AI 中文总结
针对现有MIMO-OFDM ISAC系统空-时-频自由度受限的问题，本文提出基于张量分解的灵活空-时-频优化框架，推导CRB并优化天线、子载波与符号分配以提升目标参数估计精度。
AI 中文摘要
集成感知与通信（ISAC）被视为未来第六代（6G）移动通信系统的关键使能技术。然而，现有的多输入多输出（MIMO）正交频分复用（OFDM）ISAC设计通常依赖固定位置天线与时频资源的固定分配，从而限制了无线感知在空-时-频维度的自由度。本文针对MIMO-OFDM ISAC系统提出一种具备灵活空-时-频优化的新型无线感知框架，并提出一种基于张量分解的方法来估计目标参数，包括方位角/仰角、距离和速度。具体而言，我们首先建立MIMO-OFDM ISAC系统的单静态无线感知模型，其中天线单元位置、OFDM符号与子载波的分配均可灵活配置。随后，我们将目标参数估计问题建模为可采用规范多面体（canonical polyadic）格式的张量分解问题，该格式支持分别沿空间、时间和频谱维度从对应的因子矩阵并行估计目标参数。基于分解得到的因子矩阵，我们推导了未知目标参数的克拉美-罗下界（CRB），并揭示方位角/仰角、速度和距离的估计精度从根本上由阵列几何结构、OFDM符号与子载波的分布决定。基于该洞察，我们得到天线单元位置的优化解，以及子载波分配和OFDM符号分配的最优解，以最小化CRB和目标参数估计的均方误差。
英文摘要
Integrated sensing and communication (ISAC) is regarded as a key enabling technique in future 6th-generation (6G) mobile communication systems. However, existing multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) ISAC designs generally rely on the fixed-position antennas and fixed allocation of time-frequency resources, thereby limiting the degrees of freedom of wireless sensing along the spatial-temporal-spectral dimensions. In this paper, we propose a novel wireless sensing framework for MIMO-OFDM ISAC systems with flexible spatial-temporal-spectral optimization and propose a tensor decomposition-based approach to estimate target parameters, including azimuth/elevation angles, ranges, and velocities. Specifically, we first establish a monostatic wireless sensing model for MIMO-OFDM ISAC systems, where the positions of antenna elements, the allocation of OFDM symbols and subcarriers can be flexibly configured. Then, we formulate the problem of estimating target parameters as a tensor decomposition problem admitting to the canonical polyadic format, which enables the parallel target parameters estimation process from corresponding factor matrices along the spatial, temporal, and spectral dimensions, respectively. Based on the decomposed factor matrices, we derive the Cramer-Rao Bound (CRB) for the unknown target parameters and reveal that the estimation accuracy of azimuth/elevation angles, velocities and ranges is fundamentally determined by the array geometry, the distribution of OFDM symbols and subcarriers. Building on this insight, we obtain an optimized solution for the positions of antenna elements, and optimal solutions for the subcarrier allocation and OFDM symbol allocation to minimize the CRB, as well as the mean square error of target parameters estimation.
