Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks

doi:10.1109/TWC.2022.3207918

	Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks
	Du, Xiao 1,2; Wang, Ting 1; Feng, Qiang 3; Ye, Chenhui 3; Tao, Tao 3; Wang, Lu 4; Shi, Yuanming5 ; Chen, Mingsong 1
	2022
发表期刊	IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (IF:8.9[JCR-2023],8.6[5-Year])
ISSN	1536-1276
EISSN	1558-2248
卷号	22 期号:3 页码:1-1
发表状态	已发表
DOI	10.1109/TWC.2022.3207918
摘要	The 6G network enables a subnetwork-wide evolution, resulting in a network of subnetworks". However, due to the dynamic mobility of wireless subnetworks, the data transmission of intra-subnetwork and inter-subnetwork will inevitably interfere with each other, which poses a great challenge to radio resource management. Moreover, most existing approaches require the instantaneous channel gain between subnetworks, which are usually difficult to be collected. To tackle these issues, in this paper we propose a novel effective intelligent radio resource management method using multi-agent deep reinforcement learning (MARL), which only needs the sum of received power, named received signal strength indicator (RSSI), on each channel instead of channel gains. However, to directly separate individual interference from RSSI is an almost impossible thing. To this end, we further propose a novel MARL architecture, named GA-Net, which integrates a hard attention layer to model the importance distribution of inter-subnetwork relationships based on RSSI and excludes the impact of unrelated subnetworks, and employs a graph attention network with a multi-head attention layer to exact the features and calculate their weights that will impact individual throughput. Experimental results prove that our proposed framework significantly outperforms both traditional and MARL-based methods in various aspects. IEEE
关键词	Deep learning Fertilizers Information management Multi agent systems Natural resources management Power control Radio interference Radio transmission Resource allocation Scheduling 6g mobile communication Dynamic scheduling Graph neural networks Interference Interference mitigation Mobile communications Multi agent Multi-agent DRL Power-control Resource management Subnetworks Wireless communications
URL	查看原文
收录类别	EI ; SCI
语种	英语
资助项目	National Natural Science Foundation of China[61872147] ; Shenzhen Science and Technology Plan Project[CJGJZD20210408092400001]
WOS研究方向	Engineering ; Telecommunications
WOS类目	Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001049998100029
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20224112878111
EI主题词	Reinforcement learning
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 716.3 Radio Systems and Equipment ; 723.4 Artificial Intelligence ; 731.3 Specific Variables Control ; 804 Chemical Products Generally ; 821.2 Agricultural Chemicals ; 912.2 Management
原始文献类型	Article in Press
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/241105
专题	信息科学与技术学院信息科学与技术学院_PI研究组_石远明组
通讯作者	Wang, Ting
作者单位	1.East China Normal Univ, Shanghai Key Lab Trustworthy Comp, MoE Engn Res Ctr Software Hardware Co Design Tech, Shanghai 200062, Peoples R China 2.Bell Labs, Shanghai 201206, Peoples R China 3.Nokia Shanghai Bell Corp, Bell Labs, Shanghai 201206, Peoples R China 4.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China 5.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
推荐引用方式 GB/T 7714	Du, Xiao,Wang, Ting,Feng, Qiang,et al. Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,2022,22(3):1-1.
APA	Du, Xiao.,Wang, Ting.,Feng, Qiang.,Ye, Chenhui.,Tao, Tao.,...&Chen, Mingsong.(2022).Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks.IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS,22(3),1-1.
MLA	Du, Xiao,et al."Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks".IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 22.3(2022):1-1.