Editor’s Choice Articles

The intense diversity of the Next-Generation Networking environments like 6G and the forthcoming deployment of immersive applications with varied user-specific requirements transform the efficient allocation of resources into a real challenge. Traditional solutions like the shortest path algorithm and mono-constraint methodologies are inadequate to handle customized user-defined performance parameters and effectively classify physical resources according to these intricate demands. This research offers a new evaluation mechanism to successfully replace the aforementioned traditional path ranking and path selection techniques. Specifically, the proposed framework is integrated with optimization-oriented metrics, each indicating a unique aspect of performance for evaluating candidate network paths. The deployed metrics are then algebraically synthesized to provide a distinctive multidimensional description of the examined substrate resources. These primary and composite metrics adhere to the fundamental monotonicity and isotonicity properties of a Path Algebra; hence, the validity and optimality of the proposed evaluation mechanism is guaranteed by design. To tackle the complexity created by the variety of human-centric customization, a novel methodology that analyzes and determines the weighted influence of the synthesized metrics depending on the characteristics of the served user-centric application is also introduced. The chosen suitable weights address performance-oriented mission-critical tailored objectives for adaptive optimizations. Its innovative algebraic design allows it to successfully describe and rank candidate paths in a versatile way, whether in legacy or modern architectures. The experimental data of the first scenario show that 62.5% and 50% of highlighted path evaluations proposed by the shortest path and unidimensional constraint strategies, respectively, suffer from moderate performance-oriented values compared to the proposed framework. Likewise, the results of the second examined scenario reveal that the proposed composite metric yields more suitable path rankings by 50% in contrast to its traditional counterparts, rendering the contested evaluation mechanisms obsolete. Full article

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field. Full article

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASSs). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML)-based algorithms, and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCCs) by using real-time data to optimise their operations, enhance safety, and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity vulnerabilities must be considered and properly addressed to prevent such complex systems from being tampered with. This paper proposes an approach that operates on two different levels to address cybersecurity. On one side, our solution is intended to secure communication channels between the SCCs and the vessels using Secure Exchange and COMmunication (SECOM) standard; on the other side, it aims to secure the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly and intrusion detection. The proposed approach is validated against a real implementation of the SCC deployed in the Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly. Full article

Massive MIMO deployments have been traditionally based on dedicated links in the front-haul, i.e., between the central processing units and the Access Points (APs). Recently, cell-free massive Multiple-Input Multiple-Output (MIMO) systems based on serial front-haul links have been discussed to simplify the deployments, among other reasons. However, the power consumption models currently used for cell-free massive MIMO deployments typically assume dedicated front-haul links. This paper highlights the inaccuracy of these models when applied to serial front-hauls and proposes simple adaptations to achieve more realistic results. The results obtained for an exemplary scenario show that the front-haul power would represent 61.73% of the total consumed power with the original models. In contrast, with the proposed adaptations, it could be as low as 1.59% of the total consumed power for some serial front-haul configurations. Additionally, the impact of considering APs with lower power consumption is studied, in which case, the percentages above would become 93.15% and 11.96%, respectively. Hence the importance of having power models that fit the front-haul topology. Full article

Telecommunications profoundly impacts all major aspects of our everyday life. As a consequence, student instruction typically includes a series of specialized courses, each addressing a distinct telecommunication area, separating wireless from fixed (optical) communications. This creates the problem of knowledge fragmentation, hindering the student’s perception of the topic since, at the service level, the applications and services offered to the users seem “virtually” independent from the underlying infrastructure. In this paper, to address this problem, we designed, analyzed, and implemented a 6 h course module on the five generations of wireless and fixed networks, which was presented as an integral part of the undergraduate course “Broadband Communications”, which was offered at the Dept. of Electrical and Electronic Engineering, School of Pedagogical and Technological Education (ASPETE), Athens, Greece. The main targets of this module are the following. Firstly, it aims to familiarize students with the fixed generations taxonomy, defined by the ETSI Industry Specification Group (ISG) F5G. This taxonomy serves as a foundation for understanding the evolution of telecommunications technologies. Secondly, the module seeks to integrate the acquired knowledge of the students in their previous telecommunication-related courses. During their curriculum, this knowledge was divided into two separate parts: wireless and fixed (optical). By coupling these two areas, students can develop a deeper understanding of the field. Lastly, the module aims to explore cutting-edge technologies and advancements in the telecommunications industry. In this way, it prepares students to enter the professional world during the fifth-generation era. Additionally, it provides them with valuable insights into the ongoing research and development in the field of 6G. Overall, this module serves as a comprehensive platform for students to enhance their understanding of telecommunications, from the foundational concepts to the latest advancements. To evaluate the impact of this module, the students were asked to fill out a questionnaire that included seven questions upon module completion. This questionnaire was completed successfully by 32 students in the previous academic year and by 16 students in this academic year. Moreover, a 20-question multiple choice quiz was offered to the students, allowing us to probe more into the typical errors and misconceptions about the topic. Full article

The data used during training in any given application space are directly tied to the performance of the system once deployed. While there are many other factors that are attributed to producing high-performance models based on the Neural Scaling Law within Machine Learning, there is no doubt that the data used to train a system provide the foundation from which to build. One of the underlying heuristics used within the Machine Learning space is that having more data leads to better models, but there is no easy answer to the question, “How much data is needed to achieve the desired level of performance?” This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how many training data are required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is to determine an approach that requires the lowest amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. By focusing on forecasting the performance of the model rather than the loss value, this approach allows for a greater intuitive understanding of data volume requirements. While this approach will require an initial dataset, the goal is to allow for the initial data collection to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain. Full article

Revolutionary advances in technology have been seen in many industries, with the IIoT being a prime example. The IIoT creates a network of interconnected devices, allowing smooth communication and interoperability in industrial settings. This not only boosts efficiency, productivity, and safety but also provides transformative solutions for various sectors. This research looks into open-source IIoT and edge platforms that are applicable to a range of applications with the aim of finding and developing high-potential solutions. It highlights the effect of open-source IIoT and edge computing platforms on traditional IIoT applications, showing how these platforms make development and deployment processes easier. Popular open-source IIoT platforms include DeviceHive and Thingsboard, while EdgeX Foundry is a key platform for edge computing, allowing IIoT applications to be deployed closer to data sources, thus reducing latency and conserving bandwidth. This study seeks to identify potential future domains for the implementation of IIoT solutions using these open-source platforms. Additionally, each sector is evaluated based on various criteria, such as development requirement analyses, market demand projections, the examination of leading companies and emerging startups in each domain, and the application of the International Patent Classification (IPC) scheme for in-depth sector analysis. Full article

Carrier aggregation (CA) was introduced in mobile communication systems in response to the demand for higher network capacity. CA was conceived as a technique to achieve higher data rates by aggregating multiple blocks of spectrum from the same or different frequency bands. This work explores a different point of view, where CA is employed not as a way to increase capacity through using more bandwidth, but as a diversity technique in order to increase the spectral efficiency of the existing spectrum, and therefore, achieve higher capacity without needing additional spectrum. A mathematical model and set of closed-form expressions are provided, which can be used to characterise the performance of CA as a diversity technique (in terms of both ergodic capacity and secrecy capacity) and determine the impact of various relevant configuration parameters. The numerical results obtained by evaluating the mathematical expressions derived in this work are in line with our previous simulation studies and demonstrate that CA can be effectively exploited as a diversity technique to improve the capacity and performance of mobile communication systems compared to the case of single-carrier transmission over the same amount of bandwidth. Full article

This paper considers the transceiver design in a multiuser multiple-input multiple-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay downlink communication system, where users simultaneously transmit data via an FD relay node. The design incorporates an imperfect loop interference (LI) cancellation which results in a residual LI. Linear precoders are employed at the sources and relay, and minimum mean-squared-error (MMSE) combiners are employed at the destinations to mitigate the effect of the residual LI. The corresponding design problem is highly nonconvex, so a closed-form solution is intractable. Thus, an iterative method is developed to solve this optimization problem. Simulation results are presented which show that the proposed iterative algorithm provides better performance than the corresponding half-duplex (HD) solution in terms of the achievable rate under residual LI. Full article

6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on (i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; (ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments. Full article

As Industry 4.0 networks continue to evolve at a rapid pace, they are becoming increasingly complex and distributed. These networks incorporate a range of technologies that are integrated into smart manufacturing systems, requiring adaptability, security, and resilience. However, managing the complexity of Industry 4.0 networks presents significant challenges, particularly in terms of security and the integration of diverse technologies into a functioning and efficient infrastructure. To address these challenges, emerging digital twin standards are enabling the connection of various systems by linking individual digital twins, creating a system of systems. The objective is to develop a “universal translator” that can interpret inputs from both the real and digital worlds, merging them into a seamless cyber-physical reality. It will be demonstrated how the myriad of technologies and systems in Industry 4.0 networks can be connected through the use of digital twins to create a seamless “system of systems”. This will improve interoperability, resilience, and security in smart manufacturing systems. The paper will also outline the potential benefits and limitations of digital twins in addressing the challenges of Industry 4.0 networks. Full article

5G and beyond mobile networks are envisioned as the fundamental components that drive business and societal transformation. The deterioration of the natural environment and climate change have raised questions regarding the role of the mobile network ecosystem and its potential to accelerate innovations in industrial and societal sustainability. This paper describes the challenges facing 5G/6G mobile networks from sectors essential for the sustainable use of natural resources, which include smart agriculture and forestry, biodiversity monitoring, and water management. Based on recent advancements in the above-mentioned domains, the identification of use cases and their requirements are performed together with the evaluation of current and expected future support provided by 5G and 6G networks. Finally, a list of open issues and challenges to be tackled to enable the implementation of carrier-grade services for these sectors using 5G and 6G platforms is presented. Full article

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors. Full article

Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks. Full article

This paper describes the development and evaluation of an on-body flexible antenna designed for an in-body application, as well as on-body communications at ISM and UWB frequency bands. The evaluation is performed via electromagnetic simulations using the Dassault Simulia CST Studio Suite. A planar tissue layer model, as well as a human voxel model from the human abdominal area, are used to study the antenna characteristics next to human tissues. Power flow analysis is presented to understand the power flow on the body surface as well as within the tissues. Simulation results show that this wearable flexible antenna is suitable for in-body communications in the intestinal area, e.g., for capsule endoscopy, in the industrial, scientific, and medical (ISM) band and at lower ultra-wideband (UWB). At higher frequencies, the antenna is suitable for on-body communications as well as in-body communications with lower propagation depth requirements. Additionally, an antenna prototype has been prepared and the antenna performance is verified with several on-body measurements. The measurement results show a good match with the simulation results. The novelty of the proposed antenna is a compact size and the flexible substrate material, which makes it feasible and practical for several different medical diagnosis and monitoring applications. Full article