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.

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.

Virtual reality (VR) coupled with 360° video has been used in a variety of areas, including gaming, remote learning, and healthcare, among others. The 360° video on which VR applications are based today is mostly low resolution and, in order to improve the user experience, bandwidth requirements must increase significantly. Spatial multiplexing (SM) at millimeter wave (mmWave) is an enabling technology introduced in IEEE 802.11ay to support high throughput applications. However, since IEEE 802.11ay commercial off-the-shelf devices are not yet available and the cost for implementation of mmWave testbeds is prohibitive, the expected SM performance in a real application is still unknown. In this paper, we design a mmWave multi-user (MU)-multiple-input multiple-output (MIMO) link-level high fidelity simulation platform, based on IEEE 802.11ay, which is shared as an open source code package. Our simulation platform consists of a measurement-based mmWave channel model and a digital transceiver. To support VR applications, we design the analog-digital hybrid precoders and combiners, enabling SM for MU-MIMO transmissions. We provide an extensive evaluation of the IEEE 802.11ay PHY in terms of throughput and error rates. Our platform reveals that in a living room environment, two users can support up to four streams achieving more than 20Gbit/sec data-rate per user, enabling the transmission of uncompressed 4K videos.

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.

5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications. Accurate performance evaluation is fundamental for the design of reliable mmWave networks, with accuracy rooted in the fidelity of the channel models. At mmWaves, the model must account for the spatial characteristics of propagation since networks will employ highly directional antennas to counter the much greater pathloss. In this regard, Quasi-Deterministic (QD) models are highly accurate channel models, which characterize the propagation in terms of clusters of multipath components, given by a reflected ray and multiple diffuse components of any given Computer Aided Design (CAD) scenario. This paper introduces a detailed mathematical formulation for QD models at mmWaves, that can be used as a reference for their implementation and development. Moreover, it compares channel instances obtained with an open source National Institute of Standards and Technology (NIST) QD model implementation against real measurements at 60 GHz, substantiating the accuracy of the model. Results show that, when comparing the proposed model and deterministic rays alone with a measurement campaign, the Kolmogorov-Smirnov (KS) test of the QD model improves by up to 0.537.

In this paper, we analyze the performance of link-level abstraction for orthogonal frequency-division multiplexing (OFDM) and single-carrier (SC) modes in IEEE 802.11ay wireless systems over the 60GHz millimeter-wave band. In particular, we evaluate the effectiveness of the three existing effective signal-to-noise ratio (SNR) metric (ESM) schemes (i.e., exponential ESM (EESM), mean mutual information per coded bit (MMIB) and post-processing ESM (PPESM)). Furthermore, to deal with the issue that EESM calibration is dominated by channel realizations with poor error performance, we introduce a classification based EESM (CEESM) scheme with a new metric named coefficient of variation, which is used to measure the severity of frequency-selective fading. Finally, we present several important insights developed through extensive experimentation. Based on our validation results, the MMIB and PPESM can be employed with minimum computational complexity for OFDM and SC modes, respectively. In contrast, EESM and CEESM can be considered for both modes with better accuracy, but at a cost of high implementation complexity.

In many public safety scenarios, Device-to-Device (D2D) communication should be capable of handling out-of-coverage situations, ensuring that D2D devices can communicate directly without the aid of network infrastructure. In this paper, we investigate a set of distributed resource allocation schemes for out-of-coverage D2D group communication. Particularly, we first provide guidelines concerning how to allocate D2D resources based on Modulation and Coding Scheme (MCS), Physical Resource Block (PRB) size, and Time Resource Pattern (TRP) to meet the Quality of Service (QoS) requirements of applications. We then design three distributed resource allocation schemes that select PRBs in the resource pool and/or adjust the transmitting power based on the level of available information about the network. To evaluate the designed distributed resource allocation schemes, we conduct extensive performance evaluation, validating their effectiveness in a variety of deployment scenarios.

In this paper, we analyze the physical layer of IEEE 802.11ay, the new standard for next-generation Wi-Fi operating in the unlicensed 60-GHz band, expected for release in 2019. Realistic physical-layer analysis is based on a multipath fading channel model reduced from mobile measurements, however most millimeter-wave channel sounders to date can only characterize static environments because sweeping the angular space of a channel typically takes hours due to the slow mechanical rotation of single directional antennas. Our state-of-the-art 60-GHz channel sounder, rather, employs arrays of electronically switched antennas so that a full channel sweep can be taken in fractions of a millisecond. This enabled us to conduct an extensive channel measurement campaign in an indoor environment with pedestrian motion. The fading model reduced from the measurements was us\ed to feed our 802.11ay transceiver implementation, an amendment to its 802.11ad predecessor incorporating 8x8 single-user MIMO. The analysis resulted in bit-error-rate curves for various transceiver parameters, namely phased-array-antenna dimension, number of RF chains, and modulation and coding scheme.

In this paper, we access the performance of a Device-to-Device (D2D) communication link. Particularly, we design a framework to evaluate performance with respect to coverage probability and average throughput. Our modeling framework considers a variety of D2D deployment scenarios (i.e., Outdoor-to-Outdoor, Outdoor-to-Indoor, and Indoor-to-Indoor), channel effects (i.e., path loss, shadowing, and small-scale fading), and system parameters (i.e., resource block size, fixed Modulation and Coding Scheme (MCS) versus adaptive MCS, and transmit power). For the defined scenarios, we derive mathematical expressions for the coverage probability and average throughput as a function of the distance between two D2D user equipments. Based on the designed framework, we conduct an extensive performance evaluation of the D2D communication link under various D2D deployment scenarios with varying channel effects. We also evaluate the impact of system parameters, including Physical Resource Block (PRB) size, fixed/adaptive MCS, and transmit power, on the performance of D2D communication links. Our results demonstrate the expected performance that can be achieved in practical D2D scenarios.

To date, we have designed and assembled millimeter-wave channel sounders at 60 GHz and 83 GHz. They can estimate the angle-of-departure and angle-of-arrival of channel multipath components as well as their delay and Doppler frequency shift. In addition, due to the fast acquisition time and because the receiver is mounted on a mobile robot, the systems can collect measurements for hundreds of different transmitter-receiver configurations in just minutes. It follows that channel-model reduction, including the multipath- component clustering process, must be reliable, consistent, and unsupervised. In this paper, we describe a simple clustering process tailored to the properties of millimeter-wave channels that fully exploits the multi-dimensionality of the extracted multipath components and requires only a few tunable parameters. Through extensive experimentation, we have verified that the process is robust and delivers consistent results across five different environments and across both frequency bands investigated. Illustrative examples are provided.