Evaluating cellular systems, from 5th generation (5G) New Radio (NR) and 5G-Advanced to 6th generation (6G), is challenging because the performance emerges from the tight coupling of propagation, beam management, scheduling, and higher-layer interactions. System-level simulation is therefore indispensable, yet the vast majority of studies rely on the statistical 3rd Generation Partnership Project (3GPP) channel models. These are well suited to capture average behavior across many statistical realizations, but cannot reproduce site-specific phenomena such as comer diffraction, street-canyon blockage, or deterministic line-of-sight conditions and angle-of- departure/arrival relationships that drive directional links.This paper extends 5G-LENA, an NR module for the system-level Network Simulator 3 (ns-3), with a trace-based channel model that processes the Multipath Components (MPCs) obtained from external ray-tracers (e.g., Sionna Ray Tracer (RT)) or measurement campaigns. Our module constructs frequency-domain channel matrices, and feeds them to the existing Physical (PHY)/Medium Access Control (MAC) stack without any further modifications. The result is a geometry-based channel model that remains fully compatible with the standard 3GPP implementation in 5G-LENA, while delivering site-specific geometric fidelity. This new module provides a key building block toward Digital Twin (DT) capabilities by offering realistic site-specific channel modeling, unlocking studies that require site awareness, including beam management, blockage mitigation, and environment-aware sensing. We demonstrate its capabilities for precise beam-steering validation and end-to-end metric analysis. In both cases, the trace-driven engine exposes performance inflections that the statistical model does not exhibit, confirming its value for high- fidelity system-level cellular networks research and as a step toward DT applications.

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.

An upsurge of low latency and bandwidth hungry applications such as virtual reality, augmented reality and availability of unlicensed spectrum in the millimeter wave band at 60 GHz have led to standardization of the new generation WiFi systems such as IEEE 802.11ad and 802.11ay. Due to the stringent Quality of Service requirement of those applications, IEEE 802.11ad/ay have introduced contention free channel access calledService Period. One type of user traffic supported by IEEE 802.11ad is isochronous traffic, which is essentially periodic traffic that requires certain channel time to be allocated before its period ends. In an earlier work, we presented three Admission Control Algorithms (ACAs) which admit isochronous requests to achieve the above goals. One of these ACAs, the proportional fair allocation admission control (PFAAC), offers the best tradeoff across different performance metrics. But it did not consider guard time (GT) overhead, which is essential in a practical system. In this paper, we present two methods to compute upper bounds on GT overhead. We evaluate performance of the modified PFAAC with the two methods and PFAAC with no GT overhead. The modified PFAAC with the method that uses a tighter upper bound on GT overhead, provides the best performance.

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.

Millimeter-wave technology provides the necessary improvements in capacity and performance for the next generation of wireless networks. The new IEEE 802.11ay amendment extends IEEE 802.11ad to offer 100 Gbit/s connectivity in the unlicensed 60 GHz band through technical advancements such as Multiple-Input and Multiple-Output (MIMO), channel bonding and aggregation. Additionally, it offers improvements to the Beamforming Training (BFT) process in order to increase its efficiency and accuracy. One new technique defined by IEEE 802.11ay is Group Beamforming, which allows to simultaneously train all stations, and significantly reduces training overhead, especially in very dense networks. In this paper, we provide an implementation of IEEE 802.11ay in ns-3 and perform, to the best of our knowledge, the first detailed system-level evaluation of the performance of the novel IEEE 802.11ay protocol. We specifically study the performance of Group Beamforming and compare it against the legacy 802.11ad BFT. We explore how different BFT approaches scale in large networks, identify the possible problems and evaluate at how the BFT process influences the performance of the network overall. Our analysis shows that Group Beamforming can outperform the legacy approach, resulting in lower overhead and improved network performance. However, we also found that the Access Point (AP) training is quite vulnerable to interference in dense networks, introducing severe limitations to the performance, especially in large rooms where precise BFT is crucial to maintain the communication link. Therefore, we propose several improvements to Group Beamforming that improve performance and provide robust beamforming even in very dense scenarios.

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.

An upsurge of low latency and bandwidth hungry applications such as virtual reality, augmented reality and availability of unlicensed spectrum in the mmWave band at 60 GHz have led to standardization of the next generation WiFi such as IEEE 802.11ad and 802.11ay. Due to the stringent Quality of Service (QoS) requirement of those applications, 802.11ad/ay have introduced contention free channel access called Service Period, which provides dedicated channel access exclusively reserved for communication between a pair of nodes. One type of user traffic supported by IEEE 802.11ad is isochronous traffic, which is essentially periodic traffic that requires certain channel time to be allocated before its period ends. So, isochronous traffic needs guaranteed channel time allocation with stringent deadlines. In this paper, we present three Admission Control Algorithms (ACAs) which admit isochronous requests to achieve the above goals while being fair. We also present an Earliest Deadline First (EDF) based scheduling algorithm for isochronous traffic. We evaluate the performance of the three ACAs in terms of different performance metrics. Our simulation results show that, out of the three ACAs, the proportional fair allocation based algorithm offers the best tradeoff across different performance metrics.

The multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) enables an access point (AP) and multiple stations (STAs) to determine appropriate directional antenna patterns; to this end, the AP transmits multiple action frames to the STAs during the MU-MIMO BFT. However, if the antenna weight vectors (AWVs) are determined to transmit the action frames inefficiently, this could lead to unnecessary transmissions, which could increase the BFT time. To mitigate the signaling overhead, the schemes used in our previous work employed AWVs, which use multiple beams simultaneously to transmit the action frames. Nevertheless, these existing schemes are still adversely affected by redundant transmissions because these schemes overlook the transmit diversity gain obtained from multi-beam concurrent transmission. Therefore, in this study, we propose a novel transmit antenna configuration scheme that mitigates the signaling overhead by considering the transmit diversity of the inter-symbol interference (ISI) channel incurred when multiple beams are used simultaneously. Our proposed scheme determines each candidate AWV using multiple beams and efficiently identifies the STAs within reach of the corresponding multi-beam concurrent transmission. The numerical and simulation results demonstrate that our proposed scheme shortens the BFT time in comparison with existing schemes.

The IEEE 802.11ad standard extends WiFi operation to the millimeter wave frequencies, and introduces novel features concerning both the physical (PHY) and Medium Access Control (MAC) layers. The hybrid MAC layer provides for two different kinds of resource allocations: Contention Based Access Periods (CBAPs) and contention-free Service Periods (SPs). In this paper, we propose a Markov Chain model to represent CBAPs, taking into account operation interruptions due to scheduled SPs and the deafness and hidden node problems that directional communication exacerbates. We also propose a mathematical analysis to evaluate the interference among stations and derive analytical expressions to assess the impact of various system parameters on some key performance metrics such as throughput, delay, and packet drop rate. This information may be used to efficiently design a transmission scheduler that allocates contention-based and contention-free periods based on the application requirements.

IEEE 802.11ay introduced Beamforming (BF) for asymmetric links in its D0.3 release. The BF for asymmetric links aims to increase the BF Training (BFT) success probability for Stations (STAs) with insufficient link budget to communicate with an Access-Point (AP). For the BFT of asymmetric links, the AP schedules BFT allocations that use directional reception instead of quasi-omni reception to yield extra-gain on the AP's side. Each BFT allocation consists of a number of Space-time Slots (STSs) and each STA randomly selects a STS to perform its BFT. In dense environments, a large number of STAs contends for a limited number of STSs, leading to collisions. The level of collision directly depends on the number of STSs. However, there is no specification on how to adjust the number of STSs of each BFT allocation depending on network conditions. In this paper, we propose a COllision-aware STS Adaptation (COSA) scheme for IEEE 802.11ay dense environments that allows the AP to assign the optimal number of STSs for each BFT allocation based on the observed STS status. Simulation results show that COSA achieves better performance than the traditional BFT for asymmetric links in terms of BFT success ratio and BFT time while decreasing the overhead generated by the BFT allocations.

IEEE 802.11ay introduced Beamforming for Asymmetric Links (BAL) in its D0.3 release. BAL aims to guarantee an efficient Beamforming Training (BT) when a distant station (STA) has insufficient link budget to communicate with the Access Point (AP) during the BT phase. BAL makes the use of directional BAL Allocation (BALA) to avoid the usage of quasi-omni pattern at the AP side, and thus to maximize the reception gain. However, the directional BALA scheduling is left up to implementers/vendors in the IEEE 802.11ay draft. In this article, we propose a novel and dynamic algorithm to schedule BALA: the Adaptive Scheduling and random BALA Backoff (AS-BB) algorithm. AS-BB uses the Signal-to-Interference-plus-Noise Ratio (SINR) to dynamically schedule directional BALAs and allows the STA to retry the BT in the remaining BALAs. Simulation results show that the AS-BB achieves better performance than the baseline 802.11ay BAL especially in terms of the BT convergence time and BAL success rate.

The main goal of the IEEE 802.11ax project is to improve the network efficiency of IEEE 802.11 systems in dense deployments. There are currently several methods under consideration in the IEEE 802.11ax task group to achieve the aforementioned goal, including Multi-User Multiple Input Multiple Output (MU-MIMO), Multiple Packet Data Unit-aggregation, channel bonding, in addition to several Spatial Reuse (SR) improvements strategies. In this paper, we focus on the SR improvements methods and more specifically on one of the proposed solution at IEEE to improve SR for 802.11ax: Overlap Basic Service Set Physical Detection (OBSS_PD)-based SR. This solution combines adjusting the power transmission and Carrier Sense Threshold (CST) in order to increase the number of successful concurrent transmissions in dense networks. In this article, we propose and evaluate the RSSI To OBSS Threshold (RTOT) algorithm which is, to the best of our knowledge, the first algorithm to implement OBSS_PD-based SR technique. RTOT uses the beacon Received Signal Strenght Indicator (RSSI) to dynamically compute the CST, and derive afterwards the transmission power to use. We evaluate RTOT algorithm by simulation in a dense scenario. We show that the overall system throughput can be improved up to 80 % compared to IEEE 802.11 legacy networks. We highlight that the overall throughput improvement comes at the expense of per-station throughput degradation, and thus the fairness among stations is severely compromised. A trade-off must be considered between maximizing the overall throughout and maintaining an acceptable per-station throughput.

To address the issues related to the extreme densification of wireless networks, namely increased interference and lower overall throughput, the IEEE 802.11ax project was formed to revise the standard specifications for IEEE 802.11ac in order to increase the spectrum efficiency and most notably the per-user throughput by a factor of four. Improving Spatial Reuse (SR) is seen as one of the main techniques for achieving this goal. In this paper, we explore the so-called Overlap Basic Service Set Physical Detection (OBSS_PD)-based SR technique introduced in IEEE 802.11ax, which aims to increase SR by adapting both stations' Carrier Sensitivity Threshold (CST) and transmission power. To that end, OBSS_PD PD-based SR defines a proportional relationship between CST and the transmission power. In this article, we propose a novel and dynamic algorithm to implement an OBSS_PD PD-based SR. This so-called ETX To Power (ETP) algorithm uses the Expected Transmission Count (ETX) metric to dynamically compute the transmission power and the CST to use. Simulations are used to evaluate ETP in a dense scenario. We show that the overall system throughput is improved by 40 % compared to IEEE 802.11 legacy networks and at the same time the 5th percentile throughput is also increased by 22 %.

As the IPv4 address pool is being exhausted, it becomes urgent to find a way to migrate IPv4 network architectures to IPv6, or to reduce the use of IPv4 addresses. In this paper, we discuss a strategy known as “Address + Port” translation, which consists in several users sharing the same IPv4 address and being distinguished by a range of port numbers. Of critical importance for the feasibility of such a mechanism is the knowledge of the minimum number of ports to allocate to users so that no service degradation is perceived. To that extent, we analyse the port consumption of the most port-consuming Internet applications, Web browsing, and present some aggregate port consumption curves for the student population of our campus. Our results suggest that a port range of 1000 ports is totally transparent to users (which would allow to share a single IPv4 address among 64 users), while 400 ports (i.e., 150 users per address) is sufficient for most of users. Finally, the number of users per address could be further improved by benefiting from statistical multiplexing, i.e., using dynamical instead of fixed port range allocation.

This paper presents an implementation of flow binding in Nepl, the NEMO Basic Support (Nemo BS) and Mobile IPv6 implementation for Linux. Flow binding is an extension to Nemo BS and Mobile IPv6 to exchange policy routing information between a distant node and the mobile node/router. This paper also presents an evaluation which shows that the implementation is working and which benefit a mobile network can reach by using flow binding extensions.