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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12114 |
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| _version_ | 1866917926190186496 |
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| author | Xiong, Haoqiu Beerten, Robbert Cui, Zhuangzhuang Miao, Yang Pollin, Sofie |
| author_facet | Xiong, Haoqiu Beerten, Robbert Cui, Zhuangzhuang Miao, Yang Pollin, Sofie |
| contents | 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 improved respiration estimation accuracy by using multiple subcarriers and antennas. With WAC, we achieved an average correlation of 0.8 with ground truth data, compared to 0.6 for single antenna or subcarrier methods, a 0.2 correlation increase. Moreover, the system produced perfect breathing rate estimates. These findings suggest that the limited bandwidth (18 MHz in the testbed) can be effectively compensated by utilizing spatial resources, such as distributed antennas. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BS-Breath: Respiration Sensing with Cell-free Massive MIMO Xiong, Haoqiu Beerten, Robbert Cui, Zhuangzhuang Miao, Yang Pollin, Sofie Signal Processing 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 improved respiration estimation accuracy by using multiple subcarriers and antennas. With WAC, we achieved an average correlation of 0.8 with ground truth data, compared to 0.6 for single antenna or subcarrier methods, a 0.2 correlation increase. Moreover, the system produced perfect breathing rate estimates. These findings suggest that the limited bandwidth (18 MHz in the testbed) can be effectively compensated by utilizing spatial resources, such as distributed antennas. |
| title | BS-Breath: Respiration Sensing with Cell-free Massive MIMO |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.12114 |