Problem
Research questions and friction points this paper is trying to address.
respiration sensing
cell-free massive MIMO
test-time adaptation
in-band interference
beamforming
SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing
🤖 AI Summary
This study addresses the failure of conventional fusion methods in 6G cell-free MIMO respiration sensing caused by in-band motion interference. We propose a test-time adaptation (TTA) framework that exploits spatial redundancy. By revealing the inability of frequency- and variance-based criteria to distinguish in-band interference, this work innovatively leverages the spatial redundancy of antenna subsets as unlabeled signals to drive TTA. Furthermore, learnable complex beamforming is integrated with a spectral veto mechanism to achieve interference-robust sensing. Experimental results demonstrate that under simulated interference, the respiration rate error decreases from 5.8 to 0.8 bpm, while real-world measurement failure rates drop from 47% to 7%, achieving performance comparable to handcrafted fusion approaches.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Test-Time Adaptation
Spatial Redundancy
Cell-Free Massive MIMO
Respiration Sensing
O-RAN
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