Deep reinforcement learning
This paper investigates resource management in device-to-device (D2D) networks coexisting with mobile cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network’s energy needs, we formulate an online optimization problem.
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323 Views
In this paper, we propose a dual-loop control strategy to address the problems of the interference by the human-machine interaction of the lower limb exoskeleton movement. The outer ring adopts admittance control and the human-machine interaction torque is estimated by the generalized momentum observer based on Kalman filter. The inner ring adopts PID control based on DDPG.
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128 Views
Intelligence and flexibility are the two main requirements for next-generation networks that can be implemented in network slicing (NetS) technology.This intelligence and flexibility can have different indicators in networks, such as proactivity and resilience. In this paper, we propose a novel proactive end-to-end (E2E) resource management in a packet-based model, supporting NetS.
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251 Views
The 33-, 119-, and 136-bus datasets are commonly used in the field of power systems and electrical engineering to train reinforcement learning-based algorithms for distribution network reconfiguration. Distribution network reconfiguration involves altering the topology of the electrical distribution grid by opening or closing switches to optimize certain objectives, such as minimizing power losses, improving voltage profiles, or enhancing overall system efficiency. This process is essential for maintaining a reliable and cost-effective power distribution system.
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1025 Views
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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154 Views
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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397 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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356 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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407 Views