NOV 2023 09:18:11 -04
Issue No: Vol. 8, No. 10 (2023)
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- The Transactions on Intelligent Vehicles Information
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Pages: C3 - C3
Abstract: null
PubDate: MON, 13 NOV 2023 09:18:11 -04
Issue No: Vol. 8, No. 10 (2023)
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- Foundation Vehicles: From Foundation Intelligence to Foundation
Transportation for Future Mobility-
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Authors: Fei-Yue Wang;Chen Lv;
Pages: 4287 - 4291
Abstract: IEEE TIV has received its 3251 manuscript submissions, which is nearly twice as many as that of last year, 236 submissions are still being reviewed in our system, and 18 are overdue for review scoring. I would like to express my sincere gratitude to all authors, reviewers, and editorial board members for your invaluable contributions and unwavering support. Your dedication has been instrumental in maintaining the high quality and great service of our journal and fostering a vibrant scholarly community [1], [2], [3], [4], [5], [6], [7].
PubDate: MON, 13 NOV 2023 09:18:11 -04
Issue No: Vol. 8, No. 10 (2023)
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- Intelligent Vehicle Carriers to Support General Civilian Purposes
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Authors: Hailong Huang;Zhipeng Shen;Chao Huang;Yutong Wang;Fei-Yue Wang;
Pages: 4292 - 4295
Abstract: This letter, as a brief summary of the fourth part of the Distributed/Decentralized Hybrid Workshop on Sustainability for Transportation and Logistics (DHW-STL), explores new opportunities for vehicle carriers (VCs) and the potential of various carrier types in different domains, including seabed, underwater, maritime, ground-based, aerial, high-altitude, and space applications. It discusses the advancements in intelligent vehicles (IVs) technology that have revolutionized the capabilities of intelligent vehicle carriers (IVCs). These advancements have garnered the interest of technology enthusiasts alike, prompting an examination of the unique advantages and potential of these carrier types. It analyzes the current status of this integration and explores the possibilities presented by future platforms. By embracing these advancements, the future of carrier operations holds promising prospects for diverse civilian applications worldwide.
PubDate: FRI, 15 SEP 2023 10:02:23 -04
Issue No: Vol. 8, No. 10 (2023)
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- On Intelligent Mining With Parallel Intelligence
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Authors: Jianjian Yang;Qiankun Huang;Shirong Ge;Xiao Wang;Long Chen;Yinan Guo;Tianmu Gui;
Pages: 4296 - 4300
Abstract: The level of intelligence across various industries, including mining, has experienced a notable advancement since the introduction of Industry 4.0. However, the societal demands for employee well-being, resource conservation, and environmental preservation continues to be unavoidable challenges. Thus, the parallel system theory based Industry 5.0 is proposed, which augments industries with human-machine collaboration, virtual-real interaction and local-global balanced resources. In this letter, we proposed the concept of Mining 5.0. Mining 5.0 aims to realize green and sustainable development goals while prioritizing a human-centric perspective. This letter begins by elucidating the prerequisites, characteristics, and construction objectives of Mining 5.0. It then introduces the “4I”, “5O” and “6S” concluded framework of “Intelligent Mining Using Parallel Intelligence” for achieving Mining 5.0. In the last part, the efforts made by Chinese mining research institutions and leading enterprises in the development of mining intelligence standards, and the practical application of parallel mining within the mining sector are outlined. This letter is a summary of recent Distributed/Decentralized Hybrid Workshop on Autonomous Mining (DHW-AM) and aims at enhancing the intelligence of future mining operations.
PubDate: MON, 18 SEP 2023 10:04:22 -04
Issue No: Vol. 8, No. 10 (2023)
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- A Vectorized Representation Model for Trajectory Prediction of Intelligent
Vehicles in Challenging Scenarios-
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Authors: Lulu Guo;Ce Shan;Tengfei Shi;Xuan Li;Fei-Yue Wang;
Pages: 4301 - 4306
Abstract: Trajectory prediction for challenging scenarios has always been a significant problem in the field due to the complexity of dynamic scenarios and interactions. Furthermore, there is often a dynamic gap between evaluating and validating methods on fixed datasets and real driving scenarios. This letter forms part of a series of reports emanating from the IEEE Transactions on Intelligent Vehicles's Decentralized and Hybrid Workshops (DHW) dedicated to the field of Scenarios Engineering. Our research proposes a scenario engineering-based calibration and validation framework for trajectory prediction of autonomous vehicles to more effectively validate the performance of the method in challenging scenarios. First, Scenarios Engineering (SE) uses OpenSCENARIO and real dataset to generate high-definition maps for challenging driving scenarios. Then, the vectorization approach is employed to extract contextual details from the scene and agent trajectory information from the HD map, and the graph neural network is used to model the high-order interaction to realize the interactive trajectory prediction. Compared with the traditional method, the trajectory prediction can be calibrated through SE so that the prediction process can use more traffic information and attribute characteristics, and improve the evaluation index of prediction. The DHW discusses a practical case to verify the potential of the trajectory prediction framework based on scenarios generation in improving the authenticity and accuracy of trajectory prediction.
PubDate: TUE, 19 SEP 2023 10:02:59 -04
Issue No: Vol. 8, No. 10 (2023)
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- Integrated Inertial-LiDAR-Based Map Matching Localization for Varying
Environments-
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Authors: Xin Xia;Neel P. Bhatt;Amir Khajepour;Ehsan Hashemi;
Pages: 4307 - 4318
Abstract: Localization is critical for automated vehicles as it provides essential position, velocity, and heading angle information to perform object tracking, trajectory prediction, motion planning, and control. However, model/environmental uncertainties (including road friction) and noises in sensor measurements have a significant effect on the accuracy of the localization and vehicle state estimation, specially for perceptually degraded conditions. In this article, an integrated localization method based on the fusion of an inertial dead reckoning model and 3D LiDAR-based map matching is proposed and experimentally verified in an urban environment with varying environmental conditions. Leveraging a global navigation satellite system (GNSS), inertial navigation system (INS), and 3D LiDAR point clouds, a novel light point cloud map generation method, which only keeps the necessary point clouds (i.e., buildings and roads regardless of vegetation varying with seasonal change), is proposed. Subsequently, based on the onboard sensors and the pre-built light point cloud map, a dead reckoning model is derived and integrated with the normal distribution transformation (NDT) based map matching algorithm by an error-state-constrained Kalman filter to limit the localization error. On top of the Kalman filter, the stability analysis of the estimator is presented. Finally, the performance of the algorithm is validated in real road experiments under various environmental conditions. Thorough experiments in winter and summer and associated results confirm the advantages of integrating the proposed light point cloud map generation with the dead reckoning model in terms of accuracy and reduced computational complexity.
PubDate: WED, 26 JUL 2023 10:01:47 -04
Issue No: Vol. 8, No. 10 (2023)
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- Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining
Trucks in Unloading Areas-
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Authors: Qingyuan Yang;Yunfeng Ai;Siyu Teng;Yu Gao;Chenglin Cui;Bin Tian;Long Chen;
Pages: 4319 - 4330
Abstract: Cooperative trajectory planning for autonomous vehicles has garnered significant attention in structured environments, but corresponding methodologies for unstructured environments remains relatively underexplored. The unloading area, an integral component of open-pit mines, exemplifies a quintessential unstructured environment. Implementing cooperative planning for autonomous mining trucks (AMTs) within these unloading areas is crucial as the optimization of processes in these areas substantially enhances the overarching safety, productivity, and cost-effectiveness of mining operations. Hence, enhancing the operational efficiency of AMTs in the unloading area can considerably elevate productivity levels of open-pit mines. This article focuses on the real-time cooperative trajectory planning problem for AMTs in such areas, which is challenging due to i) small and irregular space ii) complex operations iii) need for path stability and speed flexibility. We propose a decoupled multi-vehicle trajectory planning (MVTP) method that decomposes trajectory planning into path planning and speed planning. Specifically, we present driving behavior enhanced path planning and sequential real-time cooperative speed planning methods. Our method is compared with several state-of-the-art MVTP methods and proves to be both secure and efficient.
PubDate: WED, 13 SEP 2023 10:01:44 -04
Issue No: Vol. 8, No. 10 (2023)
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- Finite-Time Command-Filtered Lateral-Longitudinal Formation Control of
Vehicles on Rough Roads-
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Authors: Ge Guo;Qian Zhang;
Pages: 4331 - 4340
Abstract: This paper investigates the lateral and longitudinal formation control problem of a group of vehicles driving in rough road environments. An adaptive sliding mode disturbance observer is introduced to estimate the road disturbance. Then, based on the road disturbance observer, a conmand-filtered vehicular formation control method is given, which can make the formation tracking errors converge to a small neighborhood of zero in a finite time. In other words, the lateral-longitudinal formation of vehicles can be practically finite-time stabilized despite the road disturbance, via the use of a backstepping control algorithm with a finite-time command filter based on an error compensation mechanism. Numerical simulations have shown the effectiveness of the presented method.
PubDate: MON, 10 APR 2023 07:18:33 -04
Issue No: Vol. 8, No. 10 (2023)
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- Object-Level Data-Driven Sensor Simulation for Automotive Environment
Perception-
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Authors: László Lindenmaier;Szilárd Aradi;Tamás Bécsi;Olivér Törő;Péter Gáspár;
Pages: 4341 - 4356
Abstract: The gradually evolving automated driving and ADAS functions require more enhanced environment perception. The key to reliable environmental perception is large amounts of data that are hard to collect. Several simulators provide realistic, raw sensor data based on physical sensor models. However, besides their high price, they also require very high computation capacity. Furthermore, most sensor suppliers provide high-level data, such as object detections, that is complicated to reproduce from simulated raw sensor data. This paper proposes a method that directly simulates the detections or object tracks provided by smart sensors. The model involves several uncertainties of the sensors, such as missed-, false detections, and measurement noise. In contrast to the conventional sensor models, this method tackles with state-dependent clutter model and considers the field of view in the detections model. The parameters of the proposed model are identified for an automotive smart radar and camera based on pre-evaluated real-world measurements. The resulting model provides synthetic object-level data with higher fidelity than the conventional probabilistic models, differing less than 2% from the precision and recall metrics of the actual sensors.
PubDate: MON, 19 JUN 2023 10:06:48 -04
Issue No: Vol. 8, No. 10 (2023)
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- Real-Time Operations of Autonomous Mobility- on-Demand Services With
Inter-and Intra-Zonal Relocation-
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Authors: Jiho Yeo;Sujin Lee;Kitae Jang;Jinwoo Lee;
Pages: 4357 - 4369
Abstract: In the context of shared connected autonomous vehicles (SCAVs), the relocation of idle vehicles is a crucial issue for the operation of autonomous mobility-on-demand (AMoD) services. Unlike traditional human-chauffeured taxis, AMoD operations are fully controllable by central systems and not affected by unpredictable human driver behavior. To address the spatial-temporal imbalance between supply and demand and optimize the level of service while minimizing agency costs, we propose a real-time AMoD relocation model. However, vehicle-specific control every second for large fleet sizes may cause computational burdens for the control center. To overcome this, we present a bi-level framework that decomposes the original system-level problem into an inter-zonal relocation problem for the entire service area and an intra-zonal relocation problem for each zone. This reduces the decision space to periodic inter- and intra-zonal relocation of idle vehicles. Using real-world taxi operation data from Daejeon City, Korea, we demonstrate the proposed method via agent-based simulations, assuming that SCAVs replace existing taxis. The results show that the method can significantly reduce the total generalized cost for both users and the agency. Through a sensitivity analysis, we investigate how the performance varies depending on the zone size, inter- and intra-zonal relocation interval, and demand uncertainty and discuss the observed tradeoff. The intended contribution is twofold: first, we propose a novel computationally feasible method that can efficiently operate AMoD systems in real time; second, we provide a closed-form analytical formulation that can help decision-makers explicitly understand the relationship between the cost components and the decision factors.
PubDate: FRI, 28 JUL 2023 10:03:01 -04
Issue No: Vol. 8, No. 10 (2023)
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- Identifying Critical Test Scenarios for Lane Keeping Assistance System
Using Analytic Hierarchy Process and Hierarchical Clustering-
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Authors: Rui Song;Xuan Li;Xiangmo Zhao;Mingyang Liu;Jianhua Zhou;Fei-Yue Wang;
Pages: 4370 - 4380
Abstract: Lane keeping assistance system (LKAS) is critical components of autonomous driving technologies, and their reliable operation is crucial to ensure passenger safety and maintain traffic flow. The validation of LKAS's safety and performance requires extensive testing using diverse and representative test scenarios. However, identifying the most critical test scenarios from a large data is a significant challenge. Inspired by Scenarios Engineering, the present study introduces a novel approach that includes being the first to combine analytic hierarchy process (AHP) and hierarchical clustering (HC) to identify critical test scenarios for the comprehensive evaluation of LKAS. The proposed approach first utilizes the AHP to rank the key factors that contribute to the failure of the LKAS. Then, by taking the key factors as clustering parameters, the HC method is employed to cluster LKAS failure scenarios with similar characteristics. The experimental results suggest that our method is capable of effectively clustering 200 LKAS failure scenarios into 9 categories of critical, reasonable, and reliable LKAS test scenarios. Furthermore, these 9 categories of critical test scenarios can provide guidance for the development and testing of autonomous driving and serve as a foundation for establishing a testing and evaluation system for LKAS in China and around the world.
PubDate: FRI, 21 JUL 2023 10:01:41 -04
Issue No: Vol. 8, No. 10 (2023)
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- Robust Adversarial Attacks Detection Based on Explainable Deep
Reinforcement Learning for UAV Guidance and Planning-
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Authors: Thomas Hickling;Nabil Aouf;Phillippa Spencer;
Pages: 4381 - 4394
Abstract: The dangers of adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents operating in public are increasing. Adopting AI-based techniques and, more specifically, Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but can add concerns regarding the safety of those techniques and their vulnerability against adversarial attacks. Confusion in the agent's decision-making process caused by these attacks can seriously affect the safety of the UAV. This article proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and the UAVs adopting them from attacks. The agent adopts a Deep Reinforcement Learning (DRL) scheme for guidance and planning. The agent is trained with a Deep Deterministic Policy Gradient (DDPG) with Prioritised Experience Replay (PER) DRL scheme that utilises Artificial Potential Field (APF) to improve training times and obstacle avoidance performance. A simulated environment for UAV explainable DRL-based planning and guidance, including obstacles and adversarial attacks, is built. The adversarial attacks are generated by the Basic Iterative Method (BIM) algorithm and reduced obstacle course completion rates from 97% to 35%. Two adversarial attack detectors are proposed to counter this reduction. The first one is a Convolutional Neural Network Adversarial Detector (CNN-AD), which achieves accuracy in the detection of 80%. The second detector utilises a Long Short Term Memory (LSTM) network. It reaches an accuracy of 91% with faster computing times compared to the CNN-AD, allowing for real-time adversarial detection.
PubDate: THU, 20 JUL 2023 10:00:57 -04
Issue No: Vol. 8, No. 10 (2023)
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- Semantic Loop Closure Detection for Intelligent Vehicles Using Panoramas
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Authors: Dingwen Xiao;Sirui Li;Zhe Xuanyuan;
Pages: 4395 - 4405
Abstract: Loop Closure Detection is of great significance in the field of intelligent driving systems, as it reduces the cumulative error of the estimated position of the system and assists in generating a consistent global map. Existing methods differ in frame representation methods and the corresponding frame-matching strategy. Traditionally, local feature points and descriptors are studied extensively while recently global descriptors and semantic information extracted from deep learning methods are considered superior in terms of promoting a high-level understanding of the surrounding environments of robots. However, one of the most challenging problems of using semantic information for loop detection is how to deal with inconsistent visual contents from different viewpoints in the same place. In this article, a semantic loop closure detection method using panoramas is proposed to address this issue. We design a pipeline for efficiently extracting and matching semantic information between frames to identify loops. Most importantly we propose a novel polar coordinate-based panorama representation to address the inconsistent visual appearance problem caused by viewpoint differences. Experiment results show that our proposed method can significantly increase the accuracy of loop closure detection tasks in challenging scenarios where traditional methods may fail.
PubDate: TUE, 25 JUL 2023 10:01:55 -04
Issue No: Vol. 8, No. 10 (2023)
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- Left Gaze Bias Between LHT and RHT: A Recommendation Strategy to Mitigate
Human Errors in Left- and Right-Hand Driving-
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Authors: Jiawei Xu;Kun Guo;Xiaoqin Zhang;Poly Z. H. Sun;
Pages: 4406 - 4417
Abstract: Driver errors, such as distraction, perceptual blindness, and incorrect control manipulation, can either cause road accidents or reduce driving performance in daily driving tasks. Several works in literature have illustrated perceptual blindness and distraction are associated with insufficient attention to those activities vital for safe driving. Also, inappropriate driving-related eye movements may subsequently result in manipulation errors, such as inappropriate control of the throttle or steering wheel. Although many studies have examined drivers' visual performance, there have been few attempts to compare left-hand traffic (LHT) and right-hand traffic (RHT) conditions. Even driving in the same driving scene, different eye movement patterns may also be induced due to drivers' left gaze bias. Motivated by this human factor, this research investigates potential human errors in driving from the perspective of eye movements (i.e., saccades and fixations) and corresponding control manipulations. Firstly, a driving simulator is used to investigate the discrepancy of eye movements in identical driving scenes between LHT and RHT. Secondly, visual regions and feature sensitivities are spatially correlated by comparing the gaze distribution from the central to peripheral visual fields for both LHT and RHT. Besides, considering the violation of traffic rules are likely the cause of potentially fatal driving eye movement and control mistakes, a recommendation strategy is proposed to alleviate visual errors by utilizing a neural network inspired by the driver gaze map. The experiments in both LHT and RHT driving tasks demonstrate improvements in the alleviation of human errors by importing the proposed eye movement recommendation strategy.
PubDate: MON, 24 JUL 2023 10:02:41 -04
Issue No: Vol. 8, No. 10 (2023)
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- Awareness Messages by Vulnerable Road Users and Vehicles: Field Tests via
LTE-V2X-
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Authors: Luca Lusvarghi;Carlo Augusto Grazia;Martin Klapez;Maurizio Casoni;Maria Luisa Merani;
Pages: 4418 - 4433
Abstract: Awareness messages have been recently introduced to extend the information horizon of connected vehicles and Vulnerable Road Users (VRUs) beyond their line-of-sight range. As of today, only a handful of studies has experimentally analyzed the awareness messages generated by cars, and no existing work has concentrated on VRU awareness messages. The intent of this work is to fill the gap of missing experimental activities by analyzing the results of an extensive measurement campaign, aimed at investigating the awareness messages generated by cars and VRUs in a real-world context. Field tests have been performed employing an LTE-V2X prototype with connected VRUs in the urban scenario, while cars have been considered in urban, suburban, and highway environments. In all cases, the paper details the conditions that trigger the generation of awareness messages, presents the Probability Mass Function (PMF) of the time interval between consecutive messages, and relates it to the triggering conditions. As the findings indicate that many awareness messages from VRUs were generated under non-relevant circumstances, the paper also proposes an adjustment to the standard and evaluates its effectiveness in the field, showing a notable improvement with respect to the original setting. Finally, this study evaluates the Packet Delivery Ratio (PDR) experienced by vehicle-to-vehicle communications in the above scenarios and, for the first time, determines the PDR attained by bicycle-to-vehicle communications in the urban setting.
PubDate: MON, 29 MAY 2023 10:03:16 -04
Issue No: Vol. 8, No. 10 (2023)
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- Dynamic Resource Allocation in Queue-Constrained and Delay-Sensitive
Vehicular Networks-
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Authors: Jiawei Su;Zhixin Liu;Yuan-ai Xie;Kit Yan Chan;Xinping Guan;
Pages: 4434 - 4444
Abstract: Since performing reliable and real-time data transmission is challenging, this paper focuses on the dynamic resource allocation framework with queue and delay constraints in vehicular relay networks, where vehicle users and roadside users are in cooperative communications. To reduce data fluctuation and tackle channel uncertainty, the Lyapunov optimization theory is integrated into the long-term dynamic resource allocation framework, which addresses multiple performance indicators including queue backlog, communication delay, and user utility. In particular, the hierarchical competitive relationship between vehicle users and roadside users is captured, and the Stackelberg game structure is adopted to maximize the utilities of all users. Furthermore, the lower-game subproblem is transformed by the Lyapunov optimization method and is solved by the Lagrangian function method. As the best response function, the lower-game solution is used to formulate the upper-game strategy. Since the existing of game equilibrium (GE) solution, a Stackelberg game-enabled robust resource allocation algorithm can be proposed. Finally, various numerical simulations are performed to show that the proposed algorithm has better robustness and higher throughout than the benchmarks.
PubDate: TUE, 25 APR 2023 10:02:25 -04
Issue No: Vol. 8, No. 10 (2023)
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- Intelligent Connectivity Through RIS-Assisted Wireless Communication:
Exact Performance Analysis With Phase Errors and Mobility-
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Authors: Vinay Kumar Chapala;S. M. Zafaruddin;
Pages: 4445 - 4459
Abstract: The current research offers only approximations for evaluating the performance of wireless systems that use reconfigurable intelligent surfaces (RIS) over generalized fading channels with phase error caused by imperfect phase compensation at the RIS. This article presents an exact analysis of RIS-assisted vehicular communication considering uniformly distributed phase error and generalized fading channels with a coherent combining of received signals reflected by RIS elements and direct transmissions from the source terminal. We use a generalized-$K$ shadowed distribution for the direct link and asymmetrical channels for the RIS-assisted transmission with $\kappa$-$\mu$ distribution for the first link and double generalized Gamma (dGG) distribution for the second link, combined with a statistical random waypoint (RWP) model for the moving vehicle. We employ a novel approach to represent the probability density function (PDF) and cumulative distribution function (CDF) of the resultant channel in terms of a single univariate Fox-H function and use the multivariate Fox-H approach to develop an exact statistical analysis of the end-to-end signal-to-noise ratio (SNR) for the RIS-assisted system. We also use the inequality between the arithmetic and geometric means to simplify the statistical results of the considered system in terms of the univariate Fox-H function. Our analysis also provides exact, upper bound, and asymptotic expressions of the outage probability and average bit-error-rate (BER) performance using the derived density and distribution functions. We conduct computer simulations in various practically relevant scenarios to assert that mitigating phase errors is achievable by augmenting the number of elements in the RIS module and employing a higher quantization level.
PubDate: WED, 21 JUN 2023 10:01:41 -04
Issue No: Vol. 8, No. 10 (2023)
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- One of the Most Influential Reference Resources for Engineers Around the
World-
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Pages: 4460 - 4460
Abstract: null
PubDate: MON, 13 NOV 2023 09:18:12 -04
Issue No: Vol. 8, No. 10 (2023)
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- Introducing IEEE Collabratec
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Pages: 4461 - 4461
Abstract: null
PubDate: MON, 13 NOV 2023 09:18:12 -04
Issue No: Vol. 8, No. 10 (2023)
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- Share Your Preprint Research with the World!
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Pages: 4462 - 4462
Abstract: null
PubDate: MON, 13 NOV 2023 09:18:11 -04
Issue No: Vol. 8, No. 10 (2023)
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