Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde

翻译：借助大规模空间自由度，集成感知与通信（ISAC）与海量多输入多输出（MIMO）技术的融合有望同时提升频谱效率、能量效率及感知能力。然而，海量MIMO-ISAC中部署的大量天线对同时获取精确信道状态信息与目标参数信息提出了严峻挑战。为通过统一框架应对这两项挑战，本文首先分析其基础系统模型，随后提出一种基于张量的创新方法，以同时解决信道估计与目标感知问题。具体而言，通过利用少量物理参数对高维通信信道进行参数化表征，我们将信道状态信息与目标在角度、时延及多普勒维度上的感知参数相关联。进而提出一种共享训练模式——采用相同的时间-频率资源——使得信道估计与目标参数估计可被表述为具有相似数学表达式的规范多元分解问题。在此基础上，我们首先利用特定范德蒙德结构探究张量分解的唯一性条件及可分辨目标的最大数量。