Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility, Doppler spread, and multipath dynamics rapidly alter channel statistics. Conventional pilot aided estimators incur substantial overhead, while deep learning approaches degrade under dynamic pilot patterns and time varying fading. This paper presents a pilot-aided Flash-Attention Transformer framework that unifies model-driven pilot acquisition with data driven CSI reconstruction through patch-wise self-attention and a physics aware composite loss function enforcing phase alignment, correlation consistency, and time frequency smoothness. Under a standardized 3GPP NR configuration, the proposed framework outperforms LMMSE and LSTM baselines by approximately 13 dB in phase invariant normalized mean-square error (NMSE) with markedly lower bit-error rate (BER), while reducing pilot overhead by 16 times. These results demonstrate that attention based architectures enable reliable CSI recovery and enhanced spectral efficiency without compromising link quality, addressing a fundamental bottleneck in adaptive, low-overhead channel estimation for non-stationary 5G and beyond-5G networks.

翻译：在用户移动性、多普勒扩展和多径动态快速改变信道统计特性的非平稳条件下，准确高效地估计信道状态信息（CSI）对于下一代无线系统至关重要。传统的导频辅助估计器会产生显著开销，而深度学习方法在动态导频模式和时变衰落条件下性能下降。本文提出了一种导频辅助的Flash-Attention Transformer框架，该框架通过分块自注意力机制和物理感知的复合损失函数（强制相位对齐、相关一致性和时频平滑性），将模型驱动的导频获取与数据驱动的CSI重建相统一。在标准化的3GPP NR配置下，所提框架在相位不变归一化均方误差（NMSE）上比LMMSE和LSTM基线方法提升约13 dB，误码率（BER）显著降低，同时将导频开销减少16倍。这些结果表明，基于注意力的架构能够在保证链路质量的前提下实现可靠的CSI恢复和更高的频谱效率，从而解决了非平稳5G及后5G网络中自适应、低开销信道估计的基本瓶颈。