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<p>The rapid development of 5G/6G mobile communication and the proliferation of smart mobile devices have led to a surge in the number of Mobile Communication Network (MCN) devices and communication demands, creating a shortage of spectrum resources. This paper integrates cognitive radio technology with MCN to address the challenge of effective resource allocation. A joint spectrum and power allocation algorithm under imperfect spectrum sensing is proposed to minimize cross-layer interference between primary and secondary users. A multi-objective, multi-constraint optimization model is developed for channel selection and power control in secondary user networks. Additionally, a multi-objective vulture optimization algorithm with a multi-leader mechanism is introduced. Experimental results demonstrate that the proposed algorithm achieves up to 22.95% reduction in interference compared to MODA algorithm and 13.87% compared to MOPSO algorithm, while increasing network capacity by 4.97% compared to MOGWO algorithm, outperforming existing algorithms in terms of Pareto frontier and HV index.</p>

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Resource Allocation Based on Imperfect Spectrum Sensing in Mobile Communication Environment

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Yanan Liu,
  • Zheng Dou,
  • Kuixian Li,
  • Xingdong Huo,
  • Yuanzhi He

摘要

The rapid development of 5G/6G mobile communication and the proliferation of smart mobile devices have led to a surge in the number of Mobile Communication Network (MCN) devices and communication demands, creating a shortage of spectrum resources. This paper integrates cognitive radio technology with MCN to address the challenge of effective resource allocation. A joint spectrum and power allocation algorithm under imperfect spectrum sensing is proposed to minimize cross-layer interference between primary and secondary users. A multi-objective, multi-constraint optimization model is developed for channel selection and power control in secondary user networks. Additionally, a multi-objective vulture optimization algorithm with a multi-leader mechanism is introduced. Experimental results demonstrate that the proposed algorithm achieves up to 22.95% reduction in interference compared to MODA algorithm and 13.87% compared to MOPSO algorithm, while increasing network capacity by 4.97% compared to MOGWO algorithm, outperforming existing algorithms in terms of Pareto frontier and HV index.