发表机构
Dongguan University of Technology; Macau University of Science and Technology; Xinjiang University; Harbin Institute of Technology, Shenzhen（东莞理工学院; 澳门科技大学; 新疆大学; 哈尔滨工业大学（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对环境反向散射通信中的联合定位与检测，提出GeoFrameNet保持几何不确定性并融合跨帧共识，显著降低定位误差和误码率。
AI 中文摘要
我们解决了环境反向散射通信中的联合连续定位与数据检测问题，其中弱观测可能使相互竞争的几何假设都看似合理，而分阶段的点估计接口可能会丢弃关于这种模糊性的信息。我们开发了几何感知帧网络（GeoFrameNet），它将信道频率响应投影到基于物理的时延-到达角格点上，并在正交频分复用符号上聚合符号稳健证据，以形成关于物理可行候选的共享几何后验。定位使用完整的后验并进行候选特定细化；检测结合候选条件差分对数几率，并使用在选定候选上重新归一化的后验权重。比特监督引导几何评分。对于固定设备，跨帧几何共识网络（CFGC-Net）融合冻结的GeoFrameNet候选对数几率及跨帧特征以进行定位，同时保留逐帧检测输出。在独立测试集上，GeoFrameNet在所有八个评估的信噪比（SNR）点上均实现了比全符号多测量向量稀疏贝叶斯学习（MMV-SBL）基线更低的定位均方根误差（RMSE），同时在-30至-22.5 dB范围内同时降低了误码率和RMSE。在-30 dB SNR下，相应的RMSE分别为3.1573米和18.8859米。CFGC-Net在二、四和八帧的评估融合策略中实现了最低的总RMSE。
英文摘要
We address joint continuous localization and data detection in ambient backscatter communication, where weak observations can leave competing geometry hypotheses plausible and a staged point-estimate interface can discard information about this ambiguity. We develop the \emph{Geometry-aware Frame Network (GeoFrameNet)}, which projects channel frequency responses onto a physics-derived delay--angle-of-arrival lattice and aggregates sign-robust evidence across orthogonal frequency-division multiplexing symbols to form a shared geometry posterior over physically feasible candidates. Localization uses the full posterior with candidate-specific refinement; detection combines candidate-conditioned differential logits using posterior weights renormalized over selected candidates. Bit supervision guides geometry scoring. For a fixed device, the \emph{Cross-Frame Geometry Consensus Network (CFGC-Net)} fuses frozen GeoFrameNet candidate logits and features across frames for localization while preserving frame-wise detection outputs. On an independent test set, GeoFrameNet achieves lower localization root-mean-square error (RMSE) than an all-symbol multiple-measurement-vector sparse Bayesian learning (MMV-SBL) baseline at all eight evaluated signal-to-noise ratio (SNR) points, with simultaneous bit error rate and RMSE reductions from $-30$ to $-22.5$~dB. At $-30$~dB SNR, the respective RMSEs are 3.1573 and 18.8859~m. CFGC-Net achieves the lowest aggregate RMSE among the evaluated fusion strategies for two, four, and eight frames.
