The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-high-definition video, and extended reality, requires the enhancement of end-users’ perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user’s personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.

As we advance towards 6G communication systems, the number of network devices continues to increase resulting in spectrum scarcity. With the help of Spectrum Sensing (SS), Cognitive Radio (CR) exploits the frequency spectrum dynamically by detecting and transmitting in underutilized bands. The performance of 6G networks can be enhanced by utilizing Deep Neural Networks (DNNs) to perform SS. This paper provides a detailed survey of several Deep Learning (DL) algorithms used for SS by classifying them as Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, combined CNN-LSTM architectures and Autoencoders (AEs). The works are discussed in terms of the input provided to the DL algorithm, data acquisition technique used, data pre-processing technique used, architecture of each algorithm, evaluation metrics used, results obtained, and comparison with standard SS detectors. This survey further provides an overview of traditional ML algorithms and simple Artificial Neural Networks (ANNs) while highlighting the drawbacks of conventional SS approaches for completeness. A description of some publicly available Radio Frequency (RF) datasets is included and the need for comprehensive RF datasets and Transfer Learning (TL) is discussed. Furthermore, the research challenges related to the use of DL for SS are highlighted along with potential solutions.

In recent years, the IoT has emerged as the most promising technology in the key evolution of industry 4.0/industry 5.0, smart home automation (SHA), smart cities, energy savings and many other areas of wireless communication. There is a massively growing number of static and mobile IoT devices with a diversified range of speed and bandwidth, along with a growing demand for high data rates, which makes the network denser and more complicated. In this context, the next-generation communication technology, i.e., sixth generation (6G), is trying to build up the base to meet the imperative need of future network deployment. This article adopts the vision for 6G IoT systems and proposes an IoT-based real-time location monitoring system using Bluetooth Low Energy (BLE) for underground communication applications. An application-based analysis of industrial positioning systems is also presented.

Augmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

Holographic-type communication (HTC) permits new levels of engagement between remote users. It is anticipated that it will give a very immersive experience while enhancing the sense of spatial co-presence. In addition to the newly revealed advantages, however, stringent system requirements are imposed, such as multi-sensory and multi-dimensional data capture and reproduction, ultra-lightweight processing, ultra-low-latency transmission, realistic avatar embodiment conveying gestures and facial expressions, support for an arbitrary number of participants, etc. In this paper, we review the current limitations to the HTC system implementation and systemize the main challenges into a few major groups. Furthermore, we propose a conceptual framework for the realization of an HTC system that will guarantee the desired low-latency transmission, lightweight processing, and ease of scalability, all accompanied with a higher level of realism in human body appearance and dynamics.