能够控制无线传播环境的智能反射面 (IRS) 最近已成为一种有前途的经济高效的技术，用于提高未来无线通信系统的频谱和能源效率。先前关于 IRS 的工作主要基于理想的相移模型，假设其每个元素都具有完整的信号反射，而与相移无关，然而，这实际上很难实现。相比之下，我们在本文中提出了一种实用的相移模型，该模型可以捕捉元素反射设计中与相位相关的幅度变化。基于所提出的模型并考虑 IRS 辅助多用户系统，其中部署了一个 IRS 以协助从多天线接入点 (AP) 到多个单天线用户的下行链路通信，我们制定了一个优化问题，通过联合设计 AP 发射波束成形和 IRS 反射波束成形，以最小化 AP 的总发射功率，受用户个人信干噪比 (SINR) 约束。提出了迭代算法，通过利用交替优化 (AO) 以及基于惩罚的优化技术，有效地找到该问题的次优解决方案。此外，为了得出重要的见解，我们分析了 IRS 辅助系统的渐进性能损失，该系统采用实际移相器，但假设波束成形优化的理想相移模型，因为 IRS 元素的数量达到无穷大。
Intelligent reflecting surface (IRS) that enables the control of wireless propagation environment has recently emerged as a promising cost-effective technology for boosting the spectral and energy efficiency of future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming full signal reflection by each of its elements regardless of the phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection design. Based on the proposed model and considering an IRS-aided multiuser system with one IRS deployed to assist in the downlink communications from a multi-antenna access point (AP) to multiple single-antenna users, we formulate an optimization problem to minimize the total transmit power at the AP by jointly designing the AP transmit beamforming and the IRS reflect beamforming, subject to the users’ individual signal-to-interference-plus-noise ratio (SINR) constraints. Iterative algorithms are proposed to find suboptimal solutions to this problem efficiently by utilizing the alternating optimization (AO) as well as penalty-based optimization techniques. Moreover, to draw essential insight, we analyze the asymptotic performance loss of the IRS-aided system that employs practical phase shifters but assumes the ideal phase shift model for beamforming optimization, as the number of IRS elements goes to infinity. Simulation results unveil substantial performance gains achieved by the proposed beamforming optimization based on the practical phase shift model as compared to the conventional ideal model.
