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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Bibliographic Details
Published in:IEEE transactions on communications Vol. 73; no. 9; pp. 7617 - 7632
Main Authors: Wei, Tongyi, Chen, Weizhi, Tang, Kun, Zheng, Beixiong, Feng, Wenjie, Che, Wenquan, Xue, Quan
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2025.3554673