学习对匹配滤波器进行变形
Learning to deform the matched filter
AI总结：
本文提出可变形匹配滤波平台，引导对显式匹配滤波器的有界变形，其接收端变形泛化能力优于传统均衡器，发射端变形可降低误差矢量幅度，是固定理论与端到端学习信号处理的中间方案。
AI中文摘要：
解析信号处理模块具备可解释性与可靠性，但其最优性依赖于实际硬件与信道并不满足的假设条件。基于学习的替代方案虽具备自适应能力，却往往会舍弃使原始方案具备可理解性的结构。本文提出可变形匹配滤波（deformable matched filtering）平台，该平台中学习过程用于引导对显式匹配滤波器的有界变形，而非替代波形处理路径。在硬件在环（hardware-in-the-loop）光无线链路中，盲状态描述符驱动因果、无需导频的更新，而有效载荷样本、传输参考信号与条件标签均处于控制器之外。接收端变形在未见过的信号与信道条件下具备泛化能力，且在多数测试场景中，经平稳收敛后其性能优于跨度匹配的分数间隔均衡器。可迁移的发射端变形在全部144项未见过的评估中均降低了误差矢量幅度（error-vector magnitude），而其在接收端自适应后的额外价值主要在严重的复合失真下显现。KAN、MLP与线性控制器在同一滤波器结构内提供了不同的条件依赖优势。在24小时不间断的变工况运行中，有界可变形接收端在经历严重的中间失真后可恢复至原始工作状态，而持续运行的传统均衡器则会累积破坏性状态。这些结果表明，可变形解析滤波器是固定理论与端到端学习信号处理之间可复用的中间方案。
英文摘要：
Analytical signal-processing blocks are interpretable and reliable, but their optimality depends on assumptions that practical hardware and channels violate. Learned replacements can adapt, but often discard the structure that makes the original solution understandable. Here we introduce deformable matched filtering, a platform in which learning steers a bounded deformation of an explicit matched filter instead of replacing the waveform-processing path. In a hardware-in-the-loop optical wireless link, blind state descriptors drive causal, pilotless updates while payload samples, transmitted references, and condition labels remain outside the controller. Receiver deformation generalises across held-out signalling and channel conditions and remains ahead of a span-matched fractionally spaced equaliser after stationary convergence in most tested regimes. A transferable transmitter deformation reduces error-vector magnitude in all 144 held-out evaluations, whereas its additional value after receiver adaptation emerges principally under severe combined distortion. KAN, MLP, and linear controllers provide different condition-dependent advantages within the same filter structure. During 24 hours of uninterrupted changing-condition operation, bounded deformable receivers recover their original operating regime after severe intervening distortion while the persistent conventional equaliser accumulates destructive state. These results establish deformable analytical filters as a reusable middle ground between fixed theory and end-to-end learned signal processing.
