Mobile network traffic has seen a significant surge, doubling in the past two years, due to increased adoption of fifth-generation ($\mathbf{5 G)}$ subscriptions. This emphasizes the need for efficient solutions like multicasting to manage escalating data demands. Multicast communications, however, encounter challenges in highly directional transmission scenarios, typical for 5G and sixth-generation (6G) systems. In such scenarios, characterized by the presence of obstacles or signal attenuation affecting one user, the performance degradation propagates across the entire multicast group. While Reconfigurable Intelligent Surfaces (RISs) have shown promise in mitigating blockage issues and optimizing wireless propagation environments, limited research has been done on their application to multicast communication. To address this gap, this study introduces a novel multicast group formation framework designed for highly directional RIS-enhanced multicast communications. The framework utilizes Self-Organizing Map (SOM) to organize close-range users into multicast groups based on the similarity of their azimuth angles relative to the RIS, while either microwave multicasting or mmWave unicasting are considered for users at greater distances. Furthermore, the study provides closed-form expressions for the transition threshold from SOM to either microwave multicasting or mmWave unicasting. Our results (in terms of data rate) demonstrate that the proposed approach with microwave technology outperforms all benchmark schemes and remains stable as the number of users increases, while the mmWave unicast approach exhibits performance similar to pure unicasting.

Digital Twins (DTs), which are paired to Internet of Things (IoT) devices to represent them and augment their capabilities, are gaining ground as a promising technology to enable a wide variety of applications in the sixth-generation (6G) ecosystem, ranging from autonomous driving to extended reality and metaverse. In particular, “social” IoT (SIoT) devices, which are devices capable to establish social relationships with other devices, can be coupled with their virtual counterparts, i.e., social DTS (SDTs), to improve service discovery enabled by browsing the social network of friend devices. However, the mobility of SIoT devices (e.g., smartphones, wearables, vehicular on board units, etc.) may require frequent changes in the corresponding SDT placement in the edge domain to maintain a low latency between the physical device and its digital replica. Triggering SDT relocation at the right time is a critical task, because an incorrect choice could lead to either increased delays or a waste of network resources. This work proposes a learning-powered social-aware orchestration that predicts the mobility of SIoT devices to make more judicious migration decisions and efficiently move the paired SDTs accordingly, while ensuring the minimization of both intra-twin and inter-twin communication latencies. Different machine learning (ML) and deep learning (DL) algorithms are used for SIoT device mobility prediction and compared in terms of a wide set of meaningful metrics in order to identify the model that achieves the best trade-off between prediction accuracy and inference times under different scenarios. Simulation results showcase the improvements of the proposal in terms of reduced network overhead (by up to a factor of 3) and intra-twin and inter-twin communication latency (by up to 10%) compared to a more traditional solution, which activates the relocation of the DTs at fixed time intervals following periodic optimizations.

The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.

According to the 6G vision, the evolution of wireless communication systems will soon lead to the possibility of supporting Tbps communications, as well as satisfying, individually or jointly, a plethora of other very stringent quality requirements related to latency, bitrate, and reliability. The achievement of these goals will naturally raise many research issues within radio communications. In this context, a promising 6G wireless communications enabler is the reconfigurable intelligent surface (RIS) hardware architecture, which has already been recognized as a game-changing way to turn any naturally passive wireless communication setting into an active one. This paper investigates RIS-aided multicast 6G communications by first modeling the system delay as a first-come-first-served (FCFS) M/D/1 queue and analyzing the behavior under different blockage conditions. Then the study of multi-beam operation scenarios, covering multicast and RIS-aided multicast communications, is conducted by leveraging an M/D/c queue model. Achieved results show that large-size RISs outperform even slightly obstructed direct BS-to-user paths. In contrast, RISs of smaller sizes require the design of sophisticated power control and sharing mechanisms to achieve better performance.

The reconfigurable intelligent surface (RIS) adoption has drawn significant attention for the upcoming generation of cellular networks, i.e., 5G New Radio (NR)/6G, as a technology for forming virtual line-of-sight (LoS) links during human blockage or non-line-of-sight (NLoS) transmissions. However, the exploration of RIS placement under realistic conditions of multiple user operations has been limited by 1-2 user scenarios, but still is crucial since RIS deployment affects system performance. This paper addresses the challenge of optimal RIS deployment in 5G NR/6G cellular networks with directional antennas. Specifically, we formulate the RIS deployment problem as a facility location problem that maximizes the total data rate. We then evaluate and analyze the impact of various parameters on RIS-aided communications, such as RIS height, blockers density, number of users, and user distribution. The results confirm that the optimal RIS placement is near the BS for the case of uniform and cluster user distributions with RIS height of more than 5 m and close to the hotspots in the case of the cluster user distribution with RIS height of less than 5 m.

As the fifth-generation (5G) and beyond (5G+/6G) networks move forward, and a wide variety of new advanced Internet of Things (IoT) applications are offered, effective methodologies for discovering time-relevant information, services, and resources are being demanded. To this end, computing-, storage-, and battery-constrained IoT devices are progressively augmented via digital twins (DTs) hosted on edge servers. According to recent research results, a further feature these devices may acquire is social behavior; this latter offers enormous possibilities for fast and trustworthy service discovery, although it requires new orchestration policies of DTs at the network edge. This work addresses the dynamic placement of DTs with social capabilities [social digital twins (SDTs)] at the edge, by providing an optimal solution under IoT device mobility and by accounting for edge network deployment specifics, types of devices, and their social peculiarities. The optimization problem is formulated as a particular case of the quadratic assignment problem (QAP); also, an approximation algorithm is proposed and two relaxation techniques are applied to reduce computation complexity. Results show that the proposed placement policy ensures a latency among SDTs up to 1.4 times lower than the one obtainable with a traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their virtual counterparts. Moreover, the proposed heuristic closely approximates the optimal solution while guaranteeing the lowest computational time.

The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks– directional deafness– which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.

Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearable-technology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices.

Technology is continually undergoing a constituent development caused by the appearance of billions new interconnected “things” and their entrenchment in our daily lives. One of the underlying versatile technologies, namely wearables, is able to capture rich contextual information produced by such devices and use it to deliver a legitimately personalized experience. The main aim of this paper is to shed light on the history of wearable devices and provide a state-of-the-art review on the wearable market. Moreover, the paper provides an extensive and diverse classification of wearables, based on various factors, a discussion on wireless communication technologies, architectures, data processing aspects, and market status, as well as a variety of other actual information on wearable technology. Finally, the survey highlights the critical challenges and existing/future solutions.

In this paper, we address a problem of 3D directional deafness, which may arise for millimeter-wave (mmWave) devices, e.g., in the contention-based access period of the IEEE 802.11ad/ay protocols. To evaluate the probability of 3D deafness, we develop an analytical framework based on stochastic geometry methods. In particular, we study a minimal feasible set of devices equipped with highly directional antennas with an arbitrary antenna pattern and provide an analytical expression for the distance-dependent 3D directional deafness probability.To abstract away from particular antenna patterns, we propose an analytically tractable model of an antenna pattern that is given by a piece-wise linear function of the beamwidth. Using this tractable equation, we derive a corresponding closed-form lower bound for the deafness probability that serves as an approximation for an arbitrary antenna with the same half-power beamwidth. Finally, we study the effects of antenna settings on the deafness probability and derive a scaling law for its lower values.