  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
