Fundamentals and Experiments of Robust Respiration Sensing via Cell-Free Massive MIMO

Abstract

Respiration monitoring via radio signals enables contactless health sensing but suffers from interference caused by nearby motion. We propose a robust respiration sensing framework using Cell-free Massive MIMO (CF-mMIMO), which leverages spatial macro-diversity for interference resilience. Specifically, we analyze respiration sensing in single-antenna channels using Power Spectral Density (PSD) to reveal the impact of interference on the breathing channel’s movement spectrum. Based on this, we introduce a new metric, Sensing-Signal-to-Interference Ratio (SSIR), to evaluate local channel quality without requiring ground truth. Then, we design a Weighted Antenna Combining (WAC) method to prioritize reliable sensing links and suppress distortion. Experimental validation using a 64-antenna CF-mMIMO testbed with 100 Orthogonal Frequency-Division Multiplexing (OFDM) subcarriers over an 18 MHz bandwidth confirms the framework’s robustness. In the presence of interference, the WAC method achieves a mean waveform correlation of 0.81 with ground truth, significantly outperforming single-antenna (0.52), averaging-based methods (0.53), and existing Wi-Fi approaches. Finally, we analyze the impact of time, frequency, and spatial resource allocation on both communication and sensing performance. Results show that increasing bandwidth and antenna count benefits both communication and sensing. With a sufficient number of antennas, respiration sensing remains accurate even with long coherence times (1 second) and narrow bandwidths (3 subcarriers), enabling its integration into communication systems with negligible overhead, making it practically “for free”. This makes CF-mMIMO a promising architecture for robust and scalable Integrated Sensing and Communication (ISAC) health monitoring.