The growing popularity of online and cloud gaming applications is reshaping the landscape of the entertainment industry and acting as a key driver of market growth. However, the dependency of these applications on network resources poses significant challenges to the communication infrastructure. This is particularly critical as network performance plays a key role in influencing user satisfaction during gameplay. Inevitably, these inherently interactive applications are also closely linked to the concept of quality of experience (QoE), which expresses the perceived quality of a service by end-users. In this paper, we leverage deep learning methodologies to develop an objective QoE prediction model. Specifically, the proposed prediction model investigates the effect of wireless network operation on the QoE of gaming video streaming. Employing a tailored multi-headed convolutional neural network (multi-headed CNN), the model can predict in real-time the transmission-related QoE value using measurable quality of service (QoS) parameters. To validate the effectiveness of the model, tests and evaluations were conducted in an open radio access network testbed environment equipped with O-RAN-compatible interfaces.

Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications.

The increasing densification and diversification of modern and upcoming wireless networks have become an important motivation for the development of agile spectrum sharing. Radio environment maps (REMs) are a basic tool for spectrum utilisation characterisation and adaptive resource allocation, but they need to be estimated through accurate interpolation methods. This work evaluated the performance of two established algorithms for spatial three-dimensional (3D) data collected in two real-world scenarios: indoors, through a mechanical measuring system, and outdoors, through an unmanned aerial vehicle (UAV) for measurement collection. The investigation was undertaken for the complete dataset on two-dimensional (2D) planes of different altitudes and for a subset of limited samples (representing the regions of interest or RoIs), which were combined together to describe the spatial 3D environment. A minimum error of −9.5 dB was achieved for a sampling ratio of 21%. The methods’ performance and the input data were analysed through the resulting Kriging error standard deviation (STD) and the STD of the distances between the measurement and the estimated points. Based on the results, several challenges for the interpolation performance and the analysis of the spatial RoIs are described. They facilitate the future development of 3D spectrum occupancy characterisation in indoor and UAV-based scenarios.

Modern wireless networks are rapidly advancing through research into novel applications that push the boundaries of information and communication systems to satisfy the increasing user demand. To facilitate this process, the development of communication network simulators is necessary due to the high cost and difficulty of real-world testing, with many new simulation tools having emerged in recent years. This paper surveys the latest developments in simulators that support Sixth-Generation (6G) technologies, which aim to surpass the current wireless standards by delivering Artificial Intelligence (AI) empowered networks with ultra-low latency, terabit-per-second data rates, high mobility, and extended reality. Novel features such as Reconfigurable Intelligent Surfaces (RISs), Open Radio Access Network (O-RAN), and Integrated Space–Terrestrial Networks (ISTNs) need to be integrated into the simulation environment. The reviewed simulators and emulators are classified into general-purpose and specialized according to their type of link-level, system-level, and network-level categories. They are then compared based on scalability, computational efficiency, and 6G-specific technological considerations, with specific emphasis on open-source solutions as they are growing in prominence. The study highlights the strengths and limitations of the reviewed simulators, as well as the use cases in which they are applied, offering insights into their suitability for 6G system design. Based on the review, the challenges and future directions for simulators’ development are described, aiming to facilitate the accurate and effective modeling of future communication networks.

Mobile networks have advanced significantly, providing high-throughput voice, video, and integrated data access to support connectivity through various services to facilitate high user density. This traffic growth has also increased the complexity of outlier detection (OD) for fraudster identification, fault detection, and protecting network infrastructure and its users against cybersecurity threats. Autoencoder (AE) models are widely used for outlier detection (OD) on unlabeled and temporal data; however, they rely on fixed anomaly thresholds and anomaly-free training data, which are both difficult to obtain in practice. This paper introduces statistical masking in the encoder to enhance learning from nearly normal data by flagging potential outliers. It also proposes a quasidynamic threshold mechanism that adapts to reconstruction errors, improving detection by up to 3% median area under the receiver operating characteristic (AUROC) compared to the standard 95% threshold used in base AE models. Extensive experiments on the Milan Human Telecommunications Interaction (HTA) dataset validate the performance of the proposed methods. Combined, these two techniques yield a 31% improvement in AUROC and a 34% lower computational complexity when compared to baseline AE, long short-term memory AE (LSTM-AE), and seasonal auto-regressive integrated moving average (SARIMA), enabling efficient OD in modern cellular networks.