A comparison between ceiling-mounted FMCW, IR-UWB and Wi-Fi radar for in-bedroom human activity monitoring and sleep interruption detection

Despite their growing importance for contact-free radio frequency (RF) based healthcare monitoring, different radio technologies such as frequency-modulated continuous wave (FMCW) radar, impulse radio ultra-wideband (IR-UWB), and Wi-Fi sensing are rarely compared under identical deployment conditions, as existing studies typically differ in hardware, datasets, and evaluation methodologies. In addition, the performance of ceiling-mounted radars, despite their practical deployment and cost advantages in healthcare environments, remain underexplored. Therefore, this paper presents a controlled comparison and analysis of ceiling-mounted FMCW, IR-UWB, and Wi-Fi sensing using synchronized recordings from 20 participants across six room layouts. All technologies are evaluated with the same convolutional neural network (CNN) on both a fine-grained 10-class human activity recognition (HAR) task and a coarse 4-class sleep monitoring task. IR-UWB achieves the highest cross-subject activity recognition performance (89.0% macro F1), while FMCW generalizes best to unseen room layouts (83.8% macro F1). For sleep monitoring, all technologies exceed 92% macro F1 in unseen environments. The results reveal a fundamental trade-off between recognition performance and environmental robustness, which can be explained through differences in range resolution, antenna diversity, Doppler resolution, and spatial information retention. These findings provide practical guidelines for the design of healthcare-oriented RF sensing systems.

Wi-Fi 6 Cross-Technology Interference Detection and Mitigation by OFDMA: an Experimental Study

Cross-Technology Interference (CTI) poses challenges for the performance and robustness of wireless networks. There are opportunities for better cooperation if the spectral occupation and technology of the interference can be detected. Namely, this information can help the Orthogonal Frequency Division Multiple Access (OFDMA) scheduler in IEEE 802.11ax (Wi-Fi 6) to efficiently allocate resources to multiple users inthe frequency domain. This work shows that a single Channel State Information (CSI) snapshot, which is used for packet demodulation in the receiver, is enough to detect and classify the type of CTI on low-cost Wi-Fi 6 hardware. We show the classification accuracy of a small Convolutional Neural Network (CNN) for different Signal-to-Noise Ratio (SNR) and Signal-to-Interference Ratio (SIR) with simulated data, as well as using a wired and over-the-air test with a professional wireless connectivity tester, while running the inference on the low-cost device. Furthermore, we use openwifi, a full-stack Wi-Fi transceiver running on software-defined radio (SDR) available in the w-iLab.t testbed, as Access Point (AP) to implement a CTI-aware multi-user OFDMA scheduler when the clients send CTI detection feedback to the AP. We show experimentally that it can fully mitigate the 35% throughput loss caused by CTI when the AP applies the appropriate scheduling.