A deep reinforcement learning for user association and power control in heterogeneous networks

H Ding, F Zhao, J Tian, D Li, H Zhang - Ad Hoc Networks, 2020 - Elsevier
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …

[HTML][HTML] Energy-efficient power allocation and user association in heterogeneous networks with deep reinforcement learning

CK Hsieh, KL Chan, FT Chien - Applied Sciences, 2021 - mdpi.com
This paper studies the problem of joint power allocation and user association in wireless
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …

Deep reinforcement learning for user association and resource allocation in heterogeneous networks

N Zhao, YC Liang, D Niyato, Y Pei… - 2018 IEEE Global …, 2018 - ieeexplore.ieee.org
Heterogeneous networks (HetNets) can offload the traffic and reduce the deployment cost,
which is regarded as a promising technique in next-generation cellular networks. Because …

[HTML][HTML] Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning

A Mughees, M Tahir, MA Sheikh, A Amphawan… - Physical …, 2023 - Elsevier
Small cells are a promising technique to improve the capacity and throughput of future
wireless networks. However, user association and power allocation in heterogeneous …

Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks

N Zhao, YC Liang, D Niyato, Y Pei… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
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 …

User association and power allocation based on Q-learning in ultra dense heterogeneous networks

D Li, H Zhang, K Long, W Huangfu… - 2019 IEEE Global …, 2019 - ieeexplore.ieee.org
Ultra dense heterogeneous network (UDHN) has become one of the main frameworks of 5G.
Traditional user association methods are difficult to satisfy this new scenario for load …

[HTML][HTML] Deep reinforcement learning-assisted optimization for resource allocation in downlink OFDMA cooperative systems

MK Tefera, S Zhang, Z Jin - Entropy, 2023 - mdpi.com
This paper considers a downlink resource-allocation problem in distributed interference
orthogonal frequency-division multiple access (OFDMA) systems under maximal power …

Deep reinforcement learning for multi-agent power control in heterogeneous networks

L Zhang, YC Liang - IEEE Transactions on Wireless …, 2020 - ieeexplore.ieee.org
We consider a typical heterogeneous network (HetNet), in which multiple access points
(APs) are deployed to serve users by reusing the same spectrum band. Since different APs …

A deep Q-learning method for downlink power allocation in multi-cell networks

KI Ahmed, E Hossain - arXiv preprint arXiv:1904.13032, 2019 - arxiv.org
Optimal resource allocation is a fundamental challenge for dense and heterogeneous
wireless networks with massive wireless connections. Because of the non-convex nature of …

A survey on applications of deep reinforcement learning in resource management for 5G heterogeneous networks

YL Lee, D Qin - 2019 Asia-Pacific Signal and Information …, 2019 - ieeexplore.ieee.org
Heterogeneous networks (HetNets) have been regarded as the key technology for fifth
generation (5G) communications to support the explosive growth of mobile traffics. By …