홈 › Papers

Joint Channel Selection using FedDRL in V2X

2024-10-03 · Lorenzo Mancini, Safwan Labbi, Karim Abed Meraim, Fouzi Boukhalfa, Alain Durmus, Paul Mangold, Eric Moulines

Vehicle-to-everything (V2X) communication technology is revolutionizing transportation by enabling interactions between vehicles, devices, and infrastructures. This connectivity enhances road safety, transportation efficiency, and driver assistance systems. V2X benefits from Machine Learning, enabling real-time data analysis, better decision-making, and improved traffic predictions, making transportation safer and more efficient. In this paper, we study the problem of joint channel selection, where vehicles with different technologies choose one or more Access Points (APs) to transmit messages in a network. In this problem, vehicles must learn a strategy for channel selection, based on observations that incorporate vehicles' information (position and speed), network and communication data (Signal-to-Interference-plus-Noise Ratio from past communications), and environmental data (road type). We propose an approach based on Federated Deep Reinforcement Learning (FedDRL), which enables each vehicle to benefit from other vehicles' experiences. Specifically, we apply the federated Proximal Policy Optimization (FedPPO) algorithm to this task. We show that this method improves communication reliability while minimizing transmission costs and channel switches. The efficiency of the proposed solution is assessed via realistic simulations, highlighting the potential of FedDRL to advance V2X technology.

📄 PDF Abstract BibTeX arXiv:2410.20687

Code (0)

등록된 구현이 없습니다.

Tasks

channel selectionDecision MakingDeep Reinforcement Learning

Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach

2022-11-02 · Yang Li, Ruinong Wang, Yuanzheng Li, Meng Zhang 외

In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional ce…

Deep Reinforcement Learning Federated Learning Prediction Privacy Preserving+3

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

2022-08-04 · Nang Hung Nguyen, Phi Le Nguyen, Duc Long Nguyen, Trung Thanh Nguyen 외

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solut…

Deep Reinforcement Learning Fairness Federated Learning

FedDRL: A Trustworthy Federated Learning Model Fusion Method Based on Staged Reinforcement Learning

2023-07-25 · Leiming Chen, Weishan Zhang, Cihao Dong, Sibo Qiao 외

Traditional federated learning uses the number of samples to calculate the weights of each client model and uses this fixed weight value to fusion the global model. However, in practical scenarios, each client's device a…

Federated Learning

End-to-end learnable EEG channel selection for deep neural networks with Gumbel-softmax

2021-02-11 · Thomas Strypsteen, Alexander Bertrand

Many electroencephalography (EEG) applications rely on channel selection methods to remove the least informative channels, e.g., to reduce the amount of electrodes to be mounted, to decrease the computational load, or to…

channel selection EEG Electroencephalogram (EEG)Motor Imagery

Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

2022-12-19 · Dohyun Kim, Miguel R. Castellanos, Robert W. Heath Jr

Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobi…

Deep Reinforcement Learning Management