论文标题
大规模IRS辅助无线系统的低复杂算法框架
A Low-Complexity Algorithmic Framework for Large-Scale IRS-Assisted Wireless Systems
论文作者
论文摘要
智能反射表面（IRS）是下一代无线通信网络的革命性推动者，具有自定义无线电传播环境的能力。为了充分利用IRS辅助无线系统的潜力，必须通过传统的通信技术共同优化反光元素。但是，由此产生的优化问题构成了重大的算法挑战，这主要是由于被动硬件实现引起的大规模非凸限制。在本文中，我们提出了一个低复杂性算法框架，该框架结合了大规模IRS辅助无线系统的交替优化和基于梯度的方法。所提出的算法可证明将优化问题的固定点收敛。广泛的仿真结果表明，与现有算法相比，所提出的框架提供了显着的加速，同时实现了可比或更好的性能。
Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environment. To fully exploit the potential of IRS-assisted wireless systems, reflective elements have to be jointly optimized with conventional communication techniques. However, the resulting optimization problems pose significant algorithmic challenges, mainly due to the large-scale non-convex constraints induced by the passive hardware implementations. In this paper, we propose a low-complexity algorithmic framework incorporating alternating optimization and gradient-based methods for large-scale IRS-assisted wireless systems. The proposed algorithm provably converges to a stationary point of the optimization problem. Extensive simulation results demonstrate that the proposed framework provides significant speedups compared with existing algorithms, while achieving a comparable or better performance.
