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

Yifan Zhai, Zhongjun Ma, Bo He, Wenhui Xu, Zhenxing Li, Jie Wang, Hongyi Miao, Aobo Gao and Yewen Cao ()
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Yifan Zhai: School of Information Science and Engineering, Shandong University, Binhai Road, Qingdao 266237, China
Zhongjun Ma: Shandong Future Network Research Institute, Jinan 250003, China
Bo He: School of Information Science and Engineering, Shandong University, Binhai Road, Qingdao 266237, China
Wenhui Xu: School of Information Science and Engineering, Shandong University, Binhai Road, Qingdao 266237, China
Zhenxing Li: China Research Institute of Radiowave Propagation, Qingdao 266107, China
Jie Wang: China Research Institute of Radiowave Propagation, Qingdao 266107, China
Hongyi Miao: School of Information Science and Engineering, Shandong University, Binhai Road, Qingdao 266237, China
Aobo Gao: School of Information Science and Engineering, Shandong University, Binhai Road, Qingdao 266237, China
Yewen Cao: School of Information Science and Engineering, Shandong University, Binhai Road, Qingdao 266237, China

Mathematics, 2025, vol. 13, issue 19, 1-17

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.

Keywords: cognitive satellite–terrestrial network; DQN; power allocation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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