jingluozzz

jingluozzz's Stars

• krishnakumarsekar/awesome-quantum-machine-learning

  Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

  Language:HTML2.7k2263674

• marlbenchmark/on-policy

  This is the official implementation of Multi-Agent PPO (MAPPO).

  Language:Python1.3k794300

• kaixindelele/DRLib

  DRLib：a Concise Deep Reinforcement Learning Library, Integrating HER, PER and D2SR for Almost Off-Policy RL Algorithms.

  Language:Python51871371

• dwavesystems/dwave-ocean-sdk

  Installer for D-Wave's Ocean tools

  Language:Python4144072155

• xiaofangxd/Multitasking-Optimization

  Summary of Multitasking Optimization

  692015

• wuzhiyuan2000/FedAgg

  [INFOCOM 2024] Agglomerative Federated Learning: Empowering Larger Model Training via End-Edge-Cloud Collaboration

  Language:Python482110

• Dronie/D2D_A2C

  Channel Selection and Power Control for D2D Communication via Online Reinforcement Learning

  Language:Python374715

• ofanan/SFC_migration

  This project simulates deployment and migration of Service Function Chains (SFC) in data-centers.

  Language:Python31120

• HL1122/Resource-Allocation-D2D

  Resource allocation for Device-to-Device (D2D) communications using deep reinforcement learning.

  Language:Python30105

• yagol2020/PaperWebCrawler

  IEEE XPLORE等文献网站的爬虫工具/Crawler for Paper Website like IEEE XPLORE

  Language:Java29404

• linkpark/pomdp-service-migration

  Language:Python281113

• NetworkCommunication/premigration

  Digital-twin Network Service Pre-migration Scheme for Vehicular Edge Computing

  Language:Python20110

• arjnklc/D2D-Caching-Simulation

  Hybrid network simulation with base station and satellite. It enables comparing D2D caching algorithms.

  Language:Python193111

• seotaijiya/TPC_D2D

  Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication

  Language:Python194219

• Emrran/5G-and-New-Data-Offloading-Scheme-over-D2D

  The idea of offloading 5G cellular data over WiFi is becoming very popular nowadays. Offloading data over active WiFi connection in Handsets may reduce the dependency on small cells in our environment, reduces the traffic at the Base Transceiver Station (BTS) and reduces the cost of setting up 5G infrastructure. Furthermore, offloading over WiFi does not compromise the 5G speed that is the huge amount of 5G data can be offloaded over WiFi connection in Handsets with the speed promised by the 5G cellular network. Many published paper suggested “delayed offloading” in which traffic will be delayed up to an allowed deadline if the WiFi connection is inactive or until the WiFi connection again becomes available. In this paper, we proposed a routing scheme for offloading 5G cellular data where the data at a user’s handset will first collect in WiFi queue to offload over WiFi and if the WiFi connection is inactive in the user’s handset then the data will wait in the queue for a given deadline and while it waits in the WiFi queue, the user’s handset will try to set up a device to device (D2D) connection with a neighboring handset with an active WiFi connection. When the deadline will reach for the data in the WiFi queue, the user data will be sent to the neighboring handset which has an active WiFi connection and which will allow the user’s data to be offloaded through it, otherwise, if no handset with an active WiFi connection is found in the environment, after the deadline the data will be sent in the typical gruesome manner to the BTS through several small cells.

  Language:MATLAB17106

• shyamal-dhua/D2D

  Resource Allocation and Interference Cancellation in D2D Communication

  Language:Python16315

• yanzhenliu97/Latency-Minimization-in-Intelligent-Reflecting-Surface-Assisted-D2D-Offloading-Systems

  Language:Python14202

• paulosevero/argos

  Privacy-aware service migration strategy for edge computing environments

  Language:Python13202

• AnirbanBanik1998/SPARQ

  Mobility-aware Dynamic Joint Power Control and Resource Allocation for D2D underlaying cellular networks

  Language:Python12122

• qiongwu86/Resource-allocation-for-twin-maintenance-and-computing-tasks-in-digital-twin-mobile-edge-network

  Language:Python12103

• qiongwu86/Digital-Twin-Vehicular-Edge-Computing-Network_Task-Offloading-and-Resource-Allocation

  Language:Python9132

• charyco/A-Social-Relationship-Awareness-Based-Dependent-Task-Offloading-Algorithm-for-Mobile-Edge-Computing

  This study presents a task offloading algorithm that considers social relationships, task dependencies, energy consumption, and latency. It optimizes four modes—local, D2D, edge, and mixed execution—using the Hungarian algorithm. Experiments validate its effectiveness for mobile devices, IoT, data centers, and intelligent vehicles.

  Language:Python7

• che10086/ieee-xplore-Web-crawler

  ieee爬虫工具，输入想要搜索的ieee关键词，一键输出搜索到的所有文章的标题，摘要，摘要翻译，检索日期，关键词，文章主页，输出文件为word。

  Language:Python73

• UNIC-Lab/LGNN-RGNN

  Language:Python7102

• UNIC-Lab/Qedgix

  70

• xiaofangxd/Graph-Neural-Network-and-Multi-Task-Learning

  7101

• Ayeps/bipartited2d

  Bipartite Graph Based Proportional Fair Resource Allocation for D2D Communication

  Language:Python6208

• CloudControlSystems/KCES

  KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework

  Language:Go61

• JustinShih7710/RNN-SDM_in_MEC

  A project record from my master's thesis, titled "A Service Deployment and Migration Method Based on RNN in Mobile Edge Computing Networks"

  Language:Python5

• marlomb/Binary-Quantum-Inspired-Particle-Swarm-Optimization

  Language:Python2101