K Foysal Haque

High-throughput wireless communications and ac- curate sensing rely on high-resolution channel estimates. Es- timation algorithms should be able to accurately capture the underlying wireless multipath propagation to compensate for the channel in data transmission while leveraging it for acquiring sensing information. However, due to the frequency-dependent superposition of delayed propagation paths, certain components may undergo destructive interference at specific frequencies, obscuring their contribution. This leads to partial reconstruction of the channel, thereby degrading the overall estimation fidelity. To address this challenge, we propose BANDWEAVE, a new multi-band channel frequency response (CFR) fusion framework that significantly enhances channel estimation by leveraging complementary multipath information across multiple frequency bands. BANDWEAVE includes a three-phase progressive learning algorithm enabling adaptive, robust, and efficient fusion in dynamic multi-band wireless environments. We experimentally evaluate BANDWEAVE with an IEEE 802.11ac multi-user mul- tiple input multiple output (MU-MIMO) testbed and a 60 GHz mmWave single-user multiple input multiple output (SU-MIMO) system in three different propagation environments with different fading conditions. Experimental results show that BANDWEAVE channel estimation outperforms single-band estimation achieving more than 16% of throughput improvement and bit error rate (BER) reduction. Also, BANDWEAVE achieves more than 4.9× gain in communication performance compared to state of the art (SOTA) band-merging approaches. Latency measurements on Jetson Nano, Jetson Orin Nano and Raspberry Pi 5 show that BANDWEAVE reduces inference time and energy consumption by up to 17× and 18×, respectively, with respect to SOTA methods.

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.

Emerging applications require mobile devices to continuously execute complex deep neural networks (DNNs). While mobile edge computing (MEC) may reduce the computation burden of mobile devices, it exhibits excessive latency as it relies on encapsulating and decapsulating frames through the network protocol stack. To address this issue, we propose PhyDNNs, an approach where DNNs are modified to operate directly at the physical layer (PHY), thus significantly decreasing latency, energy consumption, and network overhead. Conversely from recent work in Joint Source and Channel Coding (JSCC), PhyDNNs adapt already trained DNNs to work at the PHY. To this end, we developed a novel information-theoretical framework to fine-tune PhyDNNs based on the trade-off between commu-nication efficiency and task performance. We have prototyped PhyDNNs with an experimental testbed using a Jetson Orin Nano as the mobile device and two USRP software-defined radios (SDRs) for wireless communication. We evaluated PhyDNNs performance considering various channel conditions, DNN models, and datasets. We also tested PhyDNNs on the Colosseum net-work emulator considering two different propagation scenarios. Experimental results show that PhyDNNs can reduce the end-to-end inference latency, amount of transmitted data, and power consumption by up to 48×, 1385×, and 13 × while keeping the accuracy within 7 % of the state-of-the-art approaches. Moreover, we show that PhyDNNs experience 4.3 times less latency than the most recent JSCC method while incurring in only 1.79% performance loss. For replicability, we shared the source code for the PhyDNNs implementation.

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 23.5x and 2.9x times in comparison to the baseline single-modal and naive simultaneous multimodal approaches respectively.

In this paper, we propose BeamSense, a completely novel approach to implement standard-compliant Wi-Fi sensing applications. Existing work leverages the manual extraction of the uncompressed channel state information (CSI) from Wi-Fi chips, which is not supported by the 802.11 standards and hence requires the usage of specialized equipment. On the contrary, BeamSense leverages the standard-compliant compressed beamforming feedback information (BFI) (beamforming feedback angles (BFAs)) to characterize the propagation environment. Conversely from the uncompressed CSI, the compressed BFAs (i) can be recorded without any firmware modification, and (ii) simultaneously captures the channels between the access point and all the stations, thus providing much better sensitivity. BeamSense features a novel cross-domain few-shot learning (FSL) algorithm for human activity recognition to handle unseen environments and subjects with a few additional data samples. We evaluate BeamSense through an extensive data collection campaign with three subjects performing twenty different activities in three different environments. We show that our BFAs-based approach achieves about 10% more accuracy when compared to CSI-based prior work, while our FSL strategy improves accuracy by up to 30% when compared with state-of-the-art cross-domain algorithms. Additionally, to demonstrate its versatility, we apply BeamSense to another smart home application – gesture recognition – achieving over 98% accuracy across various orientations and subjects. We share the collected datasets and BeamSense implementation code for reproducibility – https://github.com/kfoysalhaque/BeamSense.

In this letter, we propose SCOPE—a novel, entropy-weighted ensembling approach for material classification at sub-Terahertz (THz) frequencies. Unlike existing methods that primarily use dedicated radars, SCOPE builds upon an integrated communication and sensing system and leverages information from both penetrating and reflected signals to enhance spatial resolution and detection accuracy across environments. We adopted spatial variability augmentation (SVA) to address the challenge of generalization across varying transmission distances and antenna gains. While most prior works are limited to radar systems or simulations, SCOPE is implemented and validated in a real sub-THz system working with a 10 GHz bandwidth. Our assessments across different sensing distances, antenna gains, and channel conditions demonstrate the efficacy of SCOPE, which reaches up to 99% accuracy in detecting five materials—glass, wood, metal, air, and plastic—outperforming existing techniques. To facilitate reproducibility, our dataset and code are available at: https://github.com/kfoysalhaque/SCOPE.