Sensing, computing, and communications for energy harvesting IoTs: A survey
With the growing number of deployments of Internet of Things (IoT) infrastructure for a wide
variety of applications, the battery maintenance has become a major limitation for the …
variety of applications, the battery maintenance has become a major limitation for the …
Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service
To satisfy the expected plethora of demanding services, the future generation of wireless
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
Modeling and analysis of energy harvesting and smart grid-powered wireless communication networks: A contemporary survey
The advancements in smart power grid and the advocation of “green communications” have
inspired the wireless communication networks to harness energy from ambient …
inspired the wireless communication networks to harness energy from ambient …
Ensemble graph q-learning for large scale networks
The optimization of large-scale networks such as finding the optimal control strategies
through cost minimization is challenged by large state spaces. For networks that can be …
through cost minimization is challenged by large state spaces. For networks that can be …
Optimized shallow neural networks for sum-rate maximization in energy harvesting downlink multiuser NOMA systems
This article considers a power allocation problem in energy harvesting downlink non-
orthogonal multiple access (NOMA) systems in which a transmitter sends desired messages …
orthogonal multiple access (NOMA) systems in which a transmitter sends desired messages …
On sampled reinforcement learning in wireless networks: Exploitation of policy structures
Reinforcement learning is a classical tool to solve network control or policy optimization
problems in unknown environments. In order to learn the optimal policy correctly, the …
problems in unknown environments. In order to learn the optimal policy correctly, the …
Online learning of optimal proactive schedule based on outdated knowledge for energy harvesting powered Internet-of-Things
This paper aims to produce an effective online scheduling technique, where a base station
(BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) …
(BS) schedules the transmissions of energy harvesting-powered Internet-of-Things (IoT) …
Mobile edge computing against smart attacks with deep reinforcement learning in cognitive MIMO IoT systems
Abstract In wireless Internet of Things (IoT) systems, the multi-input multi-output (MIMO) and
cognitive radio (CR) techniques are usually involved into the mobile edge computing (MEC) …
cognitive radio (CR) techniques are usually involved into the mobile edge computing (MEC) …
Detecting reinforcement learning-based Grey hole attack in Mobile wireless sensor networks
Mobile wireless sensor networks (WSNs) are facing threats from malicious nodes that
disturb packet transmissions, leading to poor mobile WSN performance. Existing studies …
disturb packet transmissions, leading to poor mobile WSN performance. Existing studies …
Optimize mobile wireless power transfer by finite state machine reinforcement learning
Y **ng, R Young, G Nguyen, M Lefebvre… - 2022 IEEE 12th …, 2022 - ieeexplore.ieee.org
This paper addresses the optimization problems in far-field wireless power transfer systems
using Reinforcement Learning techniques. The mobile Radio-Frequency (RF) wireless …
using Reinforcement Learning techniques. The mobile Radio-Frequency (RF) wireless …