Artificial neural networks-based machine learning for wireless networks: A tutorial

M Chen, U Challita, W Saad, C Yin… - … Surveys & Tutorials, 2019 - ieeexplore.ieee.org
In order to effectively provide ultra reliable low latency communications and pervasive
connectivity for Internet of Things (IoT) devices, next-generation wireless networks can …

Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions

ME Morocho-Cayamcela, H Lee, W Lim - IEEE access, 2019 - ieeexplore.ieee.org
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …

Deep-reinforcement learning multiple access for heterogeneous wireless networks

Y Yu, T Wang, SC Liew - IEEE journal on selected areas in …, 2019 - ieeexplore.ieee.org
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …

Liquid state machine learning for resource and cache management in LTE-U unmanned aerial vehicle (UAV) networks

M Chen, W Saad, C Yin - IEEE Transactions on Wireless …, 2019 - ieeexplore.ieee.org
In this paper, the problem of joint caching and resource allocation is investigated for a
network of cache-enabled unmanned aerial vehicles (UAVs) that service wireless ground …

A dynamic matching scheme between licensed user and cognitive User based on deep neural network with Q-learning

M Tao, F Lin, Y Che - 2019 IEEE 5th International Conference …, 2019 - ieeexplore.ieee.org
In the cognitive radio network where licensed user (LU) and cognitive user (CU) coexist,
both LU and CU should benefit from their cooperation. So, we study the dynamic matching …

Exploiting user contention to optimize proactive resource allocation in future networks

V Paranthaman - 2019 - repository.mdx.ac.uk
In order to provide ubiquitous communication, seamless connectivity is now required in all
environments including highly mobile networks. By using vertical handover techniques it is …

[引用][C] HETEROGENEOUS NETWORK TRAFFIC CONTROL USING ARTIFICIAL INTELLIGENCE

SV Sudha - learning