Deep reinforcement learning

The Zip file contain the videos of the indoor/outdoor test, as well as the data logged during the flights and the CAD files to replicate the balloon systems.

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In emerging vehicular networks, delay-sensitive tasks can be processed in real time by offloading to

the edge computing servers. Unlike the legacy scenarios, in this paper a novel data offloading/delivery

decision making framework is proposed, where users have the option to divide their task into several

portions and partially offload their data to a complex multi-access edge computing (MEC) environment,

consisting of several MEC servers located on road side units (RSUs), base stations (BSs), and unused

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90 Views

In this paper, we propose a novel cooperative resource sharing in a multi-tier edge slicing networks which is

robust to imperfect channel state information (CSI) caused by user equipments’ (UEs) mobility. Due to the mobility

of UEs, the dynamic requirements of their tasks, and the limited resources of the network, we propose a smart joint

dynamic pricing and resources sharing (SJDPRS) scenario that can incentivize the infrastructure provider (InP) and

mobile network operators (MNOs). Aiming to maximize the profits of UEs, MNOs and the InP under the task

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247 Views

# -*- coding: utf-8 -*-

"""

Created on Wed Feb 26 11:19:38 2020

 

@author: ali nouruzi

"""

 

import numpy as np

import random

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317 Views