Joint Admission Control and Power Minimization in IRS-assisted Networks
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915628133122048 |
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| author | Xiong, Weijie Lin, Jingran Xiao, Zhiling Li, Qiang Zhang, Yuhan |
| author_facet | Xiong, Weijie Lin, Jingran Xiao, Zhiling Li, Qiang Zhang, Yuhan |
| contents | Joint admission control and power minimization are critical challenges in intelligent reflecting surface (IRS)-assisted networks. Traditional methods often rely on \( l_1 \)-norm approximations and alternating optimization (AO) techniques, which suffer from high computational complexity and lack robust convergence guarantees. To address these limitations, we propose a sigmoid-based approximation of the \( l_0 \)-norm AC indicator, enabling a more efficient and tractable reformulation of the problem. Additionally, we introduce a penalty dual decomposition (PDD) algorithm to jointly optimize beamforming and admission control, ensuring convergence to a stationary solution. This approach reduces computational complexity and supports distributed implementation. Moreover, it outperforms existing methods by achieving lower power consumption, accommodating more users, and reducing computational time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16000 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Admission Control and Power Minimization in IRS-assisted Networks Xiong, Weijie Lin, Jingran Xiao, Zhiling Li, Qiang Zhang, Yuhan Signal Processing Joint admission control and power minimization are critical challenges in intelligent reflecting surface (IRS)-assisted networks. Traditional methods often rely on \( l_1 \)-norm approximations and alternating optimization (AO) techniques, which suffer from high computational complexity and lack robust convergence guarantees. To address these limitations, we propose a sigmoid-based approximation of the \( l_0 \)-norm AC indicator, enabling a more efficient and tractable reformulation of the problem. Additionally, we introduce a penalty dual decomposition (PDD) algorithm to jointly optimize beamforming and admission control, ensuring convergence to a stationary solution. This approach reduces computational complexity and supports distributed implementation. Moreover, it outperforms existing methods by achieving lower power consumption, accommodating more users, and reducing computational time. |
| title | Joint Admission Control and Power Minimization in IRS-assisted Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.16000 |