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Joint Power Allocation Algorithm Based on Multi-Agent DQN in Cognitive Satellite–Terrestrial Mixed 6G Networks.

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Title: Joint Power Allocation Algorithm Based on Multi-Agent DQN in Cognitive Satellite–Terrestrial Mixed 6G Networks.
Authors: Zhai, Yifan, Ma, Zhongjun, He, Bo, Xu, Wenhui, Li, Zhenxing, Wang, Jie, Miao, Hongyi, Gao, Aobo, Cao, Yewen
Source: Mathematics (2227-7390); Oct2025, Vol. 13 Issue 19, p3133, 17p
Subject Terms: 6G networks, REINFORCEMENT learning, COMPARATIVE studies, INTELLIGENT agents, POWER resources management, NETWORK performance, TELECOMMUNICATION systems, INTERFERENCE suppression
Abstract: The Cognitive Satellite–Terrestrial Network (CSTN) is an important infrastructure for the future development of 6G communication networks. This paper focuses on a potential communication scenario, where satellite users (SUs) dominate and are selected as the primary users, and terrestrial base station users (TUs) are the secondary users. Additionally, each terrestrial base station owns multiple antennae, and the interference of TUs to SUs in the CSTN is limited to a low level or below. In this paper, based on the observation of diversity and the time-varying characteristics of a variety of user requirements, a multi-agent deep Q-network algorithm under interference limitation (MADQN-IL) was proposed, where the power of each antenna in the base station is allocated to maximize the total system throughput while meeting the interference constraints in the CSTN. In our proposed MADQN-IL, the base stations play the role of intelligent agents, and each agent selects the antenna power allocation and cooperates with other agents through sharing system states and the total rewards. Through a simulation comparison, it was discovered that the MADQN-IL algorithm can achieve a higher system throughput than the adaptive resource adjustment (ARA) algorithm and the fixed power allocation methods. [ABSTRACT FROM AUTHOR]
Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Joint Power Allocation Algorithm Based on Multi-Agent DQN in Cognitive Satellite–Terrestrial Mixed 6G Networks.
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  Data: <searchLink fieldCode="AR" term="%22Zhai%2C+Yifan%22">Zhai, Yifan</searchLink><br /><searchLink fieldCode="AR" term="%22Ma%2C+Zhongjun%22">Ma, Zhongjun</searchLink><br /><searchLink fieldCode="AR" term="%22He%2C+Bo%22">He, Bo</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Wenhui%22">Xu, Wenhui</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhenxing%22">Li, Zhenxing</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jie%22">Wang, Jie</searchLink><br /><searchLink fieldCode="AR" term="%22Miao%2C+Hongyi%22">Miao, Hongyi</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Aobo%22">Gao, Aobo</searchLink><br /><searchLink fieldCode="AR" term="%22Cao%2C+Yewen%22">Cao, Yewen</searchLink>
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  Data: Mathematics (2227-7390); Oct2025, Vol. 13 Issue 19, p3133, 17p
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  Data: <searchLink fieldCode="DE" term="%226G+networks%22">6G networks</searchLink><br /><searchLink fieldCode="DE" term="%22REINFORCEMENT+learning%22">REINFORCEMENT learning</searchLink><br /><searchLink fieldCode="DE" term="%22COMPARATIVE+studies%22">COMPARATIVE studies</searchLink><br /><searchLink fieldCode="DE" term="%22INTELLIGENT+agents%22">INTELLIGENT agents</searchLink><br /><searchLink fieldCode="DE" term="%22POWER+resources+management%22">POWER resources management</searchLink><br /><searchLink fieldCode="DE" term="%22NETWORK+performance%22">NETWORK performance</searchLink><br /><searchLink fieldCode="DE" term="%22TELECOMMUNICATION+systems%22">TELECOMMUNICATION systems</searchLink><br /><searchLink fieldCode="DE" term="%22INTERFERENCE+suppression%22">INTERFERENCE suppression</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The Cognitive Satellite–Terrestrial Network (CSTN) is an important infrastructure for the future development of 6G communication networks. This paper focuses on a potential communication scenario, where satellite users (SUs) dominate and are selected as the primary users, and terrestrial base station users (TUs) are the secondary users. Additionally, each terrestrial base station owns multiple antennae, and the interference of TUs to SUs in the CSTN is limited to a low level or below. In this paper, based on the observation of diversity and the time-varying characteristics of a variety of user requirements, a multi-agent deep Q-network algorithm under interference limitation (MADQN-IL) was proposed, where the power of each antenna in the base station is allocated to maximize the total system throughput while meeting the interference constraints in the CSTN. In our proposed MADQN-IL, the base stations play the role of intelligent agents, and each agent selects the antenna power allocation and cooperates with other agents through sharing system states and the total rewards. Through a simulation comparison, it was discovered that the MADQN-IL algorithm can achieve a higher system throughput than the adaptive resource adjustment (ARA) algorithm and the fixed power allocation methods. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/math13193133
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 3133
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      – SubjectFull: 6G networks
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      – SubjectFull: REINFORCEMENT learning
        Type: general
      – SubjectFull: COMPARATIVE studies
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      – SubjectFull: INTELLIGENT agents
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      – SubjectFull: POWER resources management
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      – SubjectFull: NETWORK performance
        Type: general
      – SubjectFull: TELECOMMUNICATION systems
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      – SubjectFull: INTERFERENCE suppression
        Type: general
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      – TitleFull: Joint Power Allocation Algorithm Based on Multi-Agent DQN in Cognitive Satellite–Terrestrial Mixed 6G Networks.
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            NameFull: Zhai, Yifan
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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