PAPERS INCLUDED IN THE MOST POPULAR LISTS FOR THE MONTH APRIL 2022

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Good news! According to IEEEXplore, in April 2022, five (5) of our papers have been included in the corresponding journals’ lists of most famous papers. The papers are:

[1] A.-A. A. Boulogeorgos, J. M. Jornet and A. Alexiou, “Directional Terahertz Communication Systems for 6G: Fact Check,” in IEEE Vehicular Technology Magazine, vol. 16, no. 4, pp. 68-77, Dec. 2021, doi: 10.1109/MVT.2021.3113883.

[2] T. A. Tsiftsis, C. Valagiannopoulos, H. Liu, A.-A. A. Boulogeorgos and N. I. Miridakis, “Metasurface-Coated Devices: A New Paradigm for Energy-Efficient and Secure 6G Communications,” in IEEE Vehicular Technology Magazine, vol. 17, no. 1, pp. 27-36, March 2022, doi: 10.1109/MVT.2021.3119282.

[3] A.-A. A. Boulogeorgos, S. E. Trevlakis, S. A. Tegos, V. K. Papanikolaou and G. K. Karagiannidis, “Machine Learning in Nano-Scale Biomedical Engineering,” in IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 7, no. 1, pp. 10-39, March 2021, doi: 10.1109/TMBMC.2020.3035383.

[4] A. -A. A. Boulogeorgos and A. Alexiou, “Coverage Analysis of Reconfigurable Intelligent Surface Assisted THz Wireless Systems,” in IEEE Open Journal of Vehicular Technology, vol. 2, pp. 94-110, 2021, doi: 10.1109/OJVT.2021.3051209.

[5] D. Pliatsios, S. K. Goudos, T. Lagkas, V. Argyriou, A. -A. A. Boulogeorgos and P. Sarigiannidis, “Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches,” in IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 32-47, 2022, doi: 10.1109/OJAP.2021.3133459.

I would like to take this opportunity to thank my co-authors, colleagues, and friends for their contribution, help, and support.

Next, the abstracts and the IEEEXplore and ResearchGate links of the papers are provided:

[1] A.-A. A. Boulogeorgos, J. M. Jornet and A. Alexiou, “Directional Terahertz Communication Systems for 6G: Fact Check,” in IEEE Vehicular Technology Magazine, vol. 16, no. 4, pp. 68-77, Dec. 2021, doi: 10.1109/MVT.2021.3113883.
Abstract:

Sustaining a flexible and ubiquitously available high-data-rate (high-DR) network that is capable of supporting a massive number of end users demands the exploitation of higher-frequency bands, such as the terahertz band (0.1–10 THz). However, the utilization of terahertz wireless systems comes with a number of challenges, many of them associated with the very high propagation losses of terahertz signals, which require the utilization of high-gain directional antennas with strict beam alignment requirements as well as the low signal penetration of (sub)millimeter waves, which leads to intermittent blockage and shadow areas.

In this article, a quantitative discussion of these phenomena and their implications in both backhaul and fronthaul applications of the terahertz spectrum is provided. Starting from state-of-the-art demonstrated terahertz technology parameters, the directivity requirements, impact of beam misalignment, and opportunities for multihop relaying in two different application scenarios are described. For the same conditions, the impact of blockage is quantified, and the benefits of reconfigurable intelligent surfaces (RISs) are studied. Finally, the implications of blockage on the physical-layer security of terahertz systems are presented.

#IEEEXplore link: https://ieeexplore.ieee.org/document/9583918

#ResearchGate link: https://www.researchgate.net/…/354571709_A_Quantitative…

[2] T. A. Tsiftsis, C. Valagiannopoulos, H. Liu, A.-A. A. Boulogeorgos and N. I. Miridakis, “Metasurface-Coated Devices: A New Paradigm for Energy-Efficient and Secure 6G Communications,” in IEEE Vehicular Technology Magazine, vol. 17, no. 1, pp. 27-36, March 2022, doi: 10.1109/MVT.2021.3119282.
Abstract: The 6G era comes with the challenge of offering highly energy-efficient and autonomous communications securely. In this direction, we report energy efficiency (EE), energy harvesting (EH), and secure performance by employing power-collecting metasurface-coated devices capable of supporting ultralow-power (ULP) transmissions. In contrast to what are called reconfigurable intelligent surfaces ( RISs ), where the reflected signal can be combined at the receiver by being treated as transmitted from a relay, the proposed metasurface claddings can be deployed at either the transmitter or receiver or at both. The passive metasurface-coated devices can achieve ultrahigh EE and EH in addition to signal detection, combined with an enhanced secrecy rate at the legitimate user or, if used from the other side of the link, spying capabilities of eavesdroppers under ULP transmission. To quantify their efficiency, we provide a holistic model for the utilization of the metasurface shells. Building upon this model, we present preliminary results that reveal the unprecedented superiority of the proposed concept compared to the RIS paradigm. Additionally, we enumerate the main advantages of the new concept and define its role in the 6G era. Finally, possible research directions are discussed.
#IEEEXplore link: https://ieeexplore.ieee.org/document/9615497
#ResearchGate link: https://www.researchgate.net/…/355046824_Metasurface…

[3] A.-A. A. Boulogeorgos, S. E. Trevlakis, S. A. Tegos, V. K. Papanikolaou and G. K. Karagiannidis, “Machine Learning in Nano-Scale Biomedical Engineering,” in IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 7, no. 1, pp. 10-39, March 2021, doi: 10.1109/TMBMC.2020.3035383.

Abstract: Machine learning (ML) empowers biomedical systems with the capability to optimize their performance through modeling of the available data extremely well, without using strong assumptions about the modeled system. Especially in nano-scale biosystems, where the generated data sets are too vast and complex to mentally parse without computational assist, ML is instrumental in analyzing and extracting new insights, accelerating material and structure discoveries and designing experience as well as supporting nano-scale communications and networks. However, despite these efforts, the use of ML in nano-scale biomedical engineering remains still under-explored in certain areas and research challenges are still open in fields such as structure and material design and simulations, communications and signal processing, and bio-medicine applications. In this article, we review the existing research regarding the use of ML in nano-scale biomedical engineering. In more detail, we first identify and discuss the main challenges that can be formulated as ML problems. These challenges are classified in three main categories: structure and material design and simulation, communications and signal processing and biomedicine applications. Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges. For each of the presented methodologies, special emphasis is given to its principles, applications and limitations. Finally, we conclude the article with insightful discussions, that reveal research gaps and highlight possible future research directions.

#IEEEXplore link: https://ieeexplore.ieee.org/document/9247172

#ReseachGate link: https://www.researchgate.net/…/344764320_Machine…

[4] A. -A. A. Boulogeorgos and A. Alexiou, “Coverage Analysis of Reconfigurable Intelligent Surface Assisted THz Wireless Systems,” in IEEE Open Journal of Vehicular Technology, vol. 2, pp. 94-110, 2021, doi: 10.1109/OJVT.2021.3051209.

Abstract: This paper presents a connectivity analysis of reconfigurable intelligent surface (RIS) assisted terahertz (THz) wireless systems. Specifically, a system model that accommodates the particularities of THz band links as well as the characteristics of the RIS is reported, accompanied by a novel general end-to-end (e2e) channel attenuation formula. Based on this formula, we derive a closed-form expression that returns the optimal phase shifting of each reflection unit (RU) of the RIS. Moreover, we provide a tractable e2e channel coefficient approximation that is suitable for analyzing the RIS-assisted THz wireless system performance. Building upon the aforementioned approximation as well as the assumption that the user equipments are located in random positions within a circular cluster, we present the theoretical framework that quantifies the coverage performance of the system under investigation. In more detail, we deliver a novel closed-form expression for the coverage probability that reveals that there exists a minimum transmission power that guarantees 100% coverage probability. Both the derived channel model as well as the coverage probability are validated through extensive simulations and reveal the importance of taking into account both the THz channel particularities and the RIS characteristics, when assessing the system’s performance and designing RIS-assisted THz wireless systems.

#IEEEXplore link: https://ieeexplore.ieee.org/document/9320587

#ResearchGate link: https://www.researchgate.net/…/348364715_Coverage…

[5] D. Pliatsios, S. K. Goudos, T. Lagkas, V. Argyriou, A. -A. A. Boulogeorgos and P. Sarigiannidis, “Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches,” in IEEE Open Journal of Antennas and Propagation, vol. 3, pp. 32-47, 2022, doi: 10.1109/OJAP.2021.3133459.

Abstract: The emergence of next-generation Internet-of-Things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

#IEEEXplore link: https://ieeexplore.ieee.org/document/9638972

#ResearchGate link: https://www.researchgate.net/…/356863442_Drone-Base…

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