Machine Learning-Enabled RIS-Assisted mmWave NOMA Systems: RIS Partitioning, Beamforming Design, and Power Allocation

In this paper, a reconfigurable intelligent surface(RIS)-assisted millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication system is considered, where the RIS is virtually partitioned into several sub-RISs to serve different user clusters. We aim to maximize the sum achievable ra...

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Vydáno v:	IEEE transactions on communications Ročník 73; číslo 9; s. 7617 - 7632
Hlavní autoři:	Wei, Tongyi, Chen, Weizhi, Tang, Kun, Zheng, Beixiong, Feng, Wenjie, Che, Wenquan, Xue, Quan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.09.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Array signal processing Artificial neural networks Beamforming Clustering Clustering algorithms Communications systems Deep learning Job shop scheduling Machine learning machine learning (ML) Machine learning algorithms Millimeter wave communication Millimeter waves millimeter-wave NOMA non-orthogonal multiple access (NOMA) Nonorthogonal multiple access Optimization Partitioning Partitioning algorithms Probabilistic models Reconfigurable intelligent surface (RIS) Reconfigurable intelligent surfaces Resource management Sums
ISSN:	0090-6778, 1558-0857
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Popis
Shrnutí:	In this paper, a reconfigurable intelligent surface(RIS)-assisted millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication system is considered, where the RIS is virtually partitioned into several sub-RISs to serve different user clusters. We aim to maximize the sum achievable rate of the considered system by jointly optimizing user clustering, RIS partitioning, active beamforming, passive beamforming, and power allocation, subject to the quality-of-service (QoS) requirements of each user and the maximum transmit power constraint of the base station (BS). To tackle the formulated non-convex joint optimization problem, a three-stage algorithm based on machine learning (ML) is proposed. In the first stage, user clustering is performed by combining the K-means algorithm and the Gaussian mixture model (GMM). In the second stage, a deep learning (DL)-based RIS partitioning method is proposed, which utilizes double cascaded deep neural networks (DNNs) to partition the RIS into multiple sub-RISs virtually, each of which serves a user cluster. In the third stage, a deep reinforcement learning (DRL) algorithm based on deep deterministic policy gradient (DDPG) is invoked to obtain the active beamforming, passive beamforming, and power allocation. Simulation results demonstrate that the proposed RIS partitioning method can effectively improve the utilization of RIS and thereby increase the total throughput of the system. Moreover, the sum achievable rate obtained by the proposed ML-based three-stage algorithm is higher than that of the conventional alternating optimization (AO) algorithm.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0090-6778 1558-0857
DOI:	10.1109/TCOMM.2025.3554673