Intelligent reflecting surfaces (IRSs) are emerging as promising enablers for the next generation of wireless communication systems, because of their ability to customize favorable radio propagation environments. However, with the conventional passive architecture, IRSs can only adjust the phase of the incident signals limiting the achievable beamforming gain. To fully unleash the potential of IRSs, in this paper, we consider a more general IRS architecture, i.e., active IRSs, which can adapt the phase and amplify the magnitude of the reflected incident signal simultaneously with the support of an additional power source. To realize green communication in active IRS-assisted multiuser systems, we jointly optimize the reflection matrix at the IRS and the beamforming vector at the base station (BS) for the minimization of the BS transmit power. The resource allocation algorithm design is formulated as an optimization problem taking into account the maximum power budget of the active IRS and the quality-of-service (QoS) requirements of the users. To handle the non-convex design problem, we develop a novel and computationally efficient algorithm based on the bilinear transformation and inner approximation methods. The proposed algorithm is guaranteed to converge to a locally optimal solution of the considered problem. Simulation results illustrate the effectiveness of the proposed scheme compared to the two baseline schemes. Moreover, the results unveil that deploying active IRSs is a promising approach to enhance the system performance compared to conventional passive IRSs, especially when strong direct links exist.
翻译:智能反射表面(IRS)正在成为下一代无线通信系统(IRS)有希望的助推器,因为这些系统有能力定制有利的无线电传播环境。然而,在常规被动结构下,IRS只能调整事件信号的阶段,限制可实现的波束增益。为了充分释放IRS的潜力,本文件中,我们认为一个更普遍的IRS架构,即积极的IRS,可以调整阶段,扩大反射事件信号的广度,同时辅之以另一个动力源。为了在活跃的IRS辅助多用户系统中实现绿色通信,我们联合优化IRS的反射矩阵和基础站(BS)的波形变矢量,以尽量减少BS传输能力。资源配置算法设计是一个优化问题,考虑到活跃的IRS的最大功率预算以及用户对服务质量的要求。在处理非convex设计问题时,我们根据双线直接转换和多用户系统(IRS)辅助的绿色多用户系统中的绿色通信,我们共同优化了IRS的反射矩阵矩阵和基站(BA)的映射量矢量矢量矩阵矩阵,特别地展示了比平线式的常规部署结果。提议的算算算算法将保证了一种最优化的系统与最优化的系统与最优化的结果。拟议的最优化的系统与最接近性平。提议的系统与最接近性平压。提议的结果是,拟议的一种比较了一种最优化的系统。拟议最优化的算法是比较的系统。提议的系统与最接近性的结果。提议的系统与较。