LuJi110

LuJi110

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DRL-Networking

Research on incentive mechanism design in mobile crowdsensing and mobile edge computing by deep reinforcement learning approaches.

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sleipnir

A Monte-Carlo simulator for Mobile Edge/Cloud Computing

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KAIST_MEC_simulator

learning based mobile edge computing simulator

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Edge-Computing-Codes

Algorithm implementation for my Edge Computing-related papers.

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adaptive-federated-learning

Code for paper "Adaptive Federated Learning in Resource Constrained Edge Computing Systems"

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DROO

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks

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book-resource-allocation

Simulation code for the book “Optimal Resource Allocation in Coordinated Multi-Cell Systems” by Emil Björnson and Eduard Jorswieck, Foundations and Trends in Communications and Information Theory, vol. 9, no. 2-3, pp. 113-381, 2013

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Resources-Allocation-in-The-Edge-Computing-Environment-Using-Reinforcement-Learning

Simulated the scenario between edge servers and users with a clear graphic interface. Also, implemented the continuous control with Deep Deterministic Policy Gradient (DDPG) to determine the resources allocation (offload targets, computational resources, migration bandwidth) in the edge servers

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Online-Flexible-Resource-Allocation

Flexible resource allocation for edge cloud computing with reinforcement learning

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DDLO

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

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