RIS-Empowered V2V Communications: Three-Dimensional Beam Domain Channel Modeling and Analysis

Shi, Wangqi; Jiang, Hao; Xiong, Baiping; Chen, Xiao; Zhang, Hongming; Chen, Zhen; Wu, Qingqing

doi:10.1109/twc.2024.3434568

IEEE Trans. Wireless Commun.

2024

DOI: 10.1109/twc.2024.3434568

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RIS-Empowered V2V Communications: Three-Dimensional Beam Domain Channel Modeling and Analysis

Wangqi Shi,

Hao Jiang,

Baiping Xiong

et al.

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Cited by 3 publications

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GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

Chen,

Liu,

Wang

2024

Electronics

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The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)–assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (FCNN) strategy. Specifically, the intelligent collaborative tuning of system parameters is achieved using this proposed technique. Simulation findings indicate that the hybrid scheme not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. Based on this, the signal transmission quality is improved, interference is reduced, and the stable and efficient operation of the RIS–assisted intelligent communication system is ensured in the complex wireless transmission scenario.

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GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

Chen,

Liu,

Wang

2024

Electronics

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Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications

Mehmood,

Muhammad,

Mehmood

et al. 2024

Mathematics

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In future 6G networks, real-time and accurate vehicular data are key requirements for enhancing the data-driven multi-access edge computing (MEC) applications. Existing estimation techniques to forecast vehicle position aim to meet the real-time data needs but compromise accuracy due to a lack of context awareness. While algorithms such as the Kalman filter improve estimation accuracy by considering certainty-grading and current-state estimate of measurements, they do not include the road context, which is vital for more accurate predictions. Unfortunately, current implementations of linear Kalman filters are not road-aware and struggle to predict a two-dimensional movement accurately. To this end, we propose a significant road-aware rectification-assisted prediction mechanism that enhances the modified Kalman filter predictions by incorporating road awareness. The parameters used for the Kalman filter include vehicle location, angle, speed, and time. In contrast, road-aware location rectification incorporates predicted location and lane shape, increasing the accuracy and precision of vehicle location predictions, reaching up to 99.9%. Performance is evaluated by comparing actual, predicted, and rectified vehicular traces at different speeds. The results demonstrate that the prediction error is approximately 0.005, while the proposed rectification process further reduces the error to 0.001, highlighting the effectiveness of the proposed approach. Overall, results support the idea of provisioning accurate, proactive, and real-time vehicular location data at the edge using a road-aware approach, thereby revolutionizing 6G vehicle location provisioning in MEC.

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3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness

Zhuang,

Zhang,

Wang

et al. 2024

Sensors

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Evaluating students’ learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students’ learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.

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RIS-Empowered V2V Communications: Three-Dimensional Beam Domain Channel Modeling and Analysis

Cited by 3 publications

References 33 publications

GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

Enhancing Vehicle Location Prediction Accuracy with Road-Aware Rectification for Multi-Access Edge Computing Applications

3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness

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