Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times. We solve this problem via a novel expediting beam selection by leveraging multimodal data collected from sensors like LiDAR, camera images, and GPS. We propose individual modality and distributed fusion-based deep learning (F-DL) architectures that can execute locally as well as at a mobile edge computing center (MEC), with a study on associated tradeoffs. We also formulate and solve an optimization problem that considers practical beam-searching, MEC processing and sensor-to-MEC data delivery latency overheads for determining the output dimensions of the above F-DL architectures. Results from extensive evaluations conducted on publicly available synthetic and home-grown real-world datasets reveal 95% and 96% improvement in beam selection speed over classical RF-only beam sweeping, respectively. F-DL also outperforms the state-of-the-art techniques by 20-22% in predicting top-10 best beam pairs.
Orchestrating Multimodal DNN Workloads in Wireless Neural Processing
Department of Electronic and Electrical Engineering, University College London（电子与电气工程系，伦敦大学学院）；Department of Electronic Engineering, Kyung Hee University（电子工程系，庆熙大学）；School of Electrical and Electronic Engineering, the University of Sheffield（电气与电子工程学院，谢菲尔德大学）；Department of Electronics and Information Convergence Engineering, Kyung Hee University（电子与信息融合工程系，庆熙大学）
Implicit Channel Learning for Machine Learning Applications in 6G Wireless Networks
Duzce University（杜兹切大学）；University of Luxembourg（卢森堡大学）；Carleton University（卡尔顿大学）；United States Army Research Laboratory（美国陆军研究实验室）；University of Hertfordshire（赫特福德大学）
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach
Semantic Communication-Enhanced Split Federated Learning for Vehicular Networks: Architecture, Challenges, and Case Study
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Faculty of Information Technology, University of Jyväskylä（于韦斯屈莱大学信息科技学院）；Center for Intelligent Networking and Communications (CINC), University of Electronic Science and Technology of China（电子科技大学智能网络与通信中心）
Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
University of Electronic Science and Technology of China（电子科技大学）；Simula Metropolitan Center for Digital Engineering（Simula都市数字工程中心）；University of Oslo（奥斯陆大学）
VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network
School of Engineering, University of Guelph（圭尔夫大学工程学院）；College of Electrical Engineering and Computer Sciences, Ningbo University（宁波大学电气与计算机科学学院）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）
