Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems.

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Minghao Wang, Xin Liu, Fang Wang, Yang Liu, Tianshuang Qiu, Minglu Jin

Author Information

Minghao Wang: College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021, China.
Xin Liu: College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021, China.
Fang Wang: College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021, China.
Yang Liu: College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021, China. yangliu@imu.edu.cn.
Tianshuang Qiu: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.
Minglu Jin: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.

PMID: 38632323 DOI: 10.1038/s41598-024-59241-x

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system's achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Nature. 2015 Feb 26;518(7540):529-33 [PMID: 25719670]
IEEE Internet Things J. 2022 Jun 15;9(12): [PMID: 38486943]

62161037/National Natural Science Foundation of China
62071257/National Natural Science Foundation of China
2023JQ17/Natural Science Foundation of Inner Mongolia Autonomous Region

Journal Article

Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems.Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks.Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture.Deep Learning-Based Joint CSI Feedback and Hybrid Precoding in FDD mmWave Massive MIMO Systems.Spectrum Allocation and User Scheduling Based on Combinatorial Multi-Armed Bandit for 5G Massive MIMO.User-Driven Relay Beamforming for mmWave Massive Analog-Relay MIMO.Implementation of the deep learning method for signal detection in massive-MIMO-NOMA systems.Joint Beam-Forming, User Clustering and Power Allocation for MIMO-NOMA Systems.Threshold-Based User-Assisted Cooperative Relaying in Beamspace Massive MIMO NOMA Systems.Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems.

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