Sensing biometric markers such as respiration rate (RR) and heart rate (HR) in non-medical contexts using the high resolution of Millimeter-Wave (mmWave) Wi-Fi networks has recently gathered considerable attention. A significant challenge in deploying a Wi-Fi system capable of performing sensing tasks is to minimize the overhead on the communication tasks associated with acquiring sensing information, both in terms radio resources and memory usage. In this paper, we explore the potential of IEEE 802.11bf passive sensing as a means to mitigate overhead, while effectively estimating both RR and HR. We showcase the potential to develop a low overhead Wi-Fi system that precisely captures vital signs, even in demanding situations, such as rapidly increasing RR interfering with HR, by integrating microdoppler processing with super-resolution eigenvector noise subspace analysis. The results shows that the proposed methodology enables RR and HR estimation without any radio-resource overhead and requiring very limited memory usage.

Switched phased array systems operating at high sample rates generate large amounts of data during measurements of radio channels, but many scenarios contain only few multipath components. Compressed Sensing suggests in these cases Nyquist-rate samples are wasteful in terms of data size. It has been observed that structural parameters, i.e., time of flight and angle of arrival, of the propagation paths are temporally consistent, as they vary slowly in time.Hence, we propose a local temporally consistent signal model that includes delay and angle of arrival, and also their time-derivatives, coherently connecting multiple radio channel snapshots. This allows to use a cyclic compression scheme consisting of a few compression matrices that extract mutually incoherent information from adjacent snapshots. Last, we present an algorithm to extract specular multipath components from these compressive measurements.We verify our findings on simulated data and real measurements. On simulated data we observe a good agreement of the estimates with the available ground-truth and show that the proposed cyclic compression scheme improves estimation accuracy. On measured data, we compare the estimates from data obtained at Nyquist rate to data compressed to 17% of the original size and find good agreement as well.

Integrated sensing and communication (ISAC) systems have been investigated by the research and standardization communities in the recent past. Accurately localizing the target and tracking the target’s movement are critical for numerous smart Internet of Things (IoT) systems (smart manufacturing, smart transportation, etc.). This paper aims to realize super-resolution localization and tracking in WiFi sensing by leveraging the IEEE 802.11ad beamforming training procedure. We leverage the CLEAN-Space-Alternating Generalized Expectation-maximization (CLEAN-SAGE) algorithm on a single beam sweeping cycle for target localization and investigate the targets’ delays and angle estimation. For tracking moving targets, we design mechanisms to estimate the target’s motion, including the target’s velocity and motion pattern, such as estimating the target’s spatial positions over time to obtain the Doppler shift or tracking its trajectory using a Kalman filter. In order to prove that our approach works effectively, we conduct a thorough performance evaluation study. Our evaluation results confirm that the CLEAN-SAGE algorithm can achieve estimation performance beyond the ISAC system’s inherent bandwidth and beamwidth constraints. Furthermore, we provide insights into how system configurations, including antenna size, beam overlap, and the number of iterations in the SAGE algorithm, influence its performance.

Sensing with communication waveforms has drawn growing interest thanks to the ubiquitous availability of wireless networks. However, the required sensing resources may not always be available in a communication system. In addition, the communication system may have limited bandwidth, beamwidth, and transmit power, which could limit the sensing accuracy. To investigate such challenges, in this article, we study the feasibility of using the sector-level sweeping (SLS) procedure of IEEE 802.11ad to provide opportunistic indoor radar sensing service, which is vital to smart Internet of Things (IoT) applications. In particular, we design a framework to estimate the target’s spatial position with respect to delay and angle by employing the multiple signal classification (MUSIC) super-resolution algorithms. We conduct an extensive performance evaluation to understand the tradeoffs between sensing accuracy and required sensing resources in terms of system configurations (e.g., antenna array size and the overlapping of neighboring beams) and the impact of signal-to-noise ratio (SNR). Furthermore, based on the human multipath reflections captured from a real-world measurement campaign, we reconstruct the sensing channel, investigate the feasibility of monitoring the gesture behavior in a smart home environment, and discuss some findings and insights.

Efficient design of integrated sensing and communication systems can minimize signaling overhead by reducing the size and/or rate of feedback in reporting channel state information (CSI). To minimize the signaling overhead when performing sensing operations at the transmitter, this paper proposes a procedure to reduce the feedback rate. We consider a threshold-based sensing measurement and reporting procedure, such that the CSI is transmitted only if the channel variation exceeds a threshold. However, quantifying the channel variation, determining the threshold, and recovering sensing information with a lower feedback rate are still open problems. In this paper, we first quantify the channel variation by considering several metrics including the Euclidean distance, time-reversal resonating strength, and frequency-reversal resonating strength. We then design an algorithm to adaptively select a threshold, minimizing the feedback rate, while guaranteeing sufficient sensing accuracy by reconstructing high-quality signatures of human movement. To improve sensing accuracy with irregular channel measurements, we further propose two reconstruction schemes, which can be easily employed at the transmitter in case there is no feedback available from the receiver. Finally, the sensing performance of our scheme is extensively evaluated through real and synthetic channel measurements, considering channel estimation and synchronization errors. Our results show that the amount of feedback can be reduced by 50% while maintaining good sensing performance in terms of range and velocity estimations. Moreover, in contrast to other schemes, we show that the Euclidean distance metric is better able to capture various human movements with high channel variation values.

The design of integrated sensing and communication (ISAC) systems has drawn recent attention for its capacity to solve a number of challenges. Indeed, ISAC can enable numerous benefits, such as the sharing of spectrum resources, hardware, and software, and improving the interoperability of sensing and communication. In this article, we seek to provide a thorough investigation of ISAC. We begin by reviewing the paradigms of sensing-centric design, communication-centric design, and co-design of sensing and communication. We then explore the enabling techniques that are viable for ISAC (i.e., transmit waveform design, environment modeling, sensing source, signal processing, and data processing). We also present some emergent smart-world applications that could benefit from ISAC. Furthermore, we describe some prominent tools used to collect sensing data and publicly available sensing data sets for research and development, as well as some standardization efforts. Finally, we highlight some challenges and new areas of research in ISAC, providing a helpful reference for ISAC researchers and practitioners, as well as the broader research and industry communities.

Public safety incidents typically involve a significant amount of group traffic and have a stringent requirement of connection reliability. Hence multicast could potentially improve network and user performance significantly, which triggered our study on Long-Term Evolution (LTE) Multicast Broadcast Single Frequency Network (MBSFN). In this paper, we first derive a realistic MBSFN Signal-to-Interference-plus-Noise Ratio (SINR) analytical model, with multiple antennas, multipath channel, and equalizer considered. Then through comprehensive system level simulations, we compare MBSFN and unicast in terms of resource efficiency v.s. throughput, as well as outage probability. We also study the impacts of MBSFN size and quantify the MBSFN SINR improvement due to diversity combining and interference reduction, respectively.

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

Public safety incidents typically involve significant amount of group traffic. This paper initializes our study in exploring the potential spectrum savings and improvement in first responders experience by using Multicast Broadcast Single Frequency Network (MBSFN) to serve group traffic among first responders. Towards this goal, this paper proposes a comprehensive methodology that closely follows The 3rd Generation Partnership Project (3GPP) specifications and considers unicast multiple-input multiple-output (MIMO) and MBSFN without MIMO. High fidelity Block Error Rate (BLER) curves for both MBSFN and unicast are generated, and Signal-to-noise ratio (SNR) points to switch Channel Quality Indicators (CQIs) are extracted and analyzed. Several simulation scenarios have been designed and the empirical results are analyzed.