An energy-efficient framework for internet of things underlaying heterogeneous small cell networks
Long-term evolution advanced (LTE-A) heterogeneous networks have been observed to
offer reliable and service-differentiated communication, thereby enabling numerous mobile …
offer reliable and service-differentiated communication, thereby enabling numerous mobile …
When deep reinforcement learning meets federated learning: Intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense …
Recently, smart cities, healthcare system, and smart vehicles have raised challenges on the
capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for …
capability and connectivity of state-of-the-art Internet-of-Things (IoT) devices, especially for …
Toward massive machine type communications in ultra-dense cellular IoT networks: Current issues and machine learning-assisted solutions
The ever-increasing number of resource-constrained machine-type communication (MTC)
devices is leading to the critical challenge of fulfilling diverse communication requirements …
devices is leading to the critical challenge of fulfilling diverse communication requirements …
Machine learning empowered resource allocation in IRS aided MISO-NOMA networks
A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output
(MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station …
(MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station …
DRLR: A deep-reinforcement-learning-based recruitment scheme for massive data collections in 6G-based IoT networks
T Li, W Liu, Z Zeng, NN Xiong - IEEE Internet of Things journal, 2021 - ieeexplore.ieee.org
Recently, rapid deployment on the fifth-generation (5G) networks has brought great
opportunities for enabling data-intensive applications and brings an extending expectation …
opportunities for enabling data-intensive applications and brings an extending expectation …
Machine learning techniques and a case study for intelligent wireless networks
With the widespread deployment of wireless technologies and IoT, 5G wireless networks will
support various communication connectivity and services for the huge number of wireless …
support various communication connectivity and services for the huge number of wireless …
A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges
In the fifth-generation (5G) mobile communication system, various service requirements of
different communication environments are expected to be satisfied. As a new evolution …
different communication environments are expected to be satisfied. As a new evolution …
Intelligent IoT connectivity: Deep reinforcement learning approach
In this paper, we propose a distributed solution to design a multi-hop ad hoc Internet of
Things (IoT) network where mobile IoT devices strategically determine their wireless …
Things (IoT) network where mobile IoT devices strategically determine their wireless …
Machine and deep learning for resource allocation in multi-access edge computing: A survey
With the rapid development of Internet-of-Things (IoT) devices and mobile communication
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm …
[HTML][HTML] Deep learning at the mobile edge: Opportunities for 5G networks
M McClellan, C Cervelló-Pastor, S Sallent - Applied Sciences, 2020 - mdpi.com
Mobile edge computing (MEC) within 5G networks brings the power of cloud computing,
storage, and analysis closer to the end user. The increased speeds and reduced delay …
storage, and analysis closer to the end user. The increased speeds and reduced delay …