Online Learning-Based Beamforming for Rate-Splitting Multiple Access: A Constrained Bandit Approach

doi:10.1109/ICC45041.2023.10278992

	Online Learning-Based Beamforming for Rate-Splitting Multiple Access: A Constrained Bandit Approach
	Shangshang Wang; Jingye Wang; Yijie Mao; Ziyu Shao
	2023
会议录名称	IEEE ICC 2023
ISSN	1938-1883
卷号	2023-May
页码	559-564
发表状态	已发表
DOI	10.1109/ICC45041.2023.10278992
摘要	Rate-splitting multiple access (RSMA) has emerged as a potential non-orthogonal transmission strategy and powerful interference management scheme for 6G. Most of the existing works on RSMA beamforming design assume instantaneous or statistical channel state information (CSI) is available at the transmitter. Such an assumption however is impractical especially in massive multiple-input multiple-output (MIMO) due to the dynamic wireless environments and the challenges in channel estimation. In this work, we propose a novel beamforming design framework based on online learning and online control to adaptively learn the best precoding action for a RSMA-aided downlink massive MIMO without explicit CSI feedback. In particular, we first formulate the precoder selection problem that maximizes the ergodic sum-rate subject to a long-term transmit power constraint as a constrained combinatorial multi-armed bandit (CMAB) problem. Then we propose a precoder selection with bandit learning algorithm for RSMA (PBR). Our theoretical analysis shows that PBR achieves a sublinear regret bound with a long-term power constraint guarantee. Through experimental results, we not only verify our theoretical analysis but also demonstrate the outperformance of PBR in terms of sum-rate and power consumption compared with the conventional transmission schemes without using RSMA. © 2023 IEEE.
关键词	Wireless communication Learning systems Power demand Array signal processing Transmitters Precoding Massive MIMO
会议名称	2023 IEEE International Conference on Communications, ICC 2023
会议地点	Rome, Italy
会议日期	28 May-1 June 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20234815115287
EI主题词	Beamforming
EI分类号	711.2 Electromagnetic Waves in Relation to Various Structures ; 722.3 Data Communication, Equipment and Techniques ; 723.4.2 Machine Learning
原始文献类型	Conference article (CA)
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/281929
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_邵子瑜组信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_毛奕婕组
作者单位	School of Information Science and Technology, ShanghaiTech University, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Shangshang Wang,Jingye Wang,Yijie Mao,et al. Online Learning-Based Beamforming for Rate-Splitting Multiple Access: A Constrained Bandit Approach[C]:Institute of Electrical and Electronics Engineers Inc.,2023:559-564.