phil610351 Goto Github PK

energy-harvesting-ddpg-

Codes for "Deep Deterministic Policy Gradient (DDPG) based Energy Harvesting Wireless Communications"

eua-dataset

Edge server, user dataset for Edge Computing research

facmac

fcmnet

fed-mec

This project is focused on the design, development, and deployment of federated solutions to problems in the framework of mobile edge computing.

fed-mf-mal

This is the code for paper: Scalable Federated Multi-agent Architecture forNetworked Communication Scenarios

fedadapt

Adaptive Offloading of Federated Learning on IoT Devices

federated-double-deep-q_network-

A framework that exploits the potentials of distributed federated learning and double deep Q-networks to minimize joint energy and delay in IoT networks

federated-learning-pytorch

Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data

fen

FEN Code

fleetai

fmlp-rec

further

Source code for "Influencing Long-Term Behavior in Multiagent Reinforcement Learning" (NeurIPS 2022)

game-theoretic-deep-reinforcement-learning

Code of Paper "Joint Task Offloading and Resource Optimization in NOMA-based Vehicular Edge Computing: A Game-Theoretic DRL Approach", JSA 2022.

gan-ddqn

gats-for-traffic-signal-control

gcs_aamas337

The code for AAMAS2022 《GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning》

generalight

globecom2020-resourceallocationgnn

Code for Globecom2020 paper: Resource Allocation based on Graph Neural Networks in Vehicular Communications

globecom2022

gnn-communication-networks

This is the repository for the collection of Graph-based Deep Learning for Communication Networks.

gnn-rl-for-amod

Official implementation of "Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand

gnn-rl-model-compression

GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning

gnn4traffic

This is the repository for the collection of Graph Neural Network for Traffic Forecasting.

gplight

graduationproject

基于深度强化学习的MEC计算卸载与资源分配

graduationproject-1

基于深度强化学习的MEC计算卸载与资源分配

graph-meta-rl-for-amod

Official implementation of "Graph Meta-Reinforcement Learning for TransferableAutonomous Mobility-on-Demand"

graph-reinforcement-learning-literature

This open source library is available to summarize several years of research papers on graph reinforcement learning for the convenience of researchers

green

[7] GREEN: A Global Energy Efficiency Maximization Strategy for Multi-UAV Enabled Communication Systems 作者：Lin N, Fan Y, Zhao L, et al. 出处：IEEE Transactions on Mobile Computing, 2022 摘要：在能源供应有限的情况下，无人机(UAV)支持的通信系统必须有效利用能源才能提供长期服务。在本文中，我们为支持多无人机的通信系统提出了全球能效最大化(GREEN)策略。在此类系统中，一组无人机通过使用支持无人机的干扰信道(UAV-IC)与其关联的地面终端(GT)进行通信。特别是，我们通过综合考虑通信吞吐量和整个系统的总能耗