Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Application of machine learning in wireless networks: Key techniques and open issues
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …
solving complex problems without explicit programming. Motivated by its successful …
Deep reinforcement learning-based mode selection and resource management for green fog radio access networks
Fog radio access networks (F-RANs) are seen as potential architectures to support services
of Internet of Things by leveraging edge caching and edge computing. However, current …
of Internet of Things by leveraging edge caching and edge computing. However, current …
[HTML][HTML] Intent-based networks for 6G: Insights and challenges
Abstract Intent-Based Networks (IBNs), which are originally proposed to introduce Artificial
Intelligence (AI) into the sixth-generation (6G) wireless networks, can effectively solve the …
Intelligence (AI) into the sixth-generation (6G) wireless networks, can effectively solve the …
Blockchain-based data sharing system for ai-powered network operations
The explosive development of mobile communications and networking has led to the
creation of an extremely complex system, which is difficult to manage. Hence, we propose …
creation of an extremely complex system, which is difficult to manage. Hence, we propose …
[HTML][HTML] Application of reinforcement learning and deep learning in multiple-input and multiple-output (MIMO) systems
The current wireless communication infrastructure has to face exponential development in
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
mobile traffic size, which demands high data rate, reliability, and low latency. MIMO systems …
Energy efficient 3-D UAV control for persistent communication service and fairness: A deep reinforcement learning approach
Recently, unmanned aerial vehicles (UAVs) as flying wireless communication platform have
attracted much attention. Benefiting from the mobility, UAV aerial base stations can be …
attracted much attention. Benefiting from the mobility, UAV aerial base stations can be …
ST-Tran: Spatial-temporal transformer for cellular traffic prediction
Q Liu, J Li, Z Lu - IEEE Communications Letters, 2021 - ieeexplore.ieee.org
Accurate cellular traffic prediction is conducive to managing communication networks, but
challenging, due to dynamic temporal variations and complicated spatial correlations. In this …
challenging, due to dynamic temporal variations and complicated spatial correlations. In this …
Artificial intelligence for wireless caching: Schemes, performance, and challenges
Wireless data traffic is growing unprecedentedly and it may impede network performance by
consuming an ever-greater amount of bandwidth. With the advancement in technology there …
consuming an ever-greater amount of bandwidth. With the advancement in technology there …
Reinforcement learning for intelligent healthcare systems: A comprehensive survey
The rapid increase in the percentage of chronic disease patients along with the recent
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …
pandemic pose immediate threats on healthcare expenditure and elevate causes of death …