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<p>The continually increasing requirement for spectrum and energy enhancements in the fifth-generation (5G) wireless standards is considered a distinct obstacle; especially in complex multiple access schemes and interference management. In this paper, the author presents a new method that combines the Multiple Input Multiple Output (MIMO) technique with Multi-carrier code-division multiple access (MC-CDMA) and Successive Interference Cancellation (SIC). Using the proposed method, the spectrum can be managed effectively and the interference challenge in 5G will be tackled effortlessly. For energy and Spectrum efficiency, the model uses deep learning technologies like Convolutional Neural Network (CNN), Deep Reinforcement Learning (DRL), and Spectrum Resource Optimization by Crayfish bio-inspired optimization technique. This strategy includes the use of deep learning for dynamic resource allocation in MIMO MC-CDMA systems and enables real-time optimization of the models under highly dynamic conditions. The research findings reveal that the proposed model yields better quality of service, less latency, and data throughput than traditional procedures. This additionally not only improves the spectrum sharing effectivity but also reduces interference, thereby helping to create a more dependable and improved 5G communication system. Accordingly, this research, enhancing the overall performance of the network and resource management in the 5G system.</p>

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Enhanced Spectrum and Energy Efficiency in 5G Networks Using MIMO MC-CDMA with SIC and Deep Learning-Based Resource Optimization

  • 期刊论文

  • 发表日期：

    2025年7月14日

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  • A. Vijay

摘要

The continually increasing requirement for spectrum and energy enhancements in the fifth-generation (5G) wireless standards is considered a distinct obstacle; especially in complex multiple access schemes and interference management. In this paper, the author presents a new method that combines the Multiple Input Multiple Output (MIMO) technique with Multi-carrier code-division multiple access (MC-CDMA) and Successive Interference Cancellation (SIC). Using the proposed method, the spectrum can be managed effectively and the interference challenge in 5G will be tackled effortlessly. For energy and Spectrum efficiency, the model uses deep learning technologies like Convolutional Neural Network (CNN), Deep Reinforcement Learning (DRL), and Spectrum Resource Optimization by Crayfish bio-inspired optimization technique. This strategy includes the use of deep learning for dynamic resource allocation in MIMO MC-CDMA systems and enables real-time optimization of the models under highly dynamic conditions. The research findings reveal that the proposed model yields better quality of service, less latency, and data throughput than traditional procedures. This additionally not only improves the spectrum sharing effectivity but also reduces interference, thereby helping to create a more dependable and improved 5G communication system. Accordingly, this research, enhancing the overall performance of the network and resource management in the 5G system.