Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach
Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach
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DOI：
10.1109/jstsp.2021.3111438
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发表时间：
2021-08
影响因子：
7.5
通讯作者：
Rang Liu;Ming Li;Qian Liu;A. L. Swindlehurst
中科院分区：
文献类型：
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作者：
Rang Liu;Ming Li;Qian Liu;A. L. Swindlehurst
双功能雷达通信（DFRC）系统可以使用相同的硬件平台和频谱资源同时执行雷达和通信功能。在本文中，我们考虑多输入多输出（MIMO）DFRC系统，并专注于发射波束成形设计，提供雷达传感和多用户通信。与传统的块级预编码技术不同，我们建议在DFRC系统中使用最近出现的符号级预编码方法，该方法提供了额外的自由度（DoF），可以保证更好的瞬时发射波束图用于雷达感测，并实现更好的通信性能。特别是，设计和期望的波束图案之间的平方误差最小化的通信用户的服务质量（QoS）的要求和恒定模功率约束。两个有效的算法来解决这个非凸问题的欧氏空间和黎曼空间。第一种算法采用罚对偶分解（PDD），优化最小化（MM），和块坐标下降（BCD）的方法，将原来的优化问题转化为两个可解的子问题，并迭代求解它们使用有效的算法。第二个算法提供了一个更快的解决方案，在轻微的性能损失的代价，首先将原来的问题到黎曼空间，然后利用增广拉格朗日方法（ALM），以获得一个无约束的问题，随后通过黎曼Broyden-Fletcher-Goldfarb-Shanno（RBFGS）算法解决。大量的仿真验证了所提出的符号级预编码设计在雷达传感和多用户通信中的明显优势。
Dual-functional radar-communication (DFRC) systems can simultaneously perform both radar and communication functionalities using the same hardware platform and spectrum resource. In this paper, we consider multi-input multi-output (MIMO) DFRC systems and focus on transmit beamforming designs to provide both radar sensing and multi-user communications. Unlike conventional block-level precoding techniques, we propose to use the recently emerged symbol-level precoding approach in DFRC systems, which provides additional degrees of freedom (DoFs) that guarantee preferable instantaneous transmit beampatterns for radar sensing and achieve better communication performance. In particular, the squared error between the designed and desired beampatterns is minimized subject to the quality-of-service (QoS) requirements of the communication users and the constant-modulus power constraint. Two efficient algorithms are developed to solve this non-convex problem on both the Euclidean and Riemannian spaces. The first algorithm employs penalty dual decomposition (PDD), majorization-minimization (MM), and block coordinate descent (BCD) methods to convert the original optimization problem into two solvable sub-problems, and iteratively solves them using efficient algorithms. The second algorithm provides a much faster solution at the price of a slight performance loss, first transforming the original problem into Riemannian space, and then utilizing the augmented Lagrangian method (ALM) to obtain an unconstrained problem that is subsequently solved via a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm. Extensive simulations verify the distinct advantages of the proposed symbol-level precoding designs in both radar sensing and multi-user communications.