智能超表面辅助的通感一体化性能优化

Reconfigurable intelligent surface-aided ISAC performance optimization

为了解决因障碍物等遮挡导致的覆盖空洞和探测精度下降问题,提出了一种基于栅格化的智能超表面辅助的通感一体化优化方法。首先,建模了最小化均方位置误差问题,将弱通感区域和RIS可能部署区域栅格化以降低计算难度,并采用粒子群优化算法和交替优化算法联合优化智能超表面期望反射角角度和部署位置。最后,引入探测需求因子,建模通感性能联合优化问题,并分析了探测需求因子对系统通感性能的影响。仿真结果显示:(1)相较于仅优化期望反射角,联合优化智能超表面的期望反射角和部署位置的方案,在平均信噪比仅有略微下降的情况下,可显著降低平均均方位置误差,平均均方位置误差降低10倍左右;(2)不同尺寸大小的智能超表面会对其最佳部署位置有影响;(3)联合优化智能超表面的期望反射角和部署位置提高了探测精度,最大化了通感整体性能。

In order to solve the problem of coverage hole and low detection accuracy caused by obstacles,a gridding based reconfigurable intelligent surface(RIS)assisted ISAC(integrated sensing and communication)optimization approach is proposed.Firstly,a minimizing square position error bound(SPEB)problem is modeled.The weak sensing&communication area and the potential RIS deployment area is divided into several grids to reduce the computational complexity,and particle swarm optimization algorithm and alternating optimization algorithm is used to jointly optimizing the desired angle of reflection and deployment location of RIS.In addition,a detection demand factor is introduced to model the problem of jointly maximizing sensing and communication performance,and analyze the effect of the factor on performance.The simulation results show that:(1)The joint optimization of the desired angle of reflection and the deployment location of the RIS significantly reduces the average SPEB with only a small decrease in the average SNR,the joint optimization scheme can reduce the average SPEB by about 10 times compared to that only optimizing the desired angle of reflection;(2)It can be seen that different sizes of RIS have an influence on its optimal deployment locations.(3)The joint optimization of the desired angle of reflection and the deployment location of the RIS further improves the detection accuracy,and maximizes the ISAC overall performance.