Conducting real-time wireless propagation simulation in interactive 3D environments is computationally demanding , and this challenge becomes more critical in Virtual Reality (VR), where stable and high frame rates are required for usability and comfort. This paper presents VRadioSim, a GPU-based wireless propagation simulator prototype that supports real-time visualisation of multipath rays and received-signal heatmaps in desktop and tethered VR modes. The prototype loads 3D scenes from GLB models and enables interactive placement of transmitter and receiver entities, visualisation of signal accumulation across scene surfaces, and point-based querying of received power values. A preliminary evaluation across varying scene complexities and ray counts indicates that the desktop version remains consistently interactive, and that tethered VR is currently most practical for lower complexity scenes. Signal sampling measurements show that received-power estimates are mostly consistent across different geometrical scene representations, suggesting stability of the current GPU-based propagation implementation. Overall, the findings demonstrate the practical feasibility of prototype for interactive desktop analysis and provide an initial basis for further optimisation and validation in immersive VR settings.

This paper evaluates Wi-Fi Fine Timing Measure ment (FTM) for 3D indoor positioning and compares it with Ultra-Wideband (UWB). We follow a two-phase experimental methodology. First, we quantify ranging precision in a 45 m corridor under Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and mixed conditions. Second, we assess 3D positioning accuracy in a laboratory using nonlinear multilateration with four anchors, under both LoS and strong NLoS conditions. The evaluation uses commercial off-the-shelf FTM devices (Google Nest Wi-Fi and Pixel 6 Pro) and a Qorvo MDEK1001 UWB development kit. Results show that Wi-Fi FTM provides meter-level 3D positioning with minimal infrastructure, while UWB achieves substantially higher ranging precision and lower 3D positioning error under LoS. Under strong NLoS, both technologies suffer from reduced range availability, preventing 3D estimation when fewer than four ranges are returned. These results clarify the accuracy versus deployability trade-offs when selecting FTM or UWB for practical 3D indoor positioning.

This paper addresses the challenge of achieving precise 3D localization of multiple objects in indoor environments using millimeter-wave (mmWave) sensing. mmWave positioning systems have recently emerged as a promising technology offering cm-level accuracy and robustness; however, the radar-like nature of mmWave technology presents challenges in multi-target positioning, particularly in complex environments where distinguishing between multiple objects becomes difficult. To address this, we explore clustering as a solution to analyze data from mmWave sensors and group similar data points, facilitating the identification of distinct targets. This paper aims to leverage the potential of mmWave radar technology to achieve precise ranging and angling measurements in multi-target environments, presenting a comprehensive methodology for evaluating the performance of mmWave sensors for achieving 3D positioning accuracy using four clustering approaches: K-Means, DBSCAN, Affinity Propagation, and BIRCH. The experimental results highlight the potential and challenges of each approach in terms of accuracy, robustness and execution time.

ABSTRACT The increasing demand for ultra‐reliable, low‐latency, and high‐throughput connectivity in dense urban environments presents significant challenges for next‐generation 6G networks. Traditional cellular networks, with their fixed cell boundaries and centralized base station control, are inadequate to meet the dynamic needs of such environments. A promising solution is the cell‐free network architecture, where a distributed set of access points (APs) jointly serve users without fixed cell boundaries. However, efficient access point selection and accurate user localization are crucial to achieving high performance in such networks. This paper presents a decentralized approach using Belief‐Desire‐Intention eXtended (BDIx) agents for dynamic AP selection and localization within a cluster‐based cell‐free 6G communications network. Various clustering algorithms (K‐means, DBSCAN, self‐organizing maps, MeanShift, ClusterGAN, and Autoencoders) are evaluated for their ability to optimize network throughput, energy efficiency, and spectral utilization. A hybrid localization framework, such as centroid‐based, differential circles, and multilateration methods, is employed to achieve accurate user positioning. The results demonstrate that machine learning‐based clustering methods, notably Gaussian mixture model (GMM), self‐organizing map (SOM), and ClusterGAN, offer significant improvements in throughput (up to 46.3%) and power reduction (up to 32.8%) over traditional methods. Regarding localization, deep learning models such as MLP, CNN, and TCN outperform deterministic methods, achieving sub‐meter accuracy with minimal errors (MeanDist < 1 m, > 0.999). Overall, the proposed solution enhances system scalability, energy efficiency, and positioning accuracy, establishing a promising foundation for future 6G networks. In our reference implementation, we instantiate the pipeline with a GMM for AP/UE clustering and a multilayer perceptron (MLP) regressor for localization.

Over the years, the wireless communication industry and the research community investigated methods of creating accurate and efficient models for signal propagation. The recent advancements in wireless communications and its exponential usage through high mobility of numerous connected devices introduced challenges in simulating dynamic radio propagation. To address these challenges, specialized software have been developed, offering high-fidelity simulations. However, these solutions have expensive cost requirements and are largely dependent on offline computations, lacking flexibility and scalability. As a result, their wider use in scientific and industrial sectors is limited. In response to these limitations, this paper proposes an alternative solution leveraging the latest developments in Gaming technologies, GPU technology, and Virtual Reality through the concept of Digital Twins to develop a prototype for a deterministic channel simulator. The prototype utilise a game development engine, high-performance GPU, and commercial VR headsets to achieve a low cost, accessible and scalable method for visualizing wireless signal propagation in real time. This paper presents the work in progress, describing the system architecture, current state of development and intended functionalities.

Indoor positioning and sensing using millimeter-wave (mmWave) and Ultra-Wideband (UWB) technologies have garnered significant attention in the literature. While extensive research exists on 2 D positioning with these technologies, a notable gap remains in addressing 3D positioning. Existing studies predominantly focus on the horizontal plane of localization, overlooking the necessity of vertical dimension integration. This paper identifies this and directly compares mmWave and UWB for 3D localisation. Addressing this gap, our work conducts a comparative analysis of 3D sensing with mmWave and 3D positioning with UWB technologies, evaluating the accuracy, robustness, efficiency, and associated challenges. This research underscores the need for future investigations to explore and assess the performance of these technologies in three dimensions.

The 3D nature of modern smart applications has imposed significant 3D positioning accuracy requirements, especially in indoor environments. However, a major limitation of most existing indoor localization systems is their focus on estimating positions mainly in the horizontal plane, overlooking the crucial vertical dimension. This neglect presents considerable challenges in accurately determining the 3D position of devices such as drones and individuals across multiple floors of a building let alone the cm-level accuracy that might be required in many of these applications. To tackle this issue, millimeter-wave (mmWave) positioning systems have emerged as a promising technology offering high accuracy and robustness even in complex indoor environments. This paper aims to leverage the potential of mmWave technology to achieve precise ranging and angling measurements presenting a comprehensive methodology for evaluating the performance of mmWave sensors in terms of measurement precision while demonstrating the 3D positioning accuracy that can be achieved. The main challenges and the respective solutions associated with the use of mmWave sensors for indoor positioning are highlighted, providing valuable insights into their potential and suitability for practical applications.

At the onset of Covid-19 several Mobile Contact Tracing Applications (MCTA) were deployed and in many cases contributed to curbing the pandemic by triggering Exposure Notifications (EN) to users who were in proximity to infected users. Recently, a number of MCTA were enhanced with Digital Presence Tracing (DPT) functionality in an effort of the public health authorities to break infection chains mostly in indoor crowded spaces and manage super-spreading events (e.g., concerts, parties). That is, alerting individuals who visited the same place or attended the same event with infected users. This is typically implemented by scanning a QR code at the venue entrance. In this work, we present a DPT solution that relies on EN-Hubs, i.e., Bluetooth-enabled IoT devices, that propagate EN in a machine-to-machine fashion reaching all visitors/attendants seamlessly through their MCTA. The proposed solution removes the overhead of issuing, managing, and scanning QR codes every time people visit a place. In addition, it can be conveniently retrofitted to existing nation-wide MCTA offering DPT capabilities with limited implementation cost.

Today there is a high demand for computing programmers, and at the same time a shortage of skilled professionals. This has triggered the creation of many initiatives in the past few years, with the aim of reversing the phenomenon. To achieve this, such events are designed to promote a more appealing image for programming, both as a profession and as a skill. This paper describes one such initiative, which uses a unique blend of differently themed, parallel workshops to motivate high school students to learn programming. With the use of questionnaires, we survey the participants and present our findings concerning the effectiveness of these workshops to engage the participants, to promote the value of coding, and to encourage the participants to consider a career in the field. We evaluate our results both at a general level, as well as by comparison among five individually themed workshops.

The objective of this work is to investigate potential accuracy improvements in the fingerprint-based indoor positioning processes, by imposing map-constraints into the positioning algorithms in the form of a-priori knowledge. In our approach, we propose the introduction of a Route Probability Factor (RPF), which reflects the possibility of a user, to be located on one position instead of all others. The RPF does not only affect the probabilities of the points along the pre-defined frequent routes, but also influences all the neighbouring points that lie at the proximity of each frequent route. The outcome of the evaluation process, indicates the validity of the RPF approach, demonstrated by the significant reduction of the positioning error.

This work proposes the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to tackle with the problem using different devices. In addition to the fact that RSS data collection might be a tedious and time-consuming process, the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a very suitable and efficient solution to tackle this problem. Moreover, in traditional fingerprint-based methods, the underlying radiomap is restricted to the mobile device for which the radiomap has been created. To overcome this limitation, we propose the use of linear data transformation to match the characteristics of various devices. We address both challenges by using 3D RT-generated radiomaps and highlight the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device.