BS-Breath: Respiration Sensing with Cell-free Massive MIMO
Haoqiu Xiong, Robbert Beerten, Zhuangzhuang Cui, Yang Miao, and Sofie, Pollin

TL;DR
This paper explores using cell-free massive MIMO OFDM base stations for accurate respiration sensing, demonstrating that combining multiple antennas and subcarriers significantly improves vital sign estimation accuracy.
Contribution
It introduces a novel respiration sensing method leveraging cell-free massive MIMO, combining space-frequency resources for enhanced accuracy over traditional single-antenna approaches.
Findings
Achieved an average correlation of 0.8 with ground truth data.
Significantly improved respiration rate estimation accuracy.
Utilized spatial resources to compensate for limited bandwidth.
Abstract
This paper demonstrates the feasibility of respiration pattern estimation utilizing a communication-centric cellfree massive MIMO OFDM Base Station (BS). The sensing target is typically positioned near the User Equipment (UE), which transmits uplink pilots to the BS. Our results demonstrate the potential of massive MIMO systems for accurate and reliable vital sign estimation. Initially, we adopt a single antenna sensing solution that combines multiple subcarriers and a breathing projection to align the 2D complex breathing pattern to a single displacement dimension. Then, Weighted Antenna Combining (WAC) aggregates the 1D breathing signals from multiple antennas. The results demonstrate that the combination of space-frequency resources specifically in terms of subcarriers and antennas yields higher accuracy than using only a single antenna or subcarrier. Our results significantly…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Molecular Communication and Nanonetworks · Wireless Body Area Networks
MethodsBalanced Selection · ALIGN · ADaptive gradient method with the OPTimal convergence rate
