{"ID":2838874,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16000","arxiv_id":"2511.16000","title":"Joint Admission Control and Power Minimization in IRS-assisted Networks","abstract":"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.","short_abstract":"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 guar...","url_abs":"https://arxiv.org/abs/2511.16000","url_pdf":"https://arxiv.org/pdf/2511.16000v1","authors":"[\"Weijie Xiong\",\"Jingran Lin\",\"Zhiling Xiao\",\"Qiang Li\",\"Yuhan Zhang\"]","published":"2025-11-20T02:57:35Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}