论文标题
智能反映MMWave频道的表面辅助综合感应和通信
Intelligent Reflecting Surface Assisted Integrated Sensing and Communications for mmWave Channels
论文作者
论文摘要
本文提出了在毫米波(MMWave)频段运行的智能反射表面(IRS)辅助集成感应和通信(ISAC)系统。具体而言,ISAC系统结合了通信和雷达操作并执行,与多个目标和用户同时检测和交流。 IRS通过反映元素来动态控制无线电信号的幅度或相位,以重新配置无线电传播环境并提高ISAC系统的传输速率。通过共同设计雷达信号协方差(RSC)矩阵,通信系统的波束成形向量和IRS相移,可以提高ISAC系统的传输速率,同时匹配雷达所需的波形。由于多元耦合,问题是非凸,因此我们将其分解为两个单独的子问题。首先,RSC矩阵的封闭形式溶液是从所需的雷达波形得出的。接下来,将二次变换(QT)技术应用于子问题,然后采用交替优化(AO)来确定通信波束成形向量和IRS相移。为了计算IRS相移,我们同时采用了主要化最小化(MM)和歧管优化(MO)。同样,我们为法式问题得出了封闭形式的解决方案,从而有效地降低了计算复杂性。此外,引入了一个权衡因素,以平衡沟通和感应的性能。最后,模拟验证了算法的有效性,并证明IRS可以改善ISAC系统的性能。
This paper proposes an intelligent reflecting surface (IRS) assisted integrated sensing and communication (ISAC) system operating at the millimeter-wave (mmWave) band. Specifically, the ISAC system combines communication and radar operations and performs, detecting and communicating simultaneously with multiple targets and users. The IRS dynamically controls the amplitude or phase of the radio signal via reflecting elements to reconfigure the radio propagation environment and enhance the transmission rate of the ISAC system. By jointly designing the radar signal covariance (RSC) matrix, the beamforming vector of the communication system, and the IRS phase shift, the ISAC system transmission rate can be improved while matching the desired waveform for radar. The problem is non-convex due to multivariate coupling, and thus we decompose it into two separate subproblems. First, a closed-form solution of the RSC matrix is derived from the desired radar waveform. Next, the quadratic transformation (QT) technique is applied to the subproblem, and then alternating optimization (AO) is employed to determine the communication beamforming vector and the IRS phase shift. For computing the IRS phase shift, we adopt both the majorization minimization (MM) and the manifold optimization (MO). Also, we derive a closed-form solution for the formulated problem, effectively decreasing computational complexity. Furthermore, a trade-off factor is introduced to balance the performance of communication and sensing. Finally, the simulations verify the effectiveness of the algorithm and demonstrate that the IRS can improve the performance of the ISAC system.