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 …
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 …
A dynamic adjusting reward function method for deep reinforcement learning with adjustable parameters
Z Hu, K Wan, X Gao, Y Zhai - Mathematical Problems in …, 2019 - Wiley Online Library
In deep reinforcement learning, network convergence speed is often slow and easily
converges to local optimal solutions. For an environment with reward saltation, we propose …
converges to local optimal solutions. For an environment with reward saltation, we propose …
GSMAC: GAN-based signal map construction with active crowdsourcing
With the dawn of 5G network, a new set of requirements for site spectrum monitoring,
location-based services (LBS), network construction, and cellular planning are emerging, all …
location-based services (LBS), network construction, and cellular planning are emerging, all …
Mobile parking incentives for vehicular networks: a deep reinforcement learning approach
M Yang, N Liu, L Zuo, H Gong, M Liu, M Liu - CCF Transactions on …, 2020 - Springer
In vehicular networks, parked vehicles can join vehicular communication as static nodes, but
encouraging people to share their wireless devices during parking still suffer from user …
encouraging people to share their wireless devices during parking still suffer from user …