Joint Admission Control and Power Minimization in IRS-assisted Networks

TL;DR

This paper introduces a novel sigmoid-based approximation and a penalty dual decomposition algorithm for joint admission control and power minimization in IRS-assisted networks, improving efficiency, convergence, and performance over traditional methods.

Contribution

It proposes a new sigmoid-based approximation of the l0-norm and a PDD algorithm for joint optimization, addressing computational complexity and convergence issues in IRS networks.

Findings

Reduces computational complexity compared to traditional methods

Achieves lower power consumption in IRS-assisted networks

Supports more users and faster computation

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.