论文标题
联合部署和多个访问设计,用于智能反射地面辅助网络
Joint Deployment and Multiple Access Design for Intelligent Reflecting Surface Assisted Networks
论文作者
论文摘要
研究了IRS辅助网络的基本智能反射表面(IRS)部署问题,在该网络中安排了一个IRS在特定区域中部署,以协助接入点(AP)和多个用户之间的通信。具体而言,考虑了三个多个访问方案,即非正交多访问(NOMA),频分多访问(FDMA)和时间划分多重访问(TDMA)。配制了用于部署位置的联合优化和IRS的反射系数以及AP处的功率分配的加权总和最大化问题。通过采用单调优化和半决赛松弛来找到性能上限,可以解决针对Noma和FDMA获得的非凸优化问题。通过利用IRS的时间选择性来最佳解决TDMA获得的问题。此外,对于所有三个多个访问方案,低复杂性次优算法都是通过利用交替优化和连续的凸近似技术来开发的,其中应用了局部区域优化方法以优化IRS部署位置。提供了数值结果,以表明:1)拟议的次优算法可以实现近乎最佳的性能; 2)对于Noma和FDMA/TDMA,优选的不对称和对称IRS部署策略; 3)通过优化部署位置,可以通过IRS实现的性能增长可显着提高。
The fundamental intelligent reflecting surface (IRS) deployment problem is investigated for IRS-assisted networks, where one IRS is arranged to be deployed in a specific region for assisting the communication between an access point (AP) and multiple users. Specifically, three multiple access schemes are considered, namely non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). The weighted sum rate maximization problem for joint optimization of the deployment location and the reflection coefficients of the IRS as well as the power allocation at the AP is formulated. The non-convex optimization problems obtained for NOMA and FDMA are solved by employing monotonic optimization and semidefinite relaxation to find a performance upper bound. The problem obtained for TDMA is optimally solved by leveraging the time-selective nature of the IRS. Furthermore, for all three multiple access schemes, low-complexity suboptimal algorithms are developed by exploiting alternating optimization and successive convex approximation techniques, where a local region optimization method is applied for optimizing the IRS deployment location. Numerical results are provided to show that: 1) near-optimal performance can be achieved by the proposed suboptimal algorithms; 2) asymmetric and symmetric IRS deployment strategies are preferable for NOMA and FDMA/TDMA, respectively; 3) the performance gain achieved with IRS can be significantly improved by optimizing the deployment location.