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Found 18 papers, 0 papers with code
Collection: Datasets from AFAR Challenge
For each team, the UGV was placed at three different positions, resulting in a total of 30 datasets, 15 collected in a DT simulation environment and 15 in a physical outdoor testbed.
Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field
Terahertz (THz) communication is a key enabler for 6G systems, offering ultra-wide bandwidth and unprecedented data rates.
Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks
Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users.
HoP: Homeomorphic Polar Learning for Hard Constrained Optimization
Constrained optimization demands highly efficient solvers which promotes the development of learn-to-optimize (L2O) approaches.
Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks
Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist.
Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles
Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency.
Joint User Scheduling and Computing Resource Allocation Optimization in Asynchronous Mobile Edge Computing Networks
By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained.
Spatial Channel State Information Prediction with Generative AI: Towards Holographic Communication and Digital Radio Twin
As 5G technology becomes increasingly established, the anticipation for 6G is growing, which promises to deliver faster and more reliable wireless connections via cutting-edge radio technologies.
HawkRover: An Autonomous mmWave Vehicular Communication Testbed with Multi-sensor Fusion and Deep Learning
Connected and automated vehicles (CAVs) have become a transformative technology that can change our daily life.
Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges
Channel modeling is fundamental in advancing wireless systems and has thus attracted considerable research focus.
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas.
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education.
A New Implementation of Federated Learning for Privacy and Security Enhancement
Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients.
When Machine Learning Meets Spectrum Sharing Security: Methodologies and Challenges
The exponential growth of internet connected systems has generated numerous challenges, such as spectrum shortage issues, which require efficient spectrum sharing (SS) solutions.
User Scheduling for Federated Learning Through Over-the-Air Computation
A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process.
Secure and Energy-Efficient Offloading and Resource Allocation in a NOMA-Based MEC Network
In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations.
Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC
Due to the limited bandwidth, only a subset of connected devices can be scheduled in each round.
Adaptive Federated Learning With Gradient Compression in Uplink NOMA
Federated learning (FL) is an emerging machine learning technique that aggregates model attributes from a large number of distributed devices.
Networking and Internet Architecture Signal Processing
