标题： 面向MIMO集成感知与通信的高效射频链选择：一种贪心方法 显示英文标题

标题： Efficient RF Chain Selection for MIMO Integrated Sensing and Communications: A Greedy Approach

摘要： 在多输入多输出集成感知和通信（MIMO ISAC）系统中，射频链（即RF链）选择在降低硬件成本、功耗和计算复杂性方面起着至关重要的作用。 然而，由于通信和感知之间的性能指标差异——互信息（MI）与波束图均方误差（MSE）或克拉美-罗下界（CRLB）——设计有效的RF链选择策略具有挑战性。 为克服这一问题，我们提出了一种低复杂度的贪心RF链选择框架，该框架最大化一个适用于两种功能的统一MI-based性能指标。 通过将总MI分解为每个RF链的个体贡献，我们引入了两种方法：贪心特征值基于选择（GES）和贪心余子式基于选择（GCS），它们迭代地识别并移除贡献最低的RF链。 我们进一步将框架扩展到 beamspace MIMO ISAC 系统的波束选择，引入对角波束选择（DBS）作为简化解决方案。 仿真结果表明，我们提出的的方法在显著低于穷举搜索的复杂度下实现了接近最优的性能，证明了它们在MIMO ISAC系统中的实际有效性。 显示英文摘要

摘要： In multiple-input multiple-output integrated sensing and communication (MIMO ISAC) systems, radio frequency chain (i.e., RF chain) selection plays a vital role in reducing hardware cost, power consumption, and computational complexity. However, designing an effective RF chain selection strategy is challenging due to the disparity in performance metrics between communication and sensing-mutual information (MI) versus beam-pattern mean-squared error (MSE) or the Cram\'er-Rao lower bound (CRLB). To overcome this, we propose a low-complexity greedy RF chain selection framework maximizing a unified MI-based performance metric applicable to both functions. By decomposing the total MI into individual contributions of each RF chain, we introduce two approaches: greedy eigen-based selection (GES) and greedy cofactor-based selection (GCS), which iteratively identify and remove the RF chains with the lowest contribution. We further extend our framework to beam selection for beamspace MIMO ISAC systems, introducing diagonal beam selection (DBS) as a simplified solution. Simulation results show that our proposed methods achieve near-optimal performance with significantly lower complexity than exhaustive search, demonstrating their practical effectiveness for MIMO ISAC systems.