AI 中文总结
本文提出结合深度学习与压缩感知优势的TAP框架，实现零样本泛化与无解码器设计，在五种3GPP环境中相比CsiNet大幅提升信道估计精度、缩小模型体积并加快推理速度。
AI 中文摘要
高效的信道状态信息（CSI）反馈对于频分双工（FDD）大规模多输入多输出（MIMO）系统不可或缺。现有压缩感知（CS）算法利用时延域稀疏性，但存在过高的迭代延迟和离散网格失配问题；相反，深度学习（DL）方法推理速度快，但缺乏空间可扩展性和领域适应性，无法推广到未见过的传播环境，且需要计算量大的编码器和解码器。本文提出TAP，即Tap-Assisted Parametric CSI Compression（抽头辅助参数化CSI压缩），它是一种一次性神经框架，结合了DL的速度与CS的数学可解释性。TAP用轻量级一维神经网络替代迭代追踪，通过可微亚网格插值算子从时域信道序列中提取主导连续传播时延，实现了真正的架构独立性，支持在不同阵列几何结构和未见过的传播环境中零样本泛化。此外，TAP产生完全无需解码器的有效载荷，使基站（BS）可通过简单的快速傅里叶逆变换（IFFT）重构信道。在五种3GPP环境中的大量评估表明，TAP相比CsiNet实现了3.13至12.22 dB的信道频率响应归一化均方误差（CFR-NMSE）提升，同时将模型体积缩小660倍至1 MB以下；TAP以亚毫秒级延迟运行，相比经典迭代OMP将推理速度提升2700倍，为下一代网络提供了可扩展且可部署的解决方案。
英文摘要
Efficient Channel State Information (CSI) feedback is indispensable for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Existing compressed sensing (CS) algorithms exploit delay-domain sparsity but suffer from prohibitive iterative latency and discrete grid mismatch. Conversely, deep learning (DL) approaches achieve rapid inference but lack spatial scalability and domain adaptability, failing to generalize to unseen propagation environments, and demand computationally heavy encoders and decoder. In this paper, we propose TAP, a Tap-Assisted Parametric CSI Compression. TAP is a one-shot neural framework that unifies the speed of DL with the mathematical interpretability of CS. TAP replaces iterative pursuit with a lightweight 1D neural network that extracts dominant continuous propagation delays from temporal channel sequences via a differentiable sub-grid interpolation operator. TAP achieves true architecture independence, enabling zero-shot generalization across diverse array geometries and unseen propagation environments. Furthermore, TAP yields a completely decoder-free payload, allowing the BS to reconstruct the channel via a simple inverse fast Fourier transform (IFFT). Extensive evaluations across five 3GPP environments demonstrate that TAP achieves a 3.13 to 12.22 dB channel frequency response normalized mean square error (CFR-NMSE) improvement over CsiNet while shrinking the model footprint by 660 times to under 1 MB. Operating with sub-millisecond latencies, TAP accelerates inference by 2700 times over classical iterative OMP, providing a scalable and deployment-ready solution for next-generation networks.
Comments38 pages, 13 figures
