发表机构
School of Computer Science and Engineering, University of Electronic Science and Technology of China; KU Leuven（电子科技大学计算机科学与工程学院; 鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对6G无小区MIMO基站的呼吸感知，提出SR-TTA方法，通过空间置零和带外频谱否决的无标签测试时自适应，大幅降低带内干扰下的呼吸率误差，提升跨受试者感知性能。
AI 中文摘要
未来6G网络旨在通过复用通信基础设施将感知作为原生服务提供。我们研究无小区大规模多输入多输出（MIMO）基站上的呼吸感知，需将64天线信道融合为呼吸波形。现有最优的手工融合方法在正常条件下接近最优，但在强带内运动干扰下失效，该干扰的频率处于呼吸频带内。我们表明，学习得到的复权重波束形成器可通过空间置零恢复呼吸，且部署时可通过无标签测试时自适应（Test-Time Adaptation，TTA）缩小与单记录神谕的剩余差距。关键是，我们确定了使该方法可行的无标签信号：基于频率和方差的准则无法区分带内干扰与呼吸。我们的空间冗余测试时自适应（Spatial-Redundancy Test-Time Adaptation，SR-TTA），通过在带外频谱否决下最大化随机天线子集间的一致性，在测试中保留了正常性能。在模拟带内干扰下，呼吸率误差从5.8次/分钟（bpm）降至0.8次/分钟，且该流程可映射到开放无线接入网（Open Radio Access Network，O-RAN）架构，作为O-RAN分布式单元（O-DU）范围门控、自适应xApp和校准rApp。在真实测试台记录上，一次性跨受试者校准加SR-TTA将失败率从47%降至7%，与使用无标签测试时自适应的手工组合器表现相当。
英文摘要
Future 6G networks aim to expose sensing as a native service by reusing communication infrastructure. We study respiration sensing on a cell-free massive multiple-input multiple-output (MIMO) base station, where a 64-antenna channel must be fused into a breathing waveform. The state-of-the-art hand-crafted fusion is near-optimal in benign conditions. It collapses, however, under strong in-band motion interference, whose frequency falls inside the respiration band. We show that a learned complex-weight beamformer recovers respiration by spatial nulling, and that the remaining gap to a per-recording oracle can be closed at deployment by label-free test-time adaptation. Crucially, we identify which label-free signal makes this work. Frequency- and variance-based criteria cannot separate an in-band interferer from breathing. Our spatial-redundancy test-time adaptation (SR-TTA), which maximizes consistency across random antenna subsets under an out-of-band spectral veto, preserves benign performance in our tests. The respiration-rate error drops from 5.8 to 0.8 breaths per minute (bpm) under simulated in-band interference, and the pipeline maps onto the Open Radio Access Network (O-RAN) architecture as O-RAN distributed-unit (O-DU) range-gating, an adaptation xApp, and a calibration rApp. On real testbed recordings, a one-time cross-subject calibration plus SR-TTA reduces failures from 47% to 7%, drawing level with the hand-crafted combiner using label-free test-time adaptation.
