The quasi-optical propagation of millimeter-wave (mmWave) signals enables high-accuracy localization algorithms that employ geometric approaches or machine learning models. However, most algorithms require information on the indoor environment, may entail the collection of large training datasets, or bear an infeasible computational burden for commercial off-the-shelf (COTS) devices. In this work, we propose to use tiny neural networks (NNs) to learn the relationship between angle difference-of-arrival (ADoA) measurements and locations of a receiver in an indoor environment. To relieve training data collection efforts, we resort to an algorithm-supervised approach by bootstrapping the training of our neural network through location estimates obtained from a state-of-the-art localization algorithm. We evaluate our scheme via mmWave measurements from indoor 60-GHz double-directional channel sounding. We process the measurements to yield dominant multipath components, use the corresponding angles to compute ADoA values, and finally obtain location fixes. Results show that the tiny NN achieves sub-meter errors in 74% of the cases, thus performing as good as or even better than the state-of-the-art algorithm, with significantly lower computational complexity.

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.

Accurately assessing the number of people in a room is essential for enhancing operational efficiency, safety, and sustainability, enabling applications such as smart building management, energy conservation, and emergency evacuation planning. Wi-Fi sensing, favored for its privacy-preserving capabilities and the ubiquity of Wi-Fi infrastructure, has become a popular method for such sensing tasks, including people counting. Within this context, our paper introduces a Convolutional Neural Network (CNN) model for millimeter wave (mmWave) Wi-Fi people counting, capitalizing on the IEEE 802.11bf amendment, and in particular its passive sensing framework. By evaluating Range-Doppler (RD), Azimuth-Doppler (AD), and AzimuthRange (AR) map representations as inputs, we establish AR maps’ superiority, achieving up to $\mathbf{9 8 . 5 7 \%}$ accuracy for counting up to four individuals. Our solution, free of communication overhead, also minimizes energy consumption and computational requirements, offering a scalable and efficient sensing solution for people counting, which is particularly well-suited for Internet of Things (IoT) devices with limited resources.

Wi-Fi sensing has been used to detect and track movements in an environment, resulting in the emergence of several innovative applications. Wi-Fi sensing can detect movement and locate objects by analyzing variations in the Wi-Fi signal due to its interaction with moving objects. Until recently, Wi-Fi sensing has been primarily available through proprietary solutions, which has limited its adoption. However, the recent initiative by the IEEE to develop the IEEE 802.11bf standard promises to make the adoption of Wi-Fi sensing widespread. Although Wi-Fi sensing procedures in communication standards can be overhead, there is currently a lack of literature exploring the sensing performance of Wi-Fi sensing procedures specified in the IEEE 802.11bf standard and its impact on data communication. Therefore, this paper presents a comprehensive evaluation of the sensing performance of the IEEE 802.11bf protocol and its impact on data communication in different configurations. Our findings expose the limitations of specific configurations and pave the way to provide guidance on efficient operating configurations of an IEEE 802.11bf network.

The next generation WiFi such as IEEE 802.11ad and 802.11ay can provide stringent Quality of Service (QoS) due to its support of contention free channel access called Service Period. IEEE 802.11ad supports two types of user traffic: isochronous and asynchronous. These user traffic need guaranteed Service Period duration before their periods. Hence, admission control plays an important role in an IEEE 802.11ad system. In an earlier work we studied admission control only for isochronous requests. In this paper, we present admission control and scheduling algorithms which can handle both types of requests. We devise a proportional fair and linear run time complexity algorithm that treats asynchronous requests as periodic requests, because of which it overallocates resources to the asynchronous requests. The conditions of possible performance loss due to this overallocation are analyzed. We provide detailed simulation results which show that presence of asynchronous request degrades performance of isochronous requests in terms of number of admitted requests and channel utilization. But, the smaller number of admitted isochronous requests perform better in terms of channel allocation time and delay.

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.

Dual-connectivity streaming is a key enabler of next-generation six Degrees Of Freedom (6DOF) Virtual Reality (VR) scene immersion. Indeed, using conventional sub-6 GHz WiFi only allows to reliably stream a low-quality baseline representation of the VR content, while emerging high-frequency communication technologies allow to stream in parallel a high-quality user viewport-specific enhancement representation that synergistically integrates with the baseline representation to deliver high-quality VR immersion. We investigate holistically as part of an entire future VR streaming system two such candidate emerging technologies, Free Space Optics (FSO) and millimeter-Wave (mmWave), that benefit from a large available spectrum to deliver unprecedented data rates. We analytically characterize the key components of the envisioned dual-connectivity 6DOF VR streaming system that integrates in addition edge computing and scalable 360° video tiling, and we formulate an optimization problem to maximize the immersion fidelity delivered by the system, given the WiFi and mmWave/FSO link rates, and the computing capabilities of the edge server and the users’ VR headsets. This optimization problem is mixed integer programming of high complexity and we formulate a geometric programming framework to compute the optimal solution at low complexity. We carry out simulation experiments to assess the performance of the proposed system using actual 6DOF navigation traces from multiple mobile VR users that we collected. Our results demonstrate that our system considerably advances the traditional state of the art and enables streaming of 8K-120 frames-per-second (fps) 6DOF content at high fidelity.

The ubiquitous deployment and availability of wireless communications devices, coupled with recent technical advancements, provide a unique opportunity to enable wireless sensing applications, leveraging existing communications equipment and signals. The availability of modeling tools and dataset is crucial to support the development of sensing techniques and to understand the end-to-end performance of a joint wireless communication and sensing system. However, most of the sensing performance evaluations are carried out using proprietary tools and dataset. In this paper, we present a set of open source tools and models enabling the evaluation of future WLAN sensing systems. Our framework is composed of a ray-tracing implementation specific for sensing application, an IEEE 802.11ay physical layer (PHY) digital transceiver model and a visualization application. Using these tools, we design a dataset consisting of more than 14 000 entries of millimeter wave channels and IEEE 802.11 ay signals to democratize the design of both data-driven and model driven communication and sensing algorithms. We also provide a preliminary evaluation of a CSI-based WLAN sensing system using IEEE 802.11 ay signals. The results indicate that existing communication systems can be used to enable sensing applications.

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.

Virtual Reality (VR) promises immersive experiences in diverse areas such as gaming, entertainment, education, healthcare, and remote monitoring. In VR environments, users can navigate 360-degree content by moving or looking around in all directions, by rotating their heads, as in real life. A rapid head rotation can corrupt the wireless link, degrading the user experience. Due to the lack of proper head rotation models, testbeds are usually required to analyze VR systems. In this paper, we propose an open source code package that generates realistic head rotation traces. The code package is based on a simple, yet flexible, time-correlated mathematical model, which is extrapolated from a publicly available VR head rotation measurement-based dataset. We show that the probability density function of head rotation pitch and roll angles can be modeled as Gaussian distributions, while the probability density function of yaw angles can be modeled as a Gaussian mixture distribution. To introduce temporal correlation, we extrapolate the power spectral density of the angular processes, which are modeled with a bi-exponential decay. Finally, we show how the model can support and accelerate the design of future VR systems by proposing the analysis of a distributed Multiple Input Multiple Output (MIMO) system and the design of a situational awareness Machine Learning (ML) based beamforming training for millimeter wave networks.

Millimeter-wave is one of the technologies powering the new generation of wireless communication systems. To compensate the high path-loss, millimeter-wave devices need to use highly directional antennas. Consequently, beam misalignment causes strong performance degradation reducing the link throughput or even provoking a complete outage. Conventional solutions, e.g. IEEE 802.11ad, propose the usage of additional training sequences to track beam misalignment. These methods however introduce significant overhead especially in dynamic scenarios. In this paper we propose a beamforming scheme that can reduce this overhead. First, we propose an algorithm to design a codebook suitable for mobile scenarios. Secondly, we propose a blind beam tracking algorithm based on particle filter, which describes the angular position of the devices with a posterior density function constructed by particles. The proposed scheme reduces by more than 80% the overhead caused by additional training sequences.

Multi-user MIMO at millimeter wave frequencies combines the huge bandwidth availability at millimeter wave with the spatial multiplexing gain of MIMO processing. Hybrid architectures have been proposed to reach this purpose while keeping implementation costs reasonably low. Active power splitter devices are the key components of a hybrid architecture enabling a lower implementation complexity compared to a full-digital solution. They distribute the same signal to several antenna elements shifting part of the precoding operations to the analog domain. Active power splitter devices however suffer from frequency distortion and their characterization is thus essential for new system designs. In this paper we propose a splitter model based on real hardware measurements showing that the splitter design critically affects the system performance. The frequency distortion influencing the digital channel estimation significantly distorts the precoded signal leading to an EVM floor in the high SNR region. However, when dynamically adapting the splitter operating point to control the trade-off between gain and bandwidth adaptively, the system EVM can be drastically reduced.

Given the high throughput requirement for 5G, merging millimeter wave technologies and multi- user MIMO seems a very promising strategy. As hardware limitations impede to realize a full digital architecture, hybrid MIMO architectures using both analog and digital precoding are considered a feasible solution to implement multi- user MIMO at millimeter wave. Real channel propagation and hardware non-idealities degrade the performance of such systems thus experimenting the new architecture is crucial to support system design. Nevertheless, hybrid MIMO systems are not yet understood as the effects of the wide channel bandwidths at millimeter wave, the non-ideal RF front end as well as the imperfections of the analog beamforming using phased antenna arrays are often neglected. In this paper, we present a 60 GHz multi-user MIMO testbed using phased antenna arrays at both transmitter and receivers. The base station equipped with a 32 phased antenna array allocates simultaneously two users. We show that frequency selective hybrid precoding can efficiently suppress inter-user interference enabling spatial multiplexing in interference limited scenario doubling the throughput compared to a SISO scenario and compensating the frequency fluctuation of the channel. In addition, we report an EVM constellation improvement of 6 dB when comparing the hybrid MIMO architecture with a fully analog architecture.

Distributed massive multiple-input multiple-output (DM-MIMO) gives a higher spectral efficiency and enhanced coverage area, compared to collocated massive MIMO (CM-MIMO). In general, for massive MIMO, time division duplex is preferable as it enables downlink (DL) precoding based on uplink (UL) channel estimation. A time division duplex (TDD) reciprocity calibration is then essential to compensate the gap between the UL-DL channels, which can be done completely in the base station relying on sounding reference signals (SRS). For a collocated array, relying on mutual coupling between antenna elements, each SRS is received with sufficient power in the array, enabling a reliable estimate of the calibration coefficients. Nevertheless, for DM-MIMO, much less power is collected in distant inter-cluster antennas which degrades the accuracy of the estimated calibration. In this paper, we propose a novel inter-cluster combining method (ICCM) which improves the signal-to-quantization-noise ratio (SQNR) of the SRS, and hence achieves a more robust calibration accuracy for practical DM-MIMO systems. Our experimental results of two 32-antenna arrays distributed in an indoor environment show that ICCM outperforms the existing state-of-the-art algorithms in the sense of lower DL error vector magnitude (EVM) by exploiting diversity and array gain efficiently.