Koopman预测系数跟踪用于车载单站ISAC

中文摘要 AI 辅助

本文研究了车载单站集成感知与通信（ISAC）系统中数字生成的自干扰消除（SIC）的预测系数跟踪问题。在此类系统中，消除器必须抑制强发射机泄漏和附近非预期反射，同时保留延迟的感知回波。在移动平台上，发动机、旋翼或附近结构产生的机械振动可引起自干扰（SI）信道的准周期变化。最优消除系数可能在一个自适应周期内变化，导致传统自适应滤波器应用的系数滞后于移动的SI信道，从而增加残余SI。为解决此问题，我们提出了一种Koopman预测SIC（KP-SIC）模块，该模块通过预测系数轨迹增强传统自适应滤波器。KP-SIC利用最近的有效SI信道系数快照来识别结构化振动引起的动态，并预测下一自适应周期的样本变化系数序列。预测系数使消除器更接近预期的时变最优值，而自适应滤波器则校正残余预测误差和非结构化变化。为进行评估，该模块在灵活模拟SIC（FASIC）框架下与广泛线性归一化最小均方（WL-NLMS）滤波器集成。仿真表明，一旦观测窗口至少覆盖约1.5个振动周期，KP-SIC相比传统FASIC-WL-NLMS可将SI抑制提高6.3–10.2 dB。与非因果性能界相比，KP-SIC实现了可用预测增益的60%–74%。

英文摘要

This paper studies predictive coefficient tracking for digitally generated self-interference cancellation (SIC) in vehicle-mounted monostatic integrated sensing and communication (ISAC) systems. In such systems, the canceller must suppress strong transmitter leakage and nearby unintended reflections while preserving delayed sensing echoes. On mobile platforms, mechanical vibration from engines, rotors, or nearby structures can induce quasi-periodic variation in the self-interference (SI) channel. The optimal cancellation coefficients may then vary within one adaptation period, causing the applied coefficients of a conventional adaptive filter to lag behind the moving SI channel and increasing the residual SI. To address this problem, we propose a Koopman-predictive SIC (KP-SIC) module that augments a conventional adaptive filter with a predicted coefficient trajectory. KP-SIC uses recent effective SI-channel coefficient snapshots to identify structured vibration-induced dynamics and predict a sample-varying coefficient sequence for the next adaptation period. The predicted coefficients place the canceller closer to the expected time-varying optimum, while the adaptive filter corrects residual prediction errors and unstructured variation. For evaluation, the module is integrated with a widely linear normalized least mean-square (WL-NLMS) filter under a flexible analog SIC (FASIC) framework. Simulations show that KP-SIC improves SI suppression by $6.3$--$10.2$~dB over conventional FASIC-WL-NLMS once the observation window spans at least about $1.5$ vibration cycles. Compared with a non-causal performance bound, KP-SIC achieves $60$--$74\%$ of the available prediction gain.

发表机构

• Global Big Data Technologies Centre, University of Technology Sydney（全球大数据技术中心，悉尼科技大学）

机构由 AI 辅助整理，请以论文原文为准。