A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks

X Liao, J Shi, Z Li, L Zhang, B Xia - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem
since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) …

Deep-learning-based wireless resource allocation with application to vehicular networks

L Liang, H Ye, G Yu, GY Li - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

Improved genetic algorithm based intelligent resource allocation in 5G Ultra Dense networks

S Xu, R Li, Q Yang - 2018 IEEE wireless communications and …, 2018 - ieeexplore.ieee.org
As a key technology, it is expected that the Ultra Dense network (UDN) architecture will play
a key role in supporting the fifth generation (5G) of mobile communication technologies …

Learning to branch: Accelerating resource allocation in wireless networks

M Lee, G Yu, GY Li - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
Resource allocation in wireless networks, such as device-to-device (D2D) communications,
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …

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 …

Deep reinforcement learning for joint spectrum and power allocation in cellular networks

YS Nasir, D Guo - 2021 IEEE Globecom Workshops (GC …, 2021 - ieeexplore.ieee.org
A wireless network operator typically divides its radio spectrum into a number of subbands
and reuse them to serve traffic in many cells. To mitigate co-channel interference, allocation …

Energy-efficient resource allocation strategy in ultra dense small-cell networks: A Stackelberg game approach

L Xu, Y Mao, S Leng, G Qiao… - 2017 IEEE International …, 2017 - ieeexplore.ieee.org
This paper focuses on resource allocation in heterogeneous Ultra Dense small-cell
Networks (UDNs), in which massive overlaid small cells are under the coverage of a macro …

A cluster-based energy-efficient resource management scheme for ultra-dense networks

L Liang, W Wang, Y Jia, S Fu - IEEE Access, 2016 - ieeexplore.ieee.org
Ultra-dense networks (UDNs), which can provide extremely high throughput and data rates,
have been considered as one of the key techniques for the fifth generation mobile networks …

Artificial intelligence-based resource allocation in ultradense networks: Applying event-triggered Q-learning algorithms

H Zhang, M Feng, K Long… - IEEE Vehicular …, 2019 - ieeexplore.ieee.org
Ultradense networks (UDNs) have emerged as a promising architecture that can support the
extremely high demand for data traffic in the future. Through the dense deployment of …

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 …