Pinned Repositories
2019_summer_internship_zy
2019暑假实习，数据处理项目
ComputationOffloadingRL
Matlab project for fairspace
D2D-Caching-Simulation
Hybrid network simulation with base station and satellite. It enables comparing D2D caching algorithms.
DeepCC.v1.0
DeepCC: A Deep Reinforcement Learning Plug-in to Boost the performance of your TCP scheme in Cellular Networks!
Edge-Intelligence
随着移动云计算和边缘计算的快速发展，以及人工智能的广泛应用，产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中，但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算，例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂，并且可能导致模型精度的下降。在移动云计算或边缘计算中，任务卸载技术可以突破移动终端的资源限制，减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的思想，将深度神经网络进行分区，一部分层在移动端计算，而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区，降低时延以及能耗。本项目给出了边缘智能方面的相关论文，并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词：边缘智能(Edge Intelligence)，计算卸载(Computing Offloading)，CNN模型分区(CNN Partition)，协同推断(Collaborative Inference)，移动云计算(Mobile Cloud Computing)
IoRLO
Intent-oriented Offloading algorithm
NBIoT-D2D-Sim
Narrowband Internet of Things (NB-IoT) simulation model integrated with device-to-device (D2D) capabilities.
network_programming_HW
UESTC-信软-网络编程作业
Relay-nodes-Tx-Power-Optimization-for-effective-D2D-communication
Transmission Capacity in Overlay and Underlay D2D comm networks
The-JUAD-resource-allocation-for-D2D-in-a-FDD-cellular-network
WCSP：（1）Joint Uplink and Downlink Resource Allocation for D2D Communications Underlying Cellular Networks
ZealYa's Repositories
ZealYa/ComputationOffloadingRL
Matlab project for fairspace
ZealYa/Relay-nodes-Tx-Power-Optimization-for-effective-D2D-communication
Transmission Capacity in Overlay and Underlay D2D comm networks
ZealYa/DeepCC.v1.0
DeepCC: A Deep Reinforcement Learning Plug-in to Boost the performance of your TCP scheme in Cellular Networks!
ZealYa/AltFreezing
[CVPR 2023 Highlight] Official implementation of the paper: "AltFreezing for More General Video Face Forgery Detection"
ZealYa/carla-rl
Environments and Wrappers for CARLA, designed for ease of use with RL Tasks.
ZealYa/cs231n.github.io
Public facing notes page
ZealYa/d2d-vehicle-simulator
ZealYa/Deep-Compressive-Offloading
Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network Latency
ZealYa/DeepRL
Berkeley CS285 2019 homework solution
ZealYa/Dependency-Aware-Computation-Offloading-for-Mobile-Edge-Computing-with-Edge-Cloud-Cooperation
The code for paper titled "Dependency-Aware-Computation-Offloading-for-Mobile-Edge-Computing-with-Edge-Cloud-Cooperation"
ZealYa/DROO
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks
ZealYa/Efficient-network-access-with-MPTCP
ZealYa/Framework-To-Speed-Up-RACH-BLE
Random-Access Accelerator (RAA): A Framework to speed up the Random-Access procedure in 5G New Radio by enabling D2D communications with BLE
ZealYa/FTCN
[Official] Exploring Temporal Coherence for More General Video Face Forgery Detection(ICCV 2021)
ZealYa/geolib
Python geohash library
ZealYa/gym-d2d
Device-to-Device (D2D) communication OpenAI Gym environment
ZealYa/learn-to-branch-d2d
ZealYa/MECOptimalOffloading
Optimization of Offloading Scheme Algorithm for Large Number of Tasks in Mobile-Edge Computing
ZealYa/Minimum-Interference-and-power-consumption-approach-for-Underlay-D2D-model
The most optimum route from source to target involving the least number of active users using the Minimum Active Node Algorithm (MAN) to reduce the interference and power consumed.
ZealYa/mptcp_with_machine_learning-master1
mptcp_with_machine_learning-master1
ZealYa/ns3-gym
ns3-gym - The Playground for Reinforcement Learning in Networking Research
ZealYa/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
ZealYa/rl-book
Source codes for the book "Reinforcement Learning: Theory and Python Implementation"
ZealYa/RL-TCP
Reinforcement Learning based TCP congestion control
ZealYa/RL-tcp-toycase
Toy case for learning through Reinforcement Learning algorithms how to establish TCP connections.
ZealYa/rl_channel_allocation
Using Q-Learning to solve a modified bin packing type problem.
ZealYa/RLinWiFi
Code for "Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning" article published at WCNC 2021.
ZealYa/ScanRL
Implementation code of Scan-RL from the 2020 ECCV Workshop paper "Next-Best View Policy for 3D Reconstruction".
ZealYa/self-paced-rl
Implementation of the Self Paced Reinforcement Learning Experiments
ZealYa/Task-Offloading-and-Resource-Allocation-for-Multi-Server-Mobile-Edge-Computing-Networks