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<p>In today’s fast-changing world of wireless communication, there’s a growing need for networks that are highly reliable, super-fast, and able to connect a massive number of devices. To meet these demands, combining Non-Orthogonal Multiple Access (NOMA) with Multiple Input Multiple Output (MIMO) technology provides a powerful approach to increase data capacity and utilise energy more efficiently. However, despite its potential, MIMO-NOMA faces significant challenges, particularly when the wireless environment changes rapidly or the system setup becomes overly complex. To address these issues, our research presents a novel deep learning-based framework, the Hybrid Attention-Assisted Residual Deep Neural Network (Hyb-AttRDNN). This intelligent system is designed to enhance both data speeds and energy efficiency by learning to allocate power more effectively across users. What makes Hyb-AttRDNN unique is its architecture—it combines different types of attention mechanisms (such as channel attention, cross-attention, and self-attention) with a technique called residual learning, enabling the model to focus on the most critical features and avoid common training issues. The model’s primary goal is to handle power allocation intelligently, and it utilises deep learning’s ability to recognise complex patterns to achieve this more effectively than traditional methods. Our simulations show that Hyb-AttRDNN delivers excellent&#xa0;results&#xa0;a sum data rate of around 106, a user rate of 34, and an energy efficiency score of 32. These outcomes suggest that Hyb-AttRDNN could be a significant step toward enhancing the efficiency, speed, and power-management capabilities of MIMO-NOMA systems, even under challenging wireless conditions.</p>

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Performance Analysis of MIMO-NOMA System Under Varied Channel Conditions Using Hybrid Attention-assisted Residual Deep Neural Network

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Shambhavi Tiwari,
  • Kanchan Sharma

摘要

In today’s fast-changing world of wireless communication, there’s a growing need for networks that are highly reliable, super-fast, and able to connect a massive number of devices. To meet these demands, combining Non-Orthogonal Multiple Access (NOMA) with Multiple Input Multiple Output (MIMO) technology provides a powerful approach to increase data capacity and utilise energy more efficiently. However, despite its potential, MIMO-NOMA faces significant challenges, particularly when the wireless environment changes rapidly or the system setup becomes overly complex. To address these issues, our research presents a novel deep learning-based framework, the Hybrid Attention-Assisted Residual Deep Neural Network (Hyb-AttRDNN). This intelligent system is designed to enhance both data speeds and energy efficiency by learning to allocate power more effectively across users. What makes Hyb-AttRDNN unique is its architecture—it combines different types of attention mechanisms (such as channel attention, cross-attention, and self-attention) with a technique called residual learning, enabling the model to focus on the most critical features and avoid common training issues. The model’s primary goal is to handle power allocation intelligently, and it utilises deep learning’s ability to recognise complex patterns to achieve this more effectively than traditional methods. Our simulations show that Hyb-AttRDNN delivers excellent results a sum data rate of around 106, a user rate of 34, and an energy efficiency score of 32. These outcomes suggest that Hyb-AttRDNN could be a significant step toward enhancing the efficiency, speed, and power-management capabilities of MIMO-NOMA systems, even under challenging wireless conditions.