双智能反射面辅助的感知通信一体化波束赋形与相移优化

部署智能反射面可有效解决基站与部分通信用户或潜在目标之间直接链路很弱的问题，但由于单个智能反射面的覆盖范围有限，因此研究了双智能反射面辅助的感知通信一体化系统。为了减小信道训练开销，使用了统计信道信息。建立了通过联合优化基站的发射波束赋形与智能反射面的相移，在保证通信用户遍历容量的最低要求下最大化波束图增益的问题。将该问题转化为3个子问题来解耦发射波束赋形与两个智能反射面的相移，进而提出用基于半定松弛的交替优化算法来求解问题。相较于单智能反射面场景，部署双智能反射面得到了超过两倍的波束图增益。

Beamforming and Phase Shifts Optimization for a Double IRS-assisted Integrated Sensing and Communication

The problem that the direct links between the base station and some communication users or potential targets are very weak can be solved efficiently by deploying the intelligent reflecting surface(IRS).However,the coverage of single IRS is limited,and thus a double IRS-assisted integrated sensing and communication(ISAC) system is investigated.In order to reduce the channel training overhead,statistical channel state information is used.The problem of maximizing the beampattern gain by jointly optimizing the beamforming at the base station and the phase shift at the IRSs subject to the minimum ergodic capacity requirements of communication users is established.The problem is transformed into three subproblems to decouple beamforming and phase shifts of double IRSs,and then the semi-definite relaxation based alternating optimization algorithm is proposed to solve the problem.Over twice the beampattern gain is obtained by deploying double IRSs compared with single IRS scenario.