Topology aware deep learning for wireless network optimization
S Zhang, B Yin, W Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Data-driven machine learning approaches have been proposed to facilitate wireless
network optimization by learning latent knowledge from historical optimization instances …
network optimization by learning latent knowledge from historical optimization instances …
Deep learning meets wireless network optimization: Identify critical links
With the superior capability of discovering intricate structure of large data sets, deep learning
has been widely applied in various areas including wireless networking. While existing deep …
has been widely applied in various areas including wireless networking. While existing deep …
Deep learning based optimization in wireless network
With the development of wireless networks, the scale of network optimization problems is
growing correspondingly. While algorithms have been designed to reduce complexity in …
growing correspondingly. While algorithms have been designed to reduce complexity in …
Deep learning for wireless networking: The next frontier
With the growth of mobile technology in the last decade, wireless networks have become an
integral part of our everyday lives. To meet the increasingly stringent application …
integral part of our everyday lives. To meet the increasingly stringent application …
A self-supervised learning approach for accelerating wireless network optimization
The prevailing issue in multi-hop wireless networking is interference management, which
militates against the efficiency of traditional routing and scheduling algorithms. We develop …
militates against the efficiency of traditional routing and scheduling algorithms. We develop …
Knowledge-driven deep learning paradigms for wireless network optimization in 6G
In the sixth-generation (6G) networks, newly emerging diversified services of massive users
in dynamic network environments are required to be satisfied by multi-dimensional …
in dynamic network environments are required to be satisfied by multi-dimensional …
Resilient topology design for wireless backhaul: A deep reinforcement learning approach
Ultra-dense 5G and beyond deployments are setting significant burden on cellular networks,
especially for wireless backhauls. Today, a careful planning for wireless backhaul is more …
especially for wireless backhauls. Today, a careful planning for wireless backhaul is more …
Machine learning for large-scale optimization in 6g wireless networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …
Network topology optimization via deep reinforcement learning
Topology impacts important network performance metrics, including link utilization,
throughput and latency, and is of central importance to network operators. However, due to …
throughput and latency, and is of central importance to network operators. However, due to …
Self-renewal machine learning approach for fast wireless network optimization
The throughput maximization in multi-hop wireless networks is largely limited by interference
due to the reuse of the channel resources. Although machine learning (ML) can accelerate …
due to the reuse of the channel resources. Although machine learning (ML) can accelerate …