[HTML][HTML] Channel assignment and power allocation for throughput improvement with ppo in b5g heterogeneous edge networks
Abstract In Beyond the Fifth Generation (B5G) heterogeneous edge networks, numerous
users are multiplexed on a channel or served on the same frequency resource block, in …
users are multiplexed on a channel or served on the same frequency resource block, in …
Collaborative machine learning for energy-efficient edge networks in 6G
To fulfill the diversified requirements of the emerging Internet of Everything (IoE)
applications, the future sixth generation (6G) mobile network is envisioned as a …
applications, the future sixth generation (6G) mobile network is envisioned as a …
Toward a smart resource allocation policy via artificial intelligence in 6G networks: Centralized or decentralized?
In this paper, we design a new smart softwaredefined radio access network (RAN)
architecture with important properties like flexibility and traffic awareness for sixth generation …
architecture with important properties like flexibility and traffic awareness for sixth generation …
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 …
Bess aided renewable energy supply using deep reinforcement learning for 5g and beyond
The year of 2020 has witnessed the unprecedented development of 5G networks, along with
the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy …
the widespread deployment of 5G base stations (BSs). Nevertheless, the enormous energy …
Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
devices connected to the Internet. In the wake of disruptive IoT with a huge amount and …
Multi-objective optimization of energy saving and throughput in heterogeneous networks using deep reinforcement learning
K Ryu, W Kim - Sensors, 2021 - mdpi.com
Wireless networking using GHz or THz spectra has encouraged mobile service providers to
deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As …
deploy small cells to improve link quality and cell capacity using mmWave backhaul links. As …
Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …
Federated reinforcement learning-based resource allocation in D2D-enabled 6G
The current 5G and conceived 6G era with ultra-high density, ultra-high frequency
bandwidth, and ultra-low latency can support emerging applications like Extended Reality …
bandwidth, and ultra-low latency can support emerging applications like Extended Reality …
Hypergraph based resource-efficient collaborative reinforcement learning for B5G massive IoT
F Yang, C Yang, J Huang, K Yu, S Garg… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Beyond 5G (B5G) networks rapidly growing to connect billions of Internet of Things (IoT)
devices and the dense deployment of IoT devices leads the large-scale network conflict and …
devices and the dense deployment of IoT devices leads the large-scale network conflict and …