Person identification plays a vital role in enabling intelligent, personalized, and secure human-computer interaction. Recent research has demonstrated the feasibility of leveraging Wi-Fi signals for passive person identification using a person’s unique gait pattern. Although most existing work focuses on sub-6 GHz frequencies, the emergence of mmWave offers new opportunities through its finer spatial resolution, though its comparative advantages for person identification remain unexplored. This work presents the first comparative study between sub-6 GHz and mmWave Wi-Fi signals for person identification with commercial-off-the-shelf (COTS) Wi-Fi, using a novel dataset of synchronized measurements from the two frequency bands in an indoor environment. To ensure a fair comparison, we apply identical training pipelines and model configurations across both frequency bands. Leveraging end-to-end deep learning, we show that even at low sampling rates (10 Hz), mmWave Wi-Fi signals can achieve high identification accuracy (91.2% on 20 individuals) when combined with effective background subtraction.

Most Integrated Sensing and Communications (ISAC) systems require dividing airtime across their two modes. However, the specific impact of this decision on sensing performance remains unclear and underexplored. In this paper, we therefore investigate the impact on a gesture recognition system using a Millimeter-Wave (mmWave) ISAC system. With our dataset of power per beam pair gathered with two mmWave devices performing constant beam sweeps while test subjects performed distinct gestures, we train a gesture classifier using Convolutional Neural Networks. We then subsample these measurements, emulating reduced sensing airtime, showing that a sensing airtime of 25 % only reduces classification accuracy by 0.15 percentage points from full-time sensing. Alongside this high-quality sensing at low airtime, mmWave systems are known to provide extremely high data throughputs, making mmWave ISAC a prime enabler for applications such as truly wireless Extended Reality.

Joint Communication and Sensing (JCAS) is expected to play a critical role in next-generation wireless networks such as 6G. For complex sensing tasks, such as 3D pose estimation for virtual reality (VR) applications, accurate channel impulse response (CIR) or I/Q samples as well as processing using a neural network is required. Due to the higher bandwidth and antenna array sizes of future wireless networks, it is expected that offloading this data to a remote server for processing would require data rates in the order of 100s of Megabits per second, which is an unreasonable amount of overhead. Therefore it is necessary to preprocess the sensing data locally, and reduce the raw data to useful intermediary features, to mimimize the sensing data transmission overhead, especially when using multiple sensing devices. This paper proposes a method leveraging split inference to distribute neural networks across multiple devices, which achieves high accuracy while addressing the sensing data transfer bottleneck. We evaluate the performance of the proposed method in a VR gaming scenario, where mmWave Wi-Fi signals are used for 3D pose estimation. We show that split inference allows for reducing the communication overhead by three orders of magnitude compared to the centralised approach, while only losing 10% of accuracy. These results pave the way for future work, exploring highly distributed multi-static JCAS as a practical and efficient method of sensing.

Achieving high-quality wireless interactive Extended Reality (XR) will require multi-gigabit throughput at extremely low latency. The Millimeter-Wave (mmWave) frequency bands, between 24 and 300 GHz, can achieve such extreme performance. However, maintaining a consistently high Quality of Experience with highly mobile users is challenging, as mmWave communications are inherently directional. In this work, we present and evaluate an end-to-end approach to such a mmWave-based mobile XR system. We perform a highly realistic simulation of the system, incorporating accurate XR data traffic, detailed mmWave propagation models and actual user motion. We evaluate the impact of the beamforming strategy and frequency on the overall performance. In addition, we provide the first system-level evaluation of the CoVRage algorithm, a proactive and spatially aware user-side beamforming approach designed specifically for highly mobile XR environments.

Next-generation Virtual Reality (VR) technology enables full-user immersion and support for multiuser Virtual Experiences (VEs). Given the low-cost and passive nature of intelligent reflecting surfaces (IRSs), this paper investigates the optimal design of a multi-user IRS-assisted VR network, where an IRS is optimally deployed in a confined space as a function of VR fully-immersed users' trajectory. In particular, we consider sum-rate maximization of all VR users and optimize the Access Point's (AP) active beamforming, and the IRS's placement, phase shifts, and radiation patterns in a confined indoor environment operating in millimeter Wave (mmWave) frequencies. We introduce the Alternating Optimization (AO) algorithm, decompose the problem into distinct sub-problems, and solve each problem optimally. That is, maximum-ratio transmission (MRT) is applied for optimal beamforming at the AP, optimal closed-from IRS phase shifts are determined using quadratic transformation, global optimization is conducted to determine the ideal locations for the IRS elements, and the monotonic optimal radiation pattern has been analyzed. Our findings highlight that strategically allocating the IRS's resources at optimal physical locations enhances signal stability and maximizes per-user throughput.

Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices hold promise in precision medicine, offering novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise localization of events of diagnostic interest, which underpins the concept of flow-guided in-body nanoscale localization, would intuitively provide an added diagnostic value to the detected events. Raw data generated by the nanodevices is pivotal for this localization and consist of an event detection indicator and the time elapsed since the last passage of a nanodevice through the heart. The communication and energy constraints of the nanodevices lead to intermittent operation and unreliable communication, intrinsically affecting this data. This posits a need for comprehensively modelling the features of this data. These imperfections also have profound implications for the viability of existing flow-guided localization approaches, which are ill-prepared to address the intricacies of the environment. Our first contribution lies in an analytical model of raw data for flow-guided localization, dissecting how communication and energy capabilities influence the nanodevices’ data output. This model acts as a vital bridge, reconciling idealized assumptions with practical challenges of flow-guided localization. Toward addressing these practical challenges, we also present an integration of Graph Neural Networks (GNNs) into the flow-guided localization paradigm. GNNs, reinforced by the adaptability and resilience of Heterogeneous Graph Transformers (HGTs), excel in capturing complex dynamic interactions inherent to the localization of events sensed by the nanodevices. Our results highlight the potential of GNNs not only to enhance localization accuracy but also extend coverage to encompass the entire bloodstream.

Contemporary Virtual Reality (VR) setups often include an external source delivering content to a Head-Mounted Display (HMD). “Cutting the wire” in such setups and going truly wireless will require a wireless network capable of delivering enormous amounts of video data at an extremely low latency. The massive bandwidth of higher frequencies, such as the millimeter-wave (mmWave) band, can meet these requirements. Due to high attenuation and path loss in the mmWave frequencies, beamforming is essential. In wireless VR, where the antenna is integrated into the HMD, any head rotation also changes the antenna’s orientation. As such, beamforming must adapt, in real-time, to the user’s head rotations. An HMD’s built-in sensors providing accurate orientation estimates may facilitate such rapid beamforming. In this work, we present coVRage, a receive-side beamforming solution tailored for VR HMDs. Using built-in orientation prediction present on modern HMDs, the algorithm estimates how the Angle of Arrival (AoA) at the HMD will change in the near future, and covers this AoA trajectory with a dynamically shaped oblong beam, synthesized using sub-arrays. We show that this solution can cover these trajectories with consistently high gain, even in light of temporally or spatially inaccurate orientational data.

Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds, while at the same time constraining their physical movements inside VR setups through redirected walking. For enabling delivery of high data rate video content in real-time, the supporting wireless networks will leverage highly directional communication links that will “track” the users for maintaining the Line-of-Sight (LoS) connectivity. Recurrent Neural Networks (RNNs) and in particular Long Short-Term Memory (LSTM) networks have historically presented themselves as a suitable candidate for near-term movement trajectory prediction for natural human mobility, and have also recently been shown as applicable in predicting VR users' mobility under the constraints of redirected walking. In this work, we extend these initial findings by showing that Gated Recurrent Unit (GRU) networks, another candidate from the RNN family, generally outperform the traditionally utilized LSTMs. Second, we show that context from a virtual world can enhance the accuracy of the prediction if used as an additional input feature in comparison to the more traditional utilization of solely the historical physical movements of the VR users. Finally, we show that the prediction system trained on a static number of coexisting VR users be scaled to a multi-user system without significant accuracy degradation.

Full-immersive multiuser Virtual Reality (VR) setups envision supporting seamless mobility of the VR users in the virtual worlds, while simultaneously constraining them inside shared physical spaces through redirected walking. For enabling high data rate and low latency delivery of video content in such setups, the supporting wireless networks will have to utilize highly directional communication links, where these links will ideally have to “track” the mobile VR users for maintaining the Line-of-Sight (LoS) connectivity. The design decisions about the mobility patterns of the VR users in the virtual worlds will thus have a substantial effect on the mobility of these users in the physical environments, and therefore also on performance of the underlying networks. Hence, there is a need for a tool that can provide a mapping between design decisions about the users' mobility in the virtual words, and their effects on the mobility in constrained physical setups. To address this issue, we have developed and in this paper present a simulator for enabling this functionality. Given a set of VR users with their virtual movement trajectories, the outline of the physical deployment environment, and a redirected walking algorithm for avoiding physical collisions, the simulator is able to derive the physical movements of the users. Based on the derived physical movements, the simulator can capture a set of performance metrics characterizing the number of perceivable resets and the distances between such resets for each user. The simulator is also able to indicate the predictability of the physical movement trajectories, which can serve as an indication of the complexity of supporting a given virtual movement pattern by the underlying networks.

The need for cables with high-fidelity Virtual Reality (VR) headsets remains a stumbling block on the path towards interactive multi-user VR. Due to strict latency constraints, designing fully wireless headsets is challenging, with the few commercially available solutions being expensive. These solutions use proprietary millimeter wave (mmWave) communications technologies, as extremely high frequencies are needed to meet the throughput and latency requirements of VR applications. In this work, we investigate whether such a system could be built using specification-compliant IEEE 802.11ad hardware, which would significantly reduce the cost of wireless mmWave VR solutions. We present a theoretical framework to calculate attainable live VR video bitrates for different IEEE 802.11ad channel access methods, using 1 or more head-mounted displays connected to a single Access Point (AP). Using the ns-3 simulator, we validate our theoretical framework, and demonstrate that a properly configured IEEE 802.11ad AP can support at least 8 headsets receiving a 4K video stream for each eye, with transmission latency under 1 millisecond.

Spectrum sharing is a reality closer than one might think. Dynamic and intelligent spectrum allocation, where different networks collaborate to optimize spectrum usage jointly, is required to overcome spectrum scarcity. Artificial Intelligence (AI) can play a major role to solve this complex problem in dynamic environments with continuously changing data requirements. A part of the problem can be solved by using smart AI-enabled flow control. Smart flow control can have a major impact on the spectrum footprint of mobile networks where it can optimize the Quality of Service for the own and neighboring networks. This paper presents the architecture and the basic principles of the dynamic spectrum footprint control based on flow prioritization of the SCATTER radio system, a wireless endto-end communication system that participated in the DARPA Spectrum Collaboration Challenge. The flow control mechanism is a policy-based framework.

Wireless network technologies are becoming more and more popular. Because of this, important parts of the wireless spectrum become overloaded. Static spectrum allocation, which has been the norm for decades, is not suitable anymore. To maintain the high demand for spectrum and the continuous development of new wireless technologies, there is a need for an intelligent, dynamic spectrum allocation mechanism, where different network technologies collaboratively optimize the spectrum usage. New wireless network paradigms, such as Neutral Host Networks (NHNs) and private 5G, require a smart, spectrum-footprint-aware flow control algorithm to overcome the spectrum scarcity in collaborative way. This article presents a strategy, vision and flow control mechanism to implement collaboration in a Quality of Service (QoS)-driven way. The solution in this article is based on policies which may activate depending on its current and neighbor's network states. Through a flow ordering and selection strategy, these policies optimize the spectrum footprint, based on the performance and QoS-requirements of the own and surrounding networks. The proposed algorithm is tested extensively and validated on a large scale during the DARPA Spectrum Collaboration Challenge (SC2) competition. The results of the SC2 final event and intermediate scrimmages showed that the proposed approach increased the score, indicating increased inter-network collaboration was achieved.

Current mobile consumer devices are equipped with the ability to connect to the Internet using a variety of heterogeneous wireless network technologies (e.g., Wi-Fi and LTE). These devices generally opt to statically connect using a single technology, based on predefined priorities. This static behavior does not allow the network to unlock its full potential, which becomes increasingly more important as the requirements of services, in terms of for example throughput and reliability, grow. Multipath TCP (MPTCP) is a solution that allows the simultaneous use of multiple network interfaces. However, it does this uncoordinated for a single connection between two endpoints. Therefore, this paper proposes a Software-Defined Networking (SDN) architecture to enable coordinated multi-path routing across the several networks for mobile devices. Moreover, we propose a novel weighted MPTCP scheduler that allows the transmission of certain controllable percentages of data per network interface. The proposed idea is evaluated through a real-life prototype implementation with a smartphone.

Since its introduction in 2012, telecommunications operators have been applying the Network Function Virtualization principle to their core infrastructure, leading to more agile and cost-efficient deployments. While these Virtualized Network Functions (VNFs) are traditionally implemented using Virtual Machines (VMs), efforts are starting to shift to containerized VNF implementations, further improving agility and cost-efficiency. Furthermore, telecom applications often require extreme networking performance in terms of throughput and latency. While research has shown that containers outperform VMs on this front, it is currently unclear how the choice of container provider influences network performance. In this paper we compare the networking performance of Linux container implementations Docker, rkt and LXC. Throughput and latency are evaluated for single-host host, bridge (or NAT) and macvlan network configurations. This is, to the best of our knowledge, the first comparison featuring all three major Linux container implementations. We show that LXC performs best, with Docker and rkt showing throughputs of respectively up to 35 % and 58 % lower. Of the considered networking implementations, the macvlan network performs best. While it experiences a significant performance degradation when many containers are chained together, a single container using macvlan can outperform even a bare metal implementation when enough CPU resources are available.