The next-generation wireless networks are envisioned to jointly support high-rate communications and ubiquitous sensing. Ultra-Massive Multiple-Input Multiple-Output (UM-MIMO) offers abundant spatial Degrees of Freedom (DoFs) for both functions, yet its large aperture shifts electromagnetic propagation into the near field, invalidating conventional far-field (plane-wave) assumptions. While near-field channel modeling has been studied, existing channel estimation methods are inadequate: on-grid designs suffer from non-orthogonal codebooks, and off-grid methods lack convergence guarantees, yielding unreliable estimates. Moreover, channel estimation and localization are typically designed in isolation, preventing the exchange of information that could otherwise enable mutual performance improvement. To address this difficulty, we propose a unified framework that exploits near-field characteristics to jointly design channel estimation and cooperative localization. Specifically, we develop a Variational Newtonized Near-field Channel Estimation (VNNCE) algorithm that extracts position-aware soft information from the channel, and a Gaussian Fusion Cooperative Localization (GFCL) method that leverages this information across multiple Base Stations (BSs) for enhanced accuracy.

翻译：下一代无线网络旨在同时支持高速率通信与泛在感知。超大规模多输入多输出（UM-MIMO）为这两项功能提供了丰富的空间自由度，但其大孔径特性使电磁传播进入近场区域，从而失效了传统的远场（平面波）假设。尽管近场信道建模已有研究，但现有信道估计方法存在不足：基于网格的设计受限于非正交码本，而无网格方法缺乏收敛性保证，导致估计结果不可靠。此外，信道估计与定位通常独立设计，阻碍了本可相互提升性能的信息交换。为解决这一难题，我们提出了一种利用近场特性的统一框架，以实现信道估计与协作定位的联合设计。具体而言，我们开发了一种变分牛顿化近场信道估计算法（VNNCE），用于从信道中提取位置感知软信息，并提出了一种高斯融合协作定位方法（GFCL），通过跨多个基站利用该信息来提升定位精度。