Metamaterials are artificial structures with the ability of exhibiting unusual and exotic electromagnetic properties such as the realisation of negative permittivity and permeability. Due to their unique characteristics, metamaterials have drawn broad interest and are considered to be a promising solution for improving the performance and overcoming the limitations of microwave components and especially antennas. This paper presents a detailed review of the most recent advancements associated with the design of metamaterial-based antennas. A brief introduction to the theory of metamaterials is provided in order to gain an insight into their working principle. Furthermore, the current state-of-the-art regarding antenna miniaturisation, gain and isolation enhancement with metamaterials is investigated. Emphasis is primarily placed on practical metamaterial antenna applications that outperform conventional methods and are anticipated to play an active role in future wireless communications. The paper also presents and discusses various design challenges that demand further research and development efforts.

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multiple-input multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.

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.

Massive MIMO will improve the performance of future 5G systems in terms of data rate and spectral efficiency, while accommodating a large number of users. Furthermore, it allows for 3D beamforming in order to provide more degrees of freedom and increase the number of high-throughput users. Massive MIMO is expected to provide more advantages compared to other solutions in terms of energy and spectral efficiency. This will be achieved by focusing the radiation towards the direction of the intended users, thus implementing simultaneous transmission to many users while keeping interference low. It can boost the capacity compared to a conventional antenna solution, resulting in a spectral efficiency up to 50 times greater than that provided by actual 4G technology. However, to take full advantage of this technology and to overcome the challenges of implementation in a real environment, a complicated radio system is required. The purpose of this work is to present the MIMO technology evolution and challenges in a simple introductory way and investigate potential system enhancements.

Nonline-of-sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the ultra-wideband (UWB) indoor positioning system (IPS). Numerous supervised machine learning (ML) approaches have been applied for the NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of line-of-sight (LoS) signals. The inaccurate localization of the target node caused by this small number of NLoS signals can still be problematic. To solve this issue, we propose feature-based Gaussian distribution (GD) and generalized GD (GGD) NLoS detection algorithms. By employing our detection algorithm for the imbalanced dataset, a classification accuracy of 96.7% and 98.0% can be achieved. We also compared the proposed algorithm with the existing cutting edge, such as support vector machine (SVM), decision tree (DT), naïve Bayes (NB), and neural network (NN), which can achieve an accuracy of 92.6%, 92.8%, 93.2%, and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset. Finally, the proposed algorithm can also achieve a higher classification accuracy for different ratios of LoS and NLoS signals, which proves the robustness and effectiveness of the proposed method.