zealya Goto Github PK

d2d_a2c

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

d2d_cellular_network_simulator

Simulator used in the study "D2D frequency allocation using GADIA in a cellular network" (Matlab)

d2drelayselection

Code package of article "Proportional Selection of Mobile Relays in Millimeter-Wave Heterogeneous Networks"

d2dscheduler

d2dwifiassociation

Associate and locate user wifi nodes in LTE-D2D mode1

ddlo

Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks

deep-compressive-offloading

Deep Compressive Offloading: Speeding Up Neural Network Inference by Trading Edge Computation for Network Latency

deep-rl-dqn-tensorflow

TensorFlow implementation of Deep RL (Reinforcement Learning) papers based on deep Q-learning (DQN)

deepcc.v1.0

DeepCC: A Deep Reinforcement Learning Plug-in to Boost the performance of your TCP scheme in Cellular Networks!

deeprl

Berkeley CS285 2019 homework solution

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"

develop-an-intelligent-system-for-reducing-road-accident-through-the-d2d-communication

An Intelligent System that can reduce road accident by sending alert to the drivers of the vehicles via smartphones. To do that, an Android based application is developed which will collect data from a vehicle and send it to the server for calculation. After completing whole calculations, drivers will get the situation alert.

device-to-device

服务D2D、V2V仿真

dra_in_d2d

A Distributed Heuristic Approach to Allocate Resources in D2D Networks

drl-based-mec

Mobile Edge Computing Server based on Deep Reinforcement Learning (One-on-one)

droo

Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing 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**，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)

efficient-network-access-with-mptcp

flower_world

使用tensorflow和cnn做的图像识别，对四种花进行了分类。

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

geolib

Python geohash library

gitignore

A collection of useful .gitignore templates

group_equivariant_capsules_pytorch

Pytorch implementation of Group Equivariant Capsule Networks

gym-d2d

Device-to-Device (D2D) communication OpenAI Gym environment

iorlo

Intent-oriented Offloading algorithm

learn-to-branch-d2d

learngit

教程→ http://t.cn/zQ6LFwE 推送请使用UTF-8编码

learngit-git-test-lab--first

个人测试用

learning-d2d-task-off-loads

linear-inverse-rl-algorithms

Implementation of Linear Inverse Reinforcement Learning Algorithm (IRL) on Mountain Car Environment.