实用OFDM-ISAC中的时延-多普勒模糊函数整形

中文摘要 AI 辅助

正交频分复用（OFDM）是集成感知与通信（ISAC）的关键波形。然而，现有的OFDM模糊函数分析通常假设完全占用的纯数据波形，而实际帧包含直流和边缘保护空子载波、固定导频以及随机有效载荷符号。这种混合资源结构重塑了自模糊函数，并在感知感兴趣区域（ROI）中引起显著旁瓣。因此，我们提出面向ROI的深度块酉预编码OFDM（DBU-OFDM），该方法将资源特定的可训练酉变换与专用感知子载波相结合，以抑制ROI旁瓣，同时保持规定的资源支撑。我们开发了一种保持约束的参数化方法，能够表示任意酉矩阵，以及一种ROI感知的感知支撑初始化。我们证明了在仅相位优化下周期自相关函数（P-ACF）的不变性，以及在任意酉变换下零时延多普勒切面的不变性。数值结果证实了循环前缀OFDM（CP-OFDM）的P-ACF最优性，并为传统OFDM在所考虑的非周期自相关函数（A-ACF）设置中全局最优的猜想提供了数值支持。酉导频优化将ROI峰值旁瓣比提高了超过2 dB，而将其与专用感知子载波相结合可获得从几dB到几十dB的增益。在所考虑的配置下，A-ACF模式还提供了比P-ACF模式显著更大的ROI模糊整形能力。

英文摘要

Orthogonal frequency-division multiplexing (OFDM) is a key waveform for integrated sensing and communication (ISAC). Existing OFDM ambiguity analyses, however, typically assume fully occupied data-only waveforms, whereas practical frames contain direct-current and edge-guard nulls, fixed pilots, and random payload symbols. This mixed resource structure reshapes the self-ambiguity function and induces prominent sidelobes in the sensing region of interest (ROI). We therefore propose ROI-oriented deep block-unitary precoded OFDM (DBU-OFDM), which combines resource-specific trainable unitary transformations with dedicated sensing subcarriers to suppress ROI sidelobes while preserving the prescribed resource support. We develop a constraint-preserving parameterization capable of representing arbitrary unitary matrices and an ROI-aware sensing-support initialization. We prove periodic autocorrelation function (P-ACF) invariance under phase-only optimization and zero-delay Doppler-cut invariance under arbitrary unitary transformations. Numerical results corroborate the P-ACF optimality of cyclic-prefix OFDM (CP-OFDM) and provide numerical support for the conjecture that conventional OFDM is globally optimal in the considered aperiodic autocorrelation function (A-ACF) setting. Unitary pilot optimization improves the ROI peak-to-sidelobe ratio by over 2 dB, while combining it with dedicated sensing subcarriers yields gains ranging from several to tens of dB. Under the considered configuration, the A-ACF mode also provides substantially greater ROI ambiguity-shaping capability than the P-ACF mode.

发表机构

• State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术重点实验室）

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