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September 28, 20250 citationsOpen Access

QoE based Joint Adaptive User Grouping and Power Allocation in Indoor VLC-NOMA Network

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HLHuanlin LiuWWWencai WangHCHaonan Chen

Key Points

  • Proposed algorithms significantly enhance user experience by maximizing sum mean opinion scores (MOS) across users in a VLC-NOMA network.
  • The power adjustment-based power allocation (PAPA) algorithm ensures quality of experience (QoE) while managing interference using successive interference cancellation (SIC).
  • Users are adaptively grouped into non-orthogonal multiple access (NOMA) groups utilizing a hierarchical agglomerative clustering (HAC) approach for efficient transmission modes.
  • The joint adaptive user grouping and power allocation (JAUGPA) algorithm improves fairness and data transmission rates, addressing indoor communication challenges.

Abstract

Abstract The growing demand for high data rate and the increasing scarcity of spectrum resources have attracted non-orthogonal multiple access (NOMA) into visible light communication (VLC) network. For improving user’s quality of experience (QoE) in VLC-NOMA network, a joint optimization problem of user grouping and power allocation is formulated to maximize the sum mean opinion scores (MOS) of users in the VLC-NOMA network. Then, we decompose it into an inner power allocation and an outer user grouping subproblems. For the inner power allocation subproblem, we propose a power adjustment-based power allocation (PAPA) algorithm to guaranteeing the QoE and successive interference cancellation (SIC). For the outer user grouping subproblem, a hierarchical agglomerative clustering-based (HAC) user grouping algorithm is proposed that users can adaptively select NOMA groups and transmission modes. Then, we propose a joint adaptive user grouping and power allocation (JAUGPA) algorithm to solve user grouping and power allocation iteratively. The simulation results show that the proposed algorithm can increase the sum MOS and guarantee the fairness of users in the VLC-NOMA network.

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Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68d913a34ddcf71ba560bac2https://doi.org/10.21203/rs.3.rs-7332921/v1
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