There has been significant progress in Wi-Fi sensing applications pertaining to home surveillance, remote healthcare, and home entertainment among others. However, most of the work leverages manual extraction of Channel State Information (CSI) from Wi-Fi network interface card (NIC) and targets single-subject sensing. In this work, we devise a simultaneous multi-subject sensing strategy that can adapt to different environments and people being monitored. Si-FI leverages standard-compliant beamforming feedback information (BFI) as a proxy of CSI to characterize the propagation environment. Unlike CSI, BFI (i) can be captured without any firmware modifications and (ii) captures the multiple channels between the access point and the stations without any direct access to the sensing devices. Thus, conversely, from existing work, the edge server in Si-FI records the BFI of the channels between Access Point (AP) and all the stations (STAs) (sensing devices) with a single capture, reducing the channel occupation, transmission and system latency dramatically. To achieve generalization over unseen environments and people, we develop a few-shot learning algorithm named Si-FI FREL to operate with beamforming feedback angles (BFAs) (compressed BFI). We validate Si-FI through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously. We demonstrate that Si-FI achieves classification accuracy of up to 99 %, while Si-FI FREL improves the accuracy up to 27 % when compared to the state-of-the-art domain adaptation algorithm. Si-FI reduces the system latency by 50 % and channel occupation by 110 KB per sample for each sensing device compared to the state-of-the-art simultaneous multi-subject sensing work.

State-of-the-art covert routing in heterogeneous networks (HetNets) focuses on balancing covertness and throughput, but often overlooks explicit energy optimization. While covert communication inherently limits transmit power, meeting throughput demands without coordinated design can still lead to high energy consumption. To this end, we propose DECOR, Decentralized Energy-efficient COvert Routing framework that jointly optimizes covertness, throughput, and energy efficiency. Unlike traditional methods that use a single wireless technology, DECOR leverages the diversity of available wireless communication technologies in HetNet to enable simultaneous multi-modal routing. The core idea behind DECOR is that optimal simultaneous utilization of multiple modalities improves throughput and overall energy efficiency. It minimizes the end-to-end energy consumption while satisfying stringent constraints on throughput and covertness through two core steps: (1)link-level optimizationusing sequential least squares programming (SLSQP), and (2)network-level optimizationthrough a custom cluster-based routing strategy. DECOR introduces a novel clustering-based strategy that aggregates intra-cluster link information and delegates routing decisions to cluster heads, significantly reducing control overhead and enabling scalable, energy-efficient covert communication. Extensive numerical analysis demonstrates that DECOR significantly outperforms existing approaches in terms of energy-efficiency and data overhead.

The performance of multiple-input, multiple-output (MIMO) systems highly depends on the precision of channel estimates provided by the mobile users. However, the current Wi-Fi standard requires an update interval of 10 ms, irrespective of the channel dynamics. This imposes a substantial overhead for the MIMO channel estimation. Recent work mainly targets different compression strategies, potentially compromising precoding accuracy and, in turn, the network performance. In stark opposition, we propose SHRINK, a framework to dynamically adapt the feedback transmission rate to the propagation environments and performance requirements. SHRINK determines whether the users should send back their channel estimates by predicting network performance through a data-driven analysis of prior and current channel estimates. We have experimentally evaluated SHRINK using off-the-shelf Wi-Fi devices in multiple environments, including an anechoic chamber, and benchmarked its performance against several state-of-the-art approaches. Experimental results show that SHRINK reduces airtime and data overhead by 81% on average compared to the IEEE 802.11 standard without impacting the precoding performance. Moreover, SHRINK outperforms state-of-the-art approaches by an average gain of 33.6% in airtime and data overhead reduction, corresponding to an increase in throughput of 24.5%.

A fundamental challenge in covert routing is that meeting both covertness and throughput requirements often leads to increased transmit power, which can significantly elevate the overall energy consumption of the network. Therefore, it is important to achieve higher throughput and better energy efficiency while maintaining the required covertness. To this end, we propose a novel simultaneous multi-modal Decentralized Energy- Efficient covert Routing approach - DEER for a multi-hop heterogeneous network (HetNet). Unlike the prevailing single-modal approaches, DEER leverages the diversity of the available wireless communication technologies for simultaneous multi-modal routing. DEER aims to minimize the end-to-end total transmit power of the whole route in a decentralized fashion while maintaining the constraints on required throughput and covertness. DEER stems into two main steps: node-level optimization followed by network-level optimization using the proposed custom-tailored Dijkstra's based link state routing protocol to meet the constraints while minimizing the end-to-end total transmit power. We demonstrate by numerical analysis that DEER improves the energy efficiency by$23.5 x$and$2.9 x$times in comparison to the baseline single-modal and naive simultaneous multimodal approaches respectively.

In this paper, we present MAGIC, a novel approach to gesture recognition utilizing mmWave multiple-input multiple-output (MIMO) Channel State Information (CSI). Unlike existing mmWave gesture recognition methods that often rely on radar signals, MAGIC leverages CSI extracted from mmWave MIMO integrated sensing and communication (ISAC) systems. While advanced radar systems, such as those operating in frequency-modulated continuous wave (FMCW) mode, can achieve high frequency and spatial resolution, they typically require dedicated sensing infrastructure, which increases system complexity. In contrast, MAGIC utilizes high-granular CSI from orthogonal frequency-division multiplexing (OFDM) systems, enabling fine spatial, temporal, and frequency-domain information for robust gesture recognition. This eliminates the need for dedicated radar transceivers, simplifying the system and reducing transmission overhead. MAGIC employs a learning-based architecture, integrating a temporal convolutional network (TCN) to classify gestures by capturing long-range temporal dependencies. To address the critical challenge of domain adaptation in gesture recognition, we propose adaptive temporal embedding network (ATEN), a meta-learning framework that combines the temporal modeling capabilities of TCN with task-specific adaptation mechanisms. We evaluateMAGIC through a comprehensive data collection campaign involving two subjects performing 10 micro gestures across three different environments, with synchronized video streams providing the ground truth. The proposed system achieves a baseline accuracy of 99.24% using TCN. The system continues to perform well – achieving up to 98.82% accuracy – when adapting to new domains using ATEN, outperforming other state-of-the-art domain adaptation methods by 14% on average.

In this paper, we present m3MIMO a mmWave fully-digital multi-user multi-input multi-output (MU-MIMO) testbed for advanced wireless research. m3MIMO operates in the 57-64 GHz frequency range and supports up to 1 GHz of bandwidth enabling large data multiplexing in the frequency domain through orthogonal frequency-division multiplexing (OFDM). The testbed features three custom-designed Zynq UltraScale+ RFSoC-based Software Defined Radios (SDRs) empowered with the Pi-Radio fully digital transceivers. Two of these SDRs support eight transmit and receive streams each (8 × 8 MIMO), while the third SDR supports up to four channels. m3MIMO supports three different communication modes: (i) point-to-point (P2P) transmissions; (ii) single-user multi-input multi-output (SU-MIMO), where multiple streams are transmitted to a single end-device; and (iii) MU-MIMO, where two devices are simultaneously served by a single transmitter. To showcase the m3MIMO's versatility, we present two research use cases: tracking-based beamforming and mmWave-based sensing. We will open-source the m3MIMO code along with the relevant use-case datasets, facilitating further analysis1.

Emerging mobile virtual reality (VR) systems are required to continuously perform complex computer vision tasks needing computational power that is excessive for mobile devices. Thus, techniques based on wireless edge computing (WEC) have been recently proposed. However, existing WEC methods require the transmission and processing of a high amount of video data which may ultimately saturate the wireless link. In this paper, we propose a novel sensing-assisted edge computing (ISAC-EC) approach to address this issue. ISAC-EC leverages knowledge about the physical environment to reduce the end-to-end latency and overall computational burden by transmitting to the edge server only the relevant data for the delivery of the service. Our intuition is that the transmission of the portion of the video frames where there are no changes with respect to the previous frames can be avoided. Through wireless sensing, only the part of the frames where any environmental change is detected is transmitted and processed. We evaluated ISAC-EC by using a 10K 360°camera with a Wi-Fi 6 sensing system operating at 160 MHz and performing localization and tracking. Experimental results show that ISAC-EC reduces both the channel occupation and end-to-end latency by more than 90% while improving the instance segmentation and object detection performance with respect to state-of-the-art WEC approaches. For reproducibility purposes, we pledge to share our dataset and code repository.

Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others.Most of the existing works are limited to the activity classification of a single human subject at a given time.Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification.The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities.Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects.To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects.We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject.We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification.To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL).Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class.For reproducibility purposes, we share our 1TB dataset and code repository 1 [1].