论文标题
智能反映表面辅助的认知无线网络的资源分配
Resource Allocation for Intelligent Reflecting Surface-Assisted Cognitive Radio Networks
论文作者
论文摘要
在本文中，我们研究了用于智能反射表面（IRS）辅助的多源认知无线电（CR）系统的资源分配算法设计。特别是，部署了IRS来减轻二级网络对主要用户的干扰。共同优化了基站（BS）和IRS的相移矩阵的波束形成向量，以最大化二级系统的总和速率。考虑到主要用户的最大干扰耐受性，该算法设计被称为非凸优化问题。为了解决所得的非凸优化问题，我们提出了一种基于优化的次级优化算法，利用半明智的放松，惩罚方法和连续的凸近近似值。我们的仿真结果表明，与两个基线方案相比，我们所提出的方案可大大提高系统总和率。此外，我们的结果还说明了在CR网络中部署IRS的好处。
In this paper, we investigate resource allocation algorithm design for intelligent reflecting surface (IRS)-assisted multiuser cognitive radio (CR) systems. In particular, an IRS is deployed to mitigate the interference caused by the secondary network to the primary users. The beamforming vectors at the base station (BS) and the phase shift matrix at the IRS are jointly optimized for maximization of the sum rate of the secondary system. The algorithm design is formulated as a non-convex optimization problem taking into account the maximum interference tolerance of the primary users. To tackle the resulting non-convex optimization problem, we propose an alternating optimization-based suboptimal algorithm exploiting semidefinite relaxation, the penalty method, and successive convex approximation. Our simulation results show that the system sum rate is dramatically improved by our proposed scheme compared to two baseline schemes. Moreover, our results also illustrate the benefits of deploying IRSs in CR networks.
