AI 中文总结
针对中高频段MIMO系统导频开销大问题，提出张量结构多域信道外推框架，利用有限散射特性恢复CSI。开发基于塔克的SFT域信号模型，揭示ADD域混叠问题，引入支持先验辅助去混叠机制，提出TANN，经训练得统一模型，数值结果验证其有效性和泛化能力。
AI 中文摘要
中高频段大规模多输入多输出（MIMO）为下一代无线系统提供了良好的容量 - 覆盖权衡，但大天线阵列、宽带宽和更快的时间变化大幅增加了准确获取信道状态信息（CSI）所需的导频开销。本文建立了一个张量结构的多域信道外推框架，利用实际传播环境的有限散射特性，从有限观测中恢复空间 - 频率 - 时间（SFT）域的完整CSI。具体而言，开发了基于塔克的SFT域信号模型来表示完整CSI，其中因子矩阵由角度 - 延迟 - 多普勒（ADD）域网格参数化。揭示了均匀导频模式和天线端口选择导致的有限SFT域观测会在ADD域引起混叠。引入了支持先验辅助的ADD域去混叠机制，因难以精确推导其闭式表征，提出了张量结构感知轴向注意力神经网络（TANN），通过张量结构建模和不同导频抽取因子的混合配置训练，TANN产生一个无需重新训练即可在不同导频配置上泛化的统一模型。数值结果证明了该框架在不同场景下相对于基准方法的有效性和强泛化能力。
英文摘要
Upper mid-band massive multiple-input multiple-output (MIMO) offers a favorable capacity-coverage trade-off for next-generation wireless systems, but its large antenna arrays, wide bandwidths, and faster temporal variation substantially increase the pilot overhead required for accurate channel state information (CSI) acquisition. To reduce this overhead, this paper establishes a tensor-structured multi-domain channel extrapolation framework that exploits the limited-scattering nature of practical propagation environments to recover complete CSI across the spatial-frequency-temporal (SFT) domains from limited observations. Specifically, we develop a Tucker-based SFT-domain signal model to represent the complete CSI, where the factor matrices are parameterized by angle-delay-Doppler (ADD)-domain grids. Thanks to this representation, we reveal that limited SFT-domain observations imposed by uniform pilot patterns and antenna-port selection inherently induce ADD-domain aliasing, so that multiple physically distinct ADD-domain components become indistinguishable within structured ADD aliasing groups. To tackle this issue, we introduce a support-prior-assisted ADD-domain de-aliasing mechanism that leverages coarse-grained support information. Since exact closed-form characterization of this mechanism is difficult to derive, we propose a tensor-structure-aware axial-attention neural network (TANN), which integrates axis-wise attention with a lightweight multi-scale CNN-based gating module to incorporate support priors for ADD-domain de-aliasing. With tensor-structure modeling and mixed-configuration training over different pilot decimation factors, TANN yields a unified model that generalizes across pilot configurations without retraining. Numerical results demonstrate the effectiveness and strong generalization of the proposed framework over benchmark methods under diverse scenarios.
CommentsThis work has been submitted to the IEEE for possible publication
