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 …
Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
ColO-RAN: Developing machine learning-based xApps for open RAN closed-loop control on programmable experimental platforms
Cellular networks are undergoing a radical transformation toward disaggregated, fully
virtualized, and programmable architectures with increasingly heterogeneous devices and …
virtualized, and programmable architectures with increasingly heterogeneous devices and …
Thirty years of machine learning: The road to Pareto-optimal wireless networks
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …
complex compelling applications both in military and civilian fields, where the users are able …
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 …
Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications
Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …
interconnected devices, allowing the use of various smart applications. The enormous …
Delay-aware and energy-efficient computation offloading in mobile-edge computing using deep reinforcement learning
Internet of Things (IoT) is considered as the enabling platform for a variety of promising
applications, such as smart transportation and smart city, where massive devices are …
applications, such as smart transportation and smart city, where massive devices are …
Deep multi-user reinforcement learning for distributed dynamic spectrum access
O Naparstek, K Cohen - IEEE transactions on wireless …, 2018 - ieeexplore.ieee.org
We consider the problem of dynamic spectrum access for network utility maximization in
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …
multichannel wireless networks. The shared bandwidth is divided into K orthogonal …