arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.00552eess.SPcs.ITmath.IT

用于毫米波信道的智能反射面辅助通感一体化

Intelligent Reflecting Surface Assisted Integrated Sensing and Communications for mmWave Channels

Zhengyu Zhu, Zheng Li, Zheng Chu, Gangcan Sun, Wanming Hao, Pei Xiao, Inkyu Lee

AI总结:

针对毫米波ISAC系统,本文联合优化雷达协方差矩阵、通信波束成形与IRS相移,提出基于QT、AO、MM和流形优化的低复杂度算法,提升通信速率并兼顾雷达波形匹配。

摘要:

本文提出了一种工作在毫米波(mmWave)频段的智能反射面(IRS)辅助通感一体化(ISAC)系统。具体而言,该ISAC系统结合通信与雷达操作,同时对多个目标和用户执行检测与通信。IRS通过反射单元动态控制无线电信号的幅度或相位,以重构无线电传播环境并提升ISAC系统的传输速率。通过联合设计雷达信号协方差(RSC)矩阵、通信系统的波束成形向量以及IRS相移,可以在匹配雷达所需波形的同时提高ISAC系统的传输速率。由于多变量耦合,该问题是非凸的,因此我们将其分解为两个独立的子问题。首先,根据期望的雷达波形推导RSC矩阵的闭式解。接着,将二次变换(QT)技术应用于该子问题,然后采用交替优化(AO)来确定通信波束成形向量和IRS相移。为计算IRS相移,我们同时采用了最大化最小化(MM)和流形优化(MO)。此外,我们还推导了所构造问题的闭式解,有效降低了计算复杂度。进一步地,引入一个折衷因子以平衡通信与感知性能。最后,仿真验证了该算法的有效性,并证明IRS能够提升ISAC系统的性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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