Search Results for author:
Found 20 papers, 0 papers with code
Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems
For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization.
Knowledge Graph Driven UAV Cognitive Semantic Communication Systems for Efficient Object Detection
Unmanned aerial vehicles (UAVs) are widely used for object detection.
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.
Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation
Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive semantic communication systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability.
Pricing for Reconfigurable Intelligent Surface Aided Wireless Networks: Models and Principles
Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs.
One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation
To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system.
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.
Cognitive Semantic Communication Systems Driven by Knowledge Graph
Semantic communication is envisioned as a promising technique to break through the Shannon limit.
Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network
Theoretical analysis proves that the proposed algorithm can achieve a $[O(1/V), O(V)]$ tradeoff between EE and service delay.
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.
Energy-Efficient Design for IRS-Assisted MEC Networks with NOMA
Energy-efficient design is of crucial importance in wireless internet of things (IoT) networks.
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.
A Novel Automatic Modulation Classification Scheme Based on Multi-Scale Networks
Moreover, a novel loss function that combines the center loss and the cross entropy loss is exploited to learn both discriminative and separable features in order to further improve the classification performance.
Mobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks
In this paper, the offloading decision and resource allocation problem is studied with mobility consideration.
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.
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network
To support the ever increasing computational needs in such a vehicular network, the distributed virtual cloud network (VCN) is formed, based on which a computational resource sharing scheme through offloading among nearby vehicles is proposed.
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.
Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks
Unlike these legacy modeling paradigms, this paper considers heterogeneous content preference of the users with heterogeneous caching models at the edge nodes.
A Machine Learning Based Framework for the Smart Healthcare Monitoring
Thus, teh primary purpose of thus study is to reconstruct the image as visibly clear as possible and hence it helps the detection step at the trained classifier.
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
