Reinforcement Learning Power Control Algorithm Based on Graph Signal Processing for Ultra-Dense Mobile Networks

Li, Yujie; Tang, Zhoujin; Lin, Zhijian; Gong, Yu; Du, Xiaojiang; Guizani, Mohsen

doi:10.1109/tnse.2021.3051660

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…Resource allocation techniques that include SE as a performance metric are discussed in this section. In [94], the authors considered a power control in downlink ultra-dense HetNet with different types of BSs. They suggest a reinforcement learning power allocation algorithm based on graph signal processing.…”

Section: Spectrum Efficiency (Se)mentioning

confidence: 99%

A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

et al. 2023

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The sixth generation (6G) mobile communication system is expected to meet the different service needs of modern communication scenarios. Heterogeneous networks (HetNets) have received a lot of attention in recent years due to their potential as a novel structure for evolutionary networks. When compared to homogeneous networks, HetNets provide more potential for spatial spectrum reuse and higher quality of service (QoS). However, effective resource management (RM) solutions are essential to prevent interference and accomplish spectrum sharing due to mutual interference. This paper presents a comprehensive review of resource management in 6G HetNets. The study aims to give crucial background on HetNets to aid in the creation of more effective methods in this field of study. First, a detailed examination of recent work is presented in resource management aspects such as power allocation, user association, mode selection, and spectrum allocation. Second, we identify the most severe challenges associated with the current resource management methods and propose suitable solutions. Finally, several open issues and emerging areas of research are highlighted.

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Section: Spectrum Efficiency (Se)mentioning

confidence: 99%

A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

et al. 2023

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“…To address this problem, many scholars have conducted studies. [4] proposed an enhanced learning power allocation algorithm based on graph signal processing to improve the throughput and spectral efficiency of the system. [5] proposed an adaptive power allocation method based on reinforcement learning, which reduces system disturbances and increases the capacity of the system.…”

Section: Introductionmentioning

confidence: 99%

A power control algorithm based on Dyna-Q learning for ultra-dense networks

Jia,

Wang,

Wang

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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With ultra-dense networks, the large number of densely deployed low-power base stations creates more serious interference problems for the network. To address this problem, we introduce Dyna-Q learning into the power control problem of ultra-dense networks and propose a power control algorithm based on Dyna-Q learning. Firstly, we build an experience pool to store the state and selected actions of the agent during the operation; Secondly, the agent observes the current state, uses an action selection strategy to choose the next action, and then interacts with the environment to update the Q table; Finally, the agent learn again based on the virtual experience provided by the experience pool. The simulation results show that the normalized capacity of the method in this paper is improved by 0.88b/s/Hz on average and the resource consumption is reduced by 9.9% on average compared with the existing methods.

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“…The power control for UDNs was studied. [14][15][16][17][18] In previous studies, 15,16,18 machine learning was utilized as a powerful tool to optimize the user power transmission. The obtained results were very interesting.…”

Section: Introductionmentioning

confidence: 99%

Uplink performance of nonorthogonal multiple access ultradense networks with power control

Lam¹,

Hiep

Tran

2022

Int J Communication

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Summary This paper studies power control in ultradense networks which are potential network models of 5G cellular systems. The power control model that bases on stretched path loss model (SPLM) is introduced to allow each user to proactively adjust its transmission power to adapt to power loss and network status. The moment‐generating function (MGF) is utilized through the paper to analyze the uplink coverage probability (UCP) in which it is assumed that the base station (BS) of interest does not have information about fast fading of interfering channels. The analytical results indicate that the proposed power control model can reduce user power consumption while maintaining the UCP. Two interesting facts have been stated when comparing the performance of the networks in good and bad channel conditions. First, the user equipment (UE) in bad channel conditions outperforms others in good channel conditions. Second, the deployment of nonorthogonal multiple access (NOMA) technique can significantly improve the area spectral efficiency (ASE) in bad channel conditions.

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Reinforcement Learning Power Control Algorithm Based on Graph Signal Processing for Ultra-Dense Mobile Networks

Cited by 6 publications

References 30 publications

A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

A Survey on Resource Management for 6G Heterogeneous Networks: Current Research, Future Trends, and Challenges

A power control algorithm based on Dyna-Q learning for ultra-dense networks

Uplink performance of nonorthogonal multiple access ultradense networks with power control

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