5G NR 集成感知与通信（ISAC）中多相码 artifacts 的表征与抑制
Characterization and Mitigation of Polyphase-Code Artifacts in 5G NR ISAC
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中文总结 AI 辅助
针对 5G NR ISAC 中多相码引发的 RV 谱 artifacts 问题，本文提出数据-物理驱动的 PIAENet 网络，可有效抑制 artifacts，将目标检测概率从 79.58% 提升至 98.88%。
中文摘要 AI 辅助
利用 5G NR 参考信号进行目标感知已成为学术界和工业界的重要研究方向，但实际部署中的非理想因素会对目标感知性能产生显著不利影响，表现为 RV（距离-速度）谱中的 artifacts，这些 artifacts 会掩盖弱目标并引发严重虚警。为应对这些挑战，本文建立了 artifacts 的理论模型，并构建了数据-物理驱动的深度学习范式用于 artifacts 抑制。首先从理论上推导了 artifacts 的起源及其特性，分析表明该类 artifacts 与多相码（如 Zadoff-Chu 序列）相关，且具有距离域的周期性扩展、速度域的频谱扩展等特性。随后将 artifacts 的物理先验形式化为时间连续性和空间一致性，以此指导所提网络的训练机制设计。基于上述见解，本文提出了 PIAENet，其核心是多帧选择性掩码编解码模块，专门用于融入上述先验：时间连续性通过多帧机制实现，以捕捉连续 RV 谱的特征；空间一致性则通过选择性掩码机制实现，以增强受 artifacts 影响区域的重建。本文采用商用毫米波设备采集的实测数据开展了广泛验证，实验不仅验证了 artifacts 的多相码相关特性，还表明所提 PIAENet 可有效减少虚假目标数量，并将检测概率从 79.58% 提升至 98.88%。
英文摘要
Target sensing utilizing 5G NR reference signals has emerged as a prominent research direction in both academia and industry. However, non-ideal factors in practical deployments exert a significant detrimental impact on target sensing performance, manifesting as artifacts in the RV spectrum. These artifacts mask weak targets and cause severe false alarms. To address these challenges, this paper establishes a theoretical model of artifacts and constructs a data-physics-driven deep learning paradigm for artifact mitigation. First, the origin of artifacts and their characteristics are theoretically derived. These analyses demonstrate that the artifacts are associated with polyphase codes, e.g., Zadoff-Chu sequences, and reveal their characteristics, including periodic extensions in the range domain and spectral spreading in the velocity domain. Then, the physical priors of artifacts are formalized as temporal continuity and spatial consistency, informing the design of the training mechanism for the proposed network. Guided by these insights, we propose a PIAENet. At its core is a multi-frame selective-masked encoder-decoder module, explicitly designed to incorporate the above priors. Specifically, temporal continuity is implemented via a multi-frame mechanism to capture features across consecutive RV spectra. Meanwhile, spatial consistency is realized through a selective masking mechanism to enhance reconstruction of artifact-affected regions. Extensive validation is conducted using real-world measured data collected with commercial mmWave equipment. The polyphase-code-related characteristics of the artifacts are experimentally validated. Meanwhile, the experimental results demonstrate that the proposed PIAENet not only effectively reduces the false target count but also improves the detection probability from 79.58% to 98.88%.
