Flexible-antenna systems, which use a small number of radio frequency (RF) chains to dynamically access a large set of candidate antenna locations, have emerged as a hardware-efficient architecture for 6G networks. Acquiring accurate channel state information (CSI) is critical for these systems, but it typically incurs a prohibitive pilot overhead that scales with the massive number of candidate locations. To address this bottleneck, we propose a unified sensing-assisted channel estimation framework tailored for flexible-antenna systems. It reduces the full CSI reconstruction problem to a consistent two-stage process: it first resolves the dominant DOAs from the uplink data symbols by exploiting the spatial geometry, requiring no dedicated sensing pilot, and then calibrates the associated path gains using a minimal number of calibration pilots. Building on this pipeline, we develop two Newton-MUSIC algorithms tailored to different propagation environments. For line-of-sight (LOS)-dominant environments with uncorrelated sources, we propose SOC-Newton-MUSIC, which leverages second-order covariance (SOC) for low-complexity DOA sensing. For non-line-of-sight (NLOS) environments with coherent multipath, where the number of sources may exceed the number of activated RF chains, we propose FOC-Newton-MUSIC, which exploits fourth-order cumulants (FOC) to restore source identifiability and structurally expand the available spatial degrees of freedom (DOFs) through a continuous difference co-array. In both cases, by reformulating the spatial spectrum search as a continuous optimization problem, we replace exhaustive dense grid searches with parallelized Newton refinements.

翻译：灵活天线系统通过少量射频链路动态访问大量候选天线位置，已成为面向6G网络的一种硬件高效架构。获取精确的信道状态信息对此类系统至关重要，但通常需承担与海量候选位置规模成正比的 prohibitive 导频开销。为解决这一瓶颈，我们提出了一种专为灵活天线系统设计的统一感知辅助信道估计框架。该框架将完整信道状态信息重建问题简化为一致的两阶段过程：首先利用空间几何结构从上行数据符号中解析主导到达角（DOA），无需专用感知导频；随后通过最少校准导频对相应路径增益进行标定。基于该流程，我们针对不同传播环境开发了两种牛顿-多重信号分类（Newton-MUSIC）算法。针对非相关源主导的视距环境，提出SOC-Newton-MUSIC算法，利用二阶协方差实现低复杂度DOA感知。针对存在相干多径的非视距环境（信源数可能超过激活射频链数），提出FOC-Newton-MUSIC算法，利用四阶累积量恢复源可辨识性，并通过连续差分共阵列在结构上扩展可用空间自由度。两种情况下，通过将空间谱搜索重构为连续优化问题，我们以并行化牛顿细化替代了穷举式密集网格搜索。