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Found 21 papers, 3 papers with code
Teacher-Student Learning based Low Complexity Relay Selection in Wireless Powered Communications
We first obtain the optimal solution to the scheduling and power control problem for the given relay selection.
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs
The usage of federated learning (FL) in Vehicular Ad hoc Networks (VANET) has garnered significant interest in research due to the advantages of reducing transmission overhead and protecting user privacy by communicating local dataset gradients instead of raw data.
GANs for EVT Based Model Parameter Estimation in Real-time Ultra-Reliable Communication
This paper explores a novel methodology integrating Extreme Value Theory (EVT) and Generative Adversarial Networks (GANs) to achieve the precise channel modeling in real-time.
Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systems
In the optimization theory stage, following the formulation of the joint optimization problem, optimality conditions are derived to find the mathematical relations between the optimal values of the decision variables.
Statistical Analysis of Geometric Algorithms in Vehicular Visible Light Positioning
Vehicular visible light positioning (VLP) methods find relative locations of vehicles by estimating the positions of intensity-modulated head/tail lights of one vehicle (target) with respect to another (ego).
Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals
Channel estimation is a critical task in intelligent reflecting surface (IRS)-assisted wireless systems due to the uncertainties imposed by environment dynamics and rapid changes in the IRS configuration.
Measurement Based Non-Line-Of-Sight Vehicular Visible Light Communication Channel Characterization
We propose a distance-based NLoS V-VLC channel path loss model considering reflection surface characteristics and NLoS V-VLC channel impulse response (CIR) incorporating the temporal broadening effect due to vehicle reflections through weighted double gamma function.
Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising
In this paper, to investigate both time-correlated and white noise components of the V-VLC channel, we propose a noise analysis based on Allan variance (AVAR), which provides a time-series analysis method to identify noise from the data.
A Hybrid Architecture for Federated and Centralized Learning
To address this common scenario, we propose a more efficient approach called hybrid federated and centralized learning (HFCL), wherein only the clients with sufficient resources employ FL, while the remaining ones send their datasets to the PS, which computes the model on behalf of them.
Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications
In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation.
Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems
Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data.
Minimum Length Scheduling for Multi-cell Full Duplex Wireless Powered Communication Networks
In this paper, we investigate a novel minimum length scheduling problem to determine the optimal power control, and scheduling for constant and continuous rate models, while considering concurrent transmission of users, energy causality, maximum transmit power and traffic demand constraints.
Hybrid Federated and Centralized Learning
We address this through a novel hybrid federated and centralized learning (HFCL) framework to effectively train a learning model by exploiting the computational capability of the clients.
Vehicular Visible Light Positioning for Collision Avoidance and Platooning: A Survey
Recently, positioning based on visible light communication signals from vehicle head/tail LED lights (VLP) has shown significant promise as a complementary method attaining cm-level accuracy and near-kHz rate in realistic driving scenarios.
Vehicular Networks for Combating a Worldwide Pandemic: Preventing the Spread of COVID-19
As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids.
Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO
Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output.
Federated Learning in Vehicular Networks
Machine learning (ML) has recently been adopted in vehicular networks for applications such as autonomous driving, road safety prediction and vehicular object detection, due to its model-free characteristic, allowing adaptive fast response.
Federated Learning for Hybrid Beamforming in mm-Wave Massive MIMO
In this work, we introduce a federated learning (FL) based framework for hybrid beamforming, where the model training is performed at the BS by collecting only the gradients from the users.
Machine Learning Based Channel Modeling for Vehicular Visible Light Communication
Current OWC channel models based on deterministic and stochastic methods, fail to address mobility induced ambient light, optical turbulence and road reflection effects on channel characterization.
Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications
A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events occurring due to significant fading.
Minimum Length Scheduling for Full Duplex Time-Critical Wireless Powered Communication Networks
In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and transmission schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex wireless powered communication network.
