基于矩阵邻近公式的高效集成感知与通信（ISAC）波束成形设计
ISAC Beamforming Design Based on a Matrix Nearness Formulation With Improved Efficiency
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中文总结 AI 辅助
该研究针对ISAC场景，基于矩阵邻近公式提出高效波束成形方法，通过改进求解器减少特征值分解的迭代次数，降低大规模MIMO下的计算时间。
中文摘要 AI 辅助
我们提出了一种集成感知与通信（ISAC）波束成形方法，可同时执行多输入多输出（MIMO）雷达感知和多用户MIMO通信。该方法基于MIMO雷达问题的矩阵邻近公式，利用我们近期提出的高效求解器，其计算复杂度由每次迭代中的特征值分解（EVD）评估主导。我们将该公式扩展至ISAC场景，纳入最小信噪比约束作为通信设计准则，基站仅需统计信道状态信息。此外，我们提出一种方法，可在部分迭代中避免繁重的EVD评估，在大规模MIMO场景下将计算时间减少多达6倍。
英文摘要
We propose an integrated sensing and communication (ISAC) beamforming method that performs joint multiple-input multiple-output (MIMO) radar sensing and multi-user MIMO communication. Our approach builds on a matrix nearness formulation of the MIMO radar problem and utilizes our recently proposed efficient solver, where the computational complexity is dominated by an eigenvalue decomposition (EVD) evaluation at each iteration. We extend this formulation to an ISAC scenario by incorporating minimum signal-to-noise ratio constraints as communication design criteria, only requiring statistical channel state information knowledge at base station. Furthermore, we propose a method to avoid the burdensome EVD evaluations in certain iterations, reducing the computation time by up to six times in a massive MIMO setting.
