  The next-generation (6G) wireless networks are expected to provide not only
seamless and high data-rate communications, but also ubiquitous sensing
services. By providing vast spatial degrees of freedom (DoFs), ultra-massive
multiple-input multiple-output (UM-MIMO) technology is a key enabler for both
sensing and communications in 6G. However, the adoption of UM-MIMO leads to a
shift from the far field to the near field in terms of the electromagnetic
propagation, which poses novel challenges in system design. Specifically,
near-field effects introduce highly non-linear spherical wave models that
render existing designs based on plane wave assumptions ineffective. In this
paper, we focus on two crucial tasks in sensing and communications,
respectively, i.e., localization and channel estimation, and investigate their
joint design by exploring the near-field propagation characteristics, achieving
mutual benefits between two tasks. In addition, multiple base stations (BSs)
are leveraged to collaboratively facilitate a cooperative localization
framework. To address the joint channel estimation and cooperative localization
problem for near-field UM-MIMO systems, we propose a variational Newtonized
near-field channel estimation (VNNCE) algorithm and a Gaussian fusion
cooperative localization (GFCL) algorithm. The VNNCE algorithm exploits the
spatial DoFs provided by the near-field channel to obtain position-related soft
information, while the GFCL algorithm fuses this soft information to achieve
more accurate localization. Additionally, we introduce a joint architecture
that seamlessly integrates channel estimation and cooperative localization.
