Search Results for author:
Found 52 papers, 5 papers with code
SpectrumFM: A Foundation Model for Intelligent Spectrum Management
Intelligent spectrum management is crucial for improving spectrum efficiency and achieving secure utilization of spectrum resources.
UAV-Aided Progressive Interference Source Localization Based on Improved Trust Region Optimization
This paper presents a novel unmanned aerial vehicle (UAV)-aided progressive interference source localization method based on improved trust region optimization.
UAV-Assisted MEC for Disaster Response: Stackelberg Game-Based Resource Optimization
The problem is formulated to maximize the utility of both the UAV controller and UEs.
CNN+Transformer Based Anomaly Traffic Detection in UAV Networks for Emergency Rescue
Hence, in this paper, we propose a novel anomaly traffic detection architecture for UAV networks based on the software-defined networking (SDN) framework and blockchain technology.
Joint ADS-B in B5G for Hierarchical UAV Networks: Performance Analysis and MEC Based Optimization
Unmanned aerial vehicles (UAVs) play significant roles in multiple fields, which brings great challenges for the airspace safety.
Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions
Wireless signal recognition (WSR) is a crucial technique for intelligent communications and spectrum sharing in the next six-generation (6G) wireless communication networks.
Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles
The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue.
UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection
To overcome these challenges, a UAV cognitive semantic communication system is proposed by exploiting a knowledge graph.
Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum Management
To address the aforementioned challenges, a UNet-based data-and-semantic dual-driven method is proposed by introducing the semantic knowledge of binary city maps and binary sampling location maps to enhance the accuracy of spectrum map construction in complex urban environments with dense communications.
Deep Learning for Spectrum Prediction in Cognitive Radio Networks: State-of-the-Art, New Opportunities, and Challenges
Notably, this paper uses a real-world spectrum dataset to prove the advancements of DL-based methods.
Pruned Convolutional Attention Network Based Wideband Spectrum Sensing with Sub-Nyquist Sampling
Wideband spectrum sensing (WSS) is critical for orchestrating multitudinous wireless transmissions via spectrum sharing, but may incur excessive costs of hardware, power and computation due to the high sampling rate.
IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation
In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective.
Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach
Deep learning (DL) has significantly improved automatic modulation classification (AMC) by leveraging neural networks as the feature extractor. However, as the DL-based AMC becomes increasingly widespread, it is faced with the severe secure issue from various adversarial attacks.
FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification
The multi-scale attention network is used to extract the features from the input signal, the meta-prototype training is adopted to train the feature extractor and the modular open-set classifier can be utilized to classify the testing data into one of the known modulations or potential unknown modulations.
Collaborative Automatic Modulation Classification via Deep Edge Inference for Hierarchical Cognitive Radio Networks
In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of the transmission overhead, data privacy, and computation load.
Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning
For ultra-wideband and high-rate wireless communication systems, wideband spectrum sensing (WSS) is critical, since it empowers secondary users (SUs) to capture the spectrum holes for opportunistic transmission.
Spectrum Prediction With Deep 3D Pyramid Vision Transformer Learning
Based on the Deep- SPred, we first propose a novel 3D spectrum prediction method combining a flow processing strategy with 3D vision Transformer (ViT, i. e., Swin) and a pyramid to serve possible applications such as spectrum monitoring task, named 3D-SwinSTB.
Edge Learning Based Collaborative Automatic Modulation Classification for Hierarchical Cognitive Radio Networks
In hierarchical cognitive radio networks, edge or cloud servers utilize the data collected by edge devices for modulation classification, which, however, is faced with problems of the computation load, transmission overhead, and data privacy.
Interference Analysis for Coexistence of UAVs and Civil Aircrafts Based on Automatic Dependent Surveillance-Broadcast
However, due to the limited resource of channel capacity, UAVs equipped with ADS-B results in the interference between UAVs and civil aircrafts (CAs), which further impacts the accuracy of received information at GSs.
Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
As the demands for immediate and effective responses increase in both civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge as effective solutions, in which multiple cooperative UAVs can work together to achieve specific goals.
Three-Dimension Collision-Free Trajectory Planning of UAVs Based on ADS-B Information in Low-Altitude Urban Airspace
Consequently, we leverage ADS-B for surveillance and information broadcasting, and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.
Joint ADS-B in 5G for Hierarchical Aerial Networks: Performance Analysis and Optimization
Specifically, a hierarchical structure is proposed, in which the high-altitude central UAV is equipped with ADS-B and the low-altitude central UAV utilizes 5G modules to transmit flight information.
SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition
Moreover, a modular semi-supervised learning method that combines labeled and unlabeled data using MixMatch is exploited to further improve the classification performance under few-sample conditions.
KGAMC: A Novel Knowledge Graph Driven Automatic Modulation Classification Scheme
Automatic modulation classification (AMC) is a promising technology to realize intelligent wireless communications in the sixth generation (6G) wireless communication networks.
Knowledge Graph Driven UAV Cognitive Semantic Communication Systems for Efficient Object Detection
Unmanned aerial vehicles (UAVs) are widely used for object detection.
UAV Trajectory Tracking via RNN-enhanced IMM-KF with ADS-B Data
Therefore, we employ the ADS-B for UAV trajectory tracking in this work.
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability.
A Partially Observable Deep Multi-Agent Active Inference Framework for Resource Allocation in 6G and Beyond Wireless Communications Networks
Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes.
Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
The unmanned aerial vehicle (UAV) network is popular these years due to its various applications.
Novel Online-Offline MA2C-DDPG for Efficient Spectrum Allocation and Trajectory Optimization in Dynamic Spectrum Sharing UAV Networks
Unmanned aerial vehicle (UAV) communication is of crucial importance for diverse practical applications.
Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System
However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation.
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.
In-Situ Calibration of Antenna Arrays for Positioning With 5G Networks
Owing to the ubiquity of cellular communication signals, positioning with the fifth generation (5G) signal has emerged as a promising solution in global navigation satellite system-denied areas.
Sparse Bayesian Learning-Based 3D Spectrum Environment Map Construction-Sampling Optimization, Scenario-Dependent Dictionary Construction and Sparse Recovery
Then, a maximum and minimum distance (MMD) clustering-based SBL algorithm is proposed to recover the spectrum data at the unsampled positions and construct the whole 3D SEM.
Computation Offloading for Uncertain Marine Tasks by Cooperation of UAVs and Vessels
Then, we formulate the studied problem into a Markov decision process, aiming to minimize the total execution time and energy cost.
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information
It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data.
A Survey on Collaborative DNN Inference for Edge Intelligence
With the vigorous development of artificial intelligence (AI), the intelligent applications based on deep neural network (DNN) change people's lifestyles and the production efficiency.
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations.
Edge Semantic Cognitive Intelligence for 6G Networks: Novel Theoretical Models, Enabling Framework, and Typical Applications
Edge intelligence is anticipated to underlay the pathway to connected intelligence for 6G networks, but the organic confluence of edge computing and artificial intelligence still needs to be carefully treated.
Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning
We first propose a graph attention self-supervised learning algorithm (GASSL) to detect the HUAVs of a single UAV cluster, where the GASSL can fit the IFS at the same time.
Cognitive Semantic Communication Systems Driven by Knowledge Graph
Semantic communication is envisioned as a promising technique to break through the Shannon limit.
Graph Neural Network-Based Scheduling for Multi-UAV-Enabled Communications in D2D Networks
In this paper, we propose a novel graph neural networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
Then, we review a number of applications of MARL to solve emerging issues in future Internet.
Automatic Modulation Classification Using Involution Enabled Residual Networks
Automatic modulation classification (AMC) is of crucial importance for realizing wireless intelligence communications.
Resilient UAV Swarm Communications with Graph Convolutional Neural Network
Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs.
A Unified Cognitive Learning Framework for Adapting to Dynamic Environment and Tasks
Many machine learning frameworks have been proposed and used in wireless communications for realizing diverse goals.
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.
3D Spectrum Mapping Based on ROI-Driven UAV Deployment
Given the explosive growth of Internet of Things (IoT) devices ranging from the two-dimensional (2D) ground to the three-dimensional (3D) space, it is a necessity to establish a 3D spectrum map to comprehensively present and effectively manage the 3D spatial spectrum resources in smart city infrastructures.
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the core to the edge including end devices.
Networking and Internet Architecture
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge
To scale learning across geographic areas, a spatial transfer learning scheme is proposed to further promote the learning efficiency of distributed DRL entities by exploiting the traffic demand correlations.
One-bit LFMCW Radar: Spectrum Analysis and Target Detection
It is shown that compared to the conventional radar applying linear processing methods, one-bit LFMCW radar has about $1. 3$ dB performance gain when the input signal-to-noise ratios (SNRs) of targets are low.
Cognitive Internet of Things: A New Paradigm beyond Connection
Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations.
