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Summary: Irrigation drives water consumption in agriculture, essential for crop productivity and conserving water resources. Smart irrigation optimizes water usage, enhances yields, and supports sustainability. Integrating Information and Communication Technologies (ICT) and the Internet of Things (IoT) shows potential for reducing water consumption, but hardware, software, and connectivity solutions are still developing. Low-Power Wide-Area Access (LPWA) technologies enable energy-efficient IoT applications with long-range communication. LoRaWAN ensures secure data transmission up to 15 km using a single gateway. This paper presents a LoRaWAN-based prototype irrigation platform developed within the AREThOU5A framework, implemented in a 22-hectare olive grove with Kalamon olives in Greece. The system monitors soil moisture levels using IoT end nodes with low-cost microcontrollers and embedded sensors. Each node measures moisture at depths of 30 cm, 60 cm, and 90 cm, operating on battery power for months and communicating via LoRaWAN. Data is transmitted through a LoRaWAN gateway to a cloud-based infrastructure using 4G connectivity, allowing real-time access via online dashboards. Collected data will inform smart irrigation strategies, maximizing water savings and ensuring reliable connectivity. A robust software development system has integrated IoT subsystems for olive grove management, centralizing measurement data in a web-accessible database. This enables real-time monitoring, trend analysis, and cultivation optimization. The system is hosted on a public cloud platform, using Docker for flexible deployment and data management. In the olive grove in Peta, a specialized InfluxDB routine optimizes water use, operating effectively for two years. An annual dashboard highlights key data points, including soil moisture, temperature readings, and health diagnostics. Water consumption was reduced by 42 % in 2020 and 25 % in 2021. An identification application secures user data management and supports sustainability.

The main objective of this work is to improve and evaluate the effectiveness of the neural network (NN) architecture in the domain of estimation of direction of arrival (DOA), with an emphasis on a multi-class classification task with grid resolutions of 0.25 and 0.1. Specifically, a comprehensive assessment is performed to determine the competence of a residual NN (ResNet) in predicting the angle of arrival (AOA) of intercepted signals. Such signals are received by a 16-element uniform linear array and are subjected to real-world noise conditions. To this end, the superiority of the ResNet architecture in DOA estimations is substantiated through a comparison analysis with two other highly recognized NNs, namely, the feed-forward NN and the convolutional NN. Numerical results indicate that the ResNet model exhibits notable precision in estimating the AOAs, across various classes within a broad spectrum, along with a rapid temporal response. Finally, it remains consistent and maintains its superior performance even for diverse incoming signals and significantly reduced SNRs.

Multiple-input multiple-output (MIMO) and reconfigurable intelligent surfaces (RIS) enable network system designers to create intelligent, energy-efficient network systems. Higher-order beamforming gain through the deployment of multiple RISs ensures enhanced system performance. To achieve this, a cooperative design of passive beamforming is required. This paper presents RIS passive beamforming design for a double RIS-assisted multi-user multiple-input multiple-output (muMIMO) system. The authors explore machine learning (ML)-based techniques, specifically fully complex-multilayer perception (FC-MLP) and extreme learning machine (ELM), that do not require any prior knowledge of the mathematical formulations of the network system, for RIS passive beamforming design frameworks. The proposed ML-based frameworks are compared to the alternating optimization (AO) as well as to the deep deterministic policy gradient (DDPG)-based techniques in terms of average spectral efficiency (SE), as a function of transmit power, their configurations, RISs’ locations and number of RISs’ cells, as well as their computational complexities. The proposed ELM framework outperforms the AO and FC-MLP methods, and demonstrates descent performance when compared to the DDPG method.

In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at 2.4, 3.5, 5, and 5.8 GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is 0.511 kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies.

Extreme learning machine (ELM) uses a simple machine learning (ML) architecture that allows its implementation for smart IoT network operations. However, some inaccuracy lies between the predicted and the actual data during the ELM training, which can be caused due to the limitation of the modeling representation. This paper thus investigates a residual compensation-based ELM (RC-ELM) for its application in designing a receiver for MIMO-NOMA aided IoT systems. In RC-ELM, the base ELM layers determine the relationship between the transmitted and received data and the additional layers of the RC-ELM attempt to compensate the error introduced during the training mechanism. The analysis of the appropriate number of compensation layers for training error minimization is conducted on the basis of the bit error rate (BER) and the error vector magnitude (EVM) performances of the RC-ELM training. A minimum BER improvement of 5% for user 1 and 18% for user 2 is shown with with the aid of RC-ELM for a two-IoT user instance. The EVM is marginally increased by 0.0008% for user 1 and 1.61% for user 2 in training stage. Besides, the RC-ELM receiver is also compared to the minimum mean square error (MMSE), the classic ELM and the trained multilayer perceptron (MLP) receivers in terms of BER and EVM. Both of the ELM receivers show improved performances with respect to the other receivers.

The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments of Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

In this work, we talk about the problem of joint power allocation and user association based on quality-of-service for non-orthogonal multiple access (NOMA) to downlink networks. The problem is especially difficult due to its non-convex form and the large number of optimization variables, which are solved using two different nature-inspired algorithms with low complexity. We investigate the effect of different network parameters on increasing users. Numerical results show that, for a growing number of users, the problem is becoming increasingly difficult, which indicates the increasing network resources required to solve it. The results of the simulations show that using evolutionary algorithms is a fast and effective way to solve this kind of problem. Moreover, the NOMA advantage over OMA becomes clear as the number of users increases. Evolutionary techniques outperform randomly generated solutions, as expected.

Massive multiple-input multiple-output (mMIMO), assisted by reconfigurable intelligent surface (RIS), can ensure reliable and energy-efficient data transmission. However, the receiver design for large-scale networks based on traditional mathematical approaches requires complex statistics. Therefore, in this paper, machine learning (ML) approaches are investigated to design receivers for the RIS-assisted multi-user MIMO (muMIMO) systems to avoid complicated channel information requirements. Extreme learning machine (ELM) is an effective ML tool for MIMO receiver design because it simplifies the learning process. However, the learning performance of the ELM can get affected by the random choice of its hidden layer size. To address this issues, this paper proposes an incremental ELM (I-ELM) based receiver for the RIS-mu-MIMO system. The proposed receiver computes the weights between the hidden and the output layer based on the automated incremental addition of hidden neurons and provided conditions. The suggested receiver is contrasted with the multilayer perceptron (MLP), conventional ELM, and minimum mean square error (MMSE) receivers. The simulation results show that the throughput performance of the proposed receiver is satisfactory.

Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots. We address this by reformulating source detection as a multi-class classification problem solved using deep learning frameworks. Incoming waveforms are sampled using a centro-symmetric linear array with omni-directional elements and the normalized upper triangle of the autocorrelation matrix is extracted as the input feature to a modified convolutional neural network with uni-dimensional filters, trained to detect the sources in the presence of both uncorrelated and correlated signals. Two detection algorithms are introduced and referred to as CNNDetector and RadioNet, and subsequently benchmarked against the conventional source detection algorithms. By including pre-processing in forward backward spatial smoothing, RadioNet can also resolve the number of uncorrelated sources in the presence of correlated paths. Finally, the algorithms are stress tested under challenging operational conditions and extensive evaluations are presented showing the efficacy and contributions of the introduced predictive models. To the best of our knowledge, this is the first time the source detection problem has resolved L-1 sources, for an antenna array of L elements using a deep learning framework.

Indoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor location-aware IoT services. Additionally, artificial intelligence (AI) has attracted considerable research effort to address the challenges in visible-light communication (VLC) systems. This is an emerging technology in next-generation wireless networks. However, despite the rapid progress, the use of AI in localization, navigation, and position estimation is still underexplored in VLC systems, and various research challenges are still open. This methodological review summarizes the research efforts regarding the use of AI in the field of VLP, to improve the position estimation accuracy in both two-dimensional (2D) and three-dimensional (3D) indoor IoT applications. This treatise also presents open issues and potential future directions for motivating further research in the field. Various databases were utilized in this paper: Scopus, Google Scholar, and IEEE Xplore; obtained 88 papers from 2017 to early 2023. Most (68%) of the AI articles in VLP systems are machine learning (ML) methods applied for localization and position estimation in VLC systems, while the other 32% of the research articles focussed on evolutionary algorithms. ML and evolutionary models may present limitations in terms of complexity and time-consuming nature but offer highly accurate, robust, reliable, and cost-effective results in terms of position estimation over conventional approaches.

Remote sensing stands out as one of the most widely used operations in the field. In this research area, UAVs offer full coverage of large cultivation areas in a few minutes and provide orthomosaic images with valuable information based on multispectral cameras. Especially for orchards, it is helpful to isolate each tree and then calculate the preferred vegetation indices separately. Thus, tree detection and crown extraction is another important research area in the domain of Smart Farming. In this paper, we propose an innovative tree detection method based on machine learning, designed to isolate each individual tree in an orchard. First, we evaluate the effectiveness of Detectron2 and YOLOv8 object detection algorithms in identifying individual trees and generating corresponding masks. Both algorithms yield satisfactory results in cherry tree detection, with the best F1-Score up to 94.85%. In the second stage, we apply a method based on OTSU thresholding to improve the provided masks and precisely cover the crowns of the detected trees. The proposed method achieves 85.30% on IoU while Detectron2 gives 79.83% and YOLOv8 has 75.36%. Our work uses cherry trees, but it is easy to apply to any other tree species. We believe that our approach will be a key factor in enabling health monitoring for each individual tree.

Abstract The fifth generation (5G) of telecommunications networks is currently commercially deployed. One of their core enabling technologies is cellular Massive Multiple-Input-Multiple-Output (M-MIMO) systems. However, future wireless networks are expected to serve a very large number of devices and the current MIMO networks are not scalable, highlighting the need for novel solutions. At this moment, Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most promising idea in this direction. Despite their appealing characteristics, CF M-MIMO systems face their own challenges, such as power allocation and channel estimation. Deep Learning (DL) has been successfully employed to a wide range of problems in many different research areas, including wireless communications. In this paper, a review of the state-of-the-art DL methods applied to CF M-MIMO communications systems is provided. In addition, the basic characteristics of Cell-free networks are introduced, along with the presentation of the most commonly used DL models. Finally, future research directions are highlighted.

Chaos-related applications are abundant in the literature, and span the fields of secure communications, encryption, optimization, and surveillance. Such applications take advantage of the unpredictability of chaotic systems as an alternative to using true random processes. The chaotic systems used, though, must showcase the statistical characteristics suitable for each application. This may often be hard to achieve, as the design of maps with tunable statistical properties is not a trivial task. Motivated by this, the present study explores the task of constructing maps, where the statistical measures like the mean value can be appropriately controlled by tuning the map’s parameters. For this, a family of piecewise maps is considered, with three control parameters that affect the endpoint interpolations. Numerous examples are given, and the maps are studied through a collection of numerical simulations. The maps can indeed achieve a range of values for their statistical mean. Such maps may find extensive use in relevant chaos-based applications. To showcase this, the problem of chaotic path surveillance is considered as a potential application of the designed maps. Here, an autonomous agent follows a predefined trajectory but maneuvers around it in order to imbue unpredictability to potential hostile observers. The trajectory inherits the randomness of the chaotic map used as a seed, which results in chaotic motion patterns. Simulations are performed for the designed strategy.

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.

The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

The purpose of this study is to examine the impact of educational software characteristics on software performance through the mediating role of user acceptance. Our approach allows for a deeper understanding of the factors that contribute to the effectiveness of educational software by bridging the fields of educational technology, psychology, and human–computer interaction, offering a holistic perspective on software adoption and performance. This study is based on a sample collected from public and private education institutes in Northern Greece and on data obtained from 236 users. The statistical method employed is structural equation models (SEMs), via SPSS—AMOS estimation. The findings of this study suggest that user acceptance and performance appraisal are exceptionally interrelated in regard to educational applications. The study argues that user acceptance is positively related to the performance of educational software and constitutes the nested epicenter mediating construct in the educational software characteristics. Additional findings, such as computer-familiar users and users from the field of choral music, are positively related to the performance of the educational software. Our conclusions help in understanding the psychological and behavioral aspects of technology adoption in the educational setting. Findings are discussed in terms of their practical usefulness in education and further research.

The exigency for continuous use of electrical devices has created greater demands for electricity along with more efficient transmission techniques. Energy from natural resources can be solar, thermal, vibration, friction, or Radio Frequencies (RF) signals. This state-of-the-art work provides a summary of RF energy harvesting techniques and can be used as a guide for the manufacture of RF energy scavenging modules. The use of Radio Frequency (RF) Energy Harvesting (EH) technique contributes to the development of autonomous energy devices and sensors. A rectenna system includes three main units: the receiving antenna, the impedance matching network, and the rectifier. We thoroughly analyze how to design a rectenna system with special emphasis given on the design of the rectifier. At the same time many works of the last 10 years are presented. This review article categorizes the used topologies depending on the type of antennas, IMNs, and rectifiers and comparatively presents their advantages and disadvantages.

Radio Frequency (RF) energy harvesting has been emerged as a potentially reliable method to replace the costly and difficult to maintain source of low-power wireless sensor networks. A plethora of dual-band rectifier designs has been proposed in the literature operating in various frequency bands. In this paper, a triple-band RF-to-DC rectifier that operates in the frequency bands of LoRaWAN, GSM-900, and WiFi 2.4 GHz is presented. The system is composed of an impedance-matching circuit, an RF-to-DC rectifier, that converts the ambient RF energy into DC voltage able to feed low-power devices, and an output load. The proposed system resonates at three different frequencies of 866 MHz, 948 MHz and 2423 MHz, which fall within the aforementioned frequency bands of interest. The feasible solution of the proposed system was based on a dual-band rectifier operating in the frequency bands of LoRaWAN and GSM-900. A series of shunt stubs was utilized in the initial design to form the feasible solution of the proposed system. The proposed triple-band rectifier was optimized using a powerful optimization algorithm, i.e., the genetic algorithm. The overall system exhibited improved characteristics compared to the initial design in terms of its resonance. Numerical results demonstrated that the overall system exhibited an efficiency of 81% with 3.23 V of the output voltage, for an input power of 0 dBm and a load of 13 kOhm.

The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.

The design of a smart electromagnetic (EM) environment for next-generation wireless communication systems is addressed in this work. The proposed approach aims at synthesizing a desired EM field distribution over a target region, where the receiving terminals are located, through the opportunistic exploitation of the complex scattering interactions between the EM field generated by a reconfigurable primary source and the objects/scatterers present in the environment, which behave as application-driven passive metastructures. The effectiveness and the potentialities of the proposed design methodology are assessed with a proof-of-concept numerical result obtained by means of advanced and reliable simulation tools.

The Byzantine religious tradition includes Greek Orthodox Church hymns, which significantly differ from other cultures’ religious music. Since the deep learning revolution, audio and music signal processing are often approached as computer vision problems. This work trains from scratch three different novel convolutional neural networks on a hymns dataset to perform hymns classification for mobile applications. The audio data are first transformed into Mel-spectrograms and then fed as input to the model. To study in more detail our models’ performance, two state-of-the-art (SOTA) deep learning models were trained on the same dataset. Our approach outperforms the SOTA models both in terms of accuracy and their characteristics. Additional statistical analysis was conducted to validate the results obtained.

Tabular data and images have been used from machine learning models as two diverse types of inputs, in order to perform path loss predictions in urban areas. Different types of models are applied on these distinct modes of input information. The work at hand tries to incorporate both modes of input data within a single prediction model. It therefore manipulates and transforms the vectors of tabular data into images. Each feature of the tabular data vector is spread into several pixels, corresponding to the calculated importance of the particular feature. The resulting synthetic images are then fused with images representing selected regions of the area's map. Compound pseudoimages, having channels of both map-based and tabular data-based images, are then being used as inputs for a Convolutional Neural Network (CNN), which predicts the path loss value at a specific point of the area of interest. The results are clearly better than those obtained from models based on a single mode of input data, as well as from the results produced by other bimodal-input approaches. This approach could be applied for path loss prediction in urban environments for several state-of-art wireless networks like 5G and Internet of Things (IoT).

Radio frequency energy harvesting has attracted considerable interest as a technique of enabling self-powered wireless networks. This technique faces several challenges, such as the receiving and the rectifying modules of a rectenna system. Multiband antennas provide several comparative advantages against the goal of maximizing the amount of energy harvesting. In this work, we present a multiband microstrip patch antenna with three slits operating in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. A feasible solution of the antenna is obtained by the application of a recently introduced nature-inspired optimization technique, namely the Coyote Optimization Algorithm. The proposed antenna operates satisfactorily in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. Measured results of the proposed antenna exhibit an acceptable performance (multiband frequency operation, maximum gain of 3.94 dBi, broadside operation) and demonstrate features of operation, which make it a strong candidate for various RF energy harvesting applications.

Computational Intelligence (CI) is the theory, design, application and development of biologically and linguistically motivated computational paradigms. Machine Learning (ML), Fuzzy Inference Systems (FIS), and Evolutionary Computation (EC) have historically been the three principal pillars of CI. The use of CI has experienced an increasing impact on solving complex problems in antennas and propagation (AP).

Radio propagation modeling and path loss prediction have been the subject of many machine learning-based estimation attempts. Our current work uses deep learning for the task in question, trying to exploit the potential of applying convolutional neural networks in order to perform predictions based on images. A comparison between data-driven and image-driven estimations has been carried out in order to assess the proposed method. The results show that an appropriately chosen image can, per se, be treated as an alternative to a vector of tabular data and produce reliable predictions. The effect of the image’s size has also been examined.

A challenging problem in millimeter wave (mmWave) communications for the fifth generation of cellular communications and beyond (5G/B5G) is the beam selection problem. This is due to severe attenuation and penetration losses that are inherent in the mmWave band. Thus, the beam selection problem for mmWave links in a vehicular scenario can be solved as an exhaustive search among all candidate beam pairs. However, this approach cannot be assuredly completed within short contact times. On the other hand, machine learning (ML) has the potential to significantly advance 5G/B5G technology, as evidenced by the growing complexity of constructing cellular networks. In this work, we perform a comparative study of using different ML methods to solve the beam selection problem. We use a common dataset for this scenario found in the literature. We increase the accuracy of these results by approximately 30%. Moreover, we extend the given dataset by producing additional synthetic data. We apply ensemble learning techniques and obtain results with about 94% accuracy. The novelty of our work lies in the fact that we improve the existing dataset by adding more synthetic data and by designing a custom ensemble learning method for the problem at hand.

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.

Radio-frequency (RF) energy harvesting (EH) is emerging as a reliable and constantly available free energy source. The primary factor determining whether this energy can be utilized is how efficiently it can be collected. In this work, an RF EH system is presented. More particularly, we designed a dual-band RF to DC rectifier circuit at sub-6 GHz in the 5G bands, able to supply low-power sensors and microcontrollers used in agriculture, the military, or health services. The system operates at 3.5 GHz and 5 GHz in the 5G cellular network’s frequency band FR1. Numerical results reveal that the system provides maximum power conversion efficiency (PCE) equal to 53% when the output load (sensor or microcontroller) is 1.74 kΩ and the input power is 12 dBm.

Dense small cell deployment in a cloud radio access network (C-RAN) is an effective approach to satisfy the increasing requirements of the emerging fifth generation (5G) mobile networks. Nevertheless, the optimal deployment of small cells is not always possible due to certain limitations, such as the existence of backbone infrastructure and appropriate power supply. In addition, the increased throughput requirements in a certain area may be temporal, such as in the case of an event such as a sports match or a concert. To this end, the leverage of multiple drone-mounted remote radio heads (D-RRHs), coupled with advanced user scheduling and multiplexing schemes, can be a promising asset in provisioning an on-demand and dynamic C-RAN. Nevertheless, finding the optimal placement of the D-RRHs, under certain quality of service (QoS) constraints can be challenging. In this work, we propose a D-RRH placement approach that minimizes the transmission power by optimally placing the D-RRH. We formulate the placement optimization problem and decouple it into two separate sub-problems, namely the horizontal placement and the vertical placement problems. An implementation of the Weiszfeld algorithm is utilized in order to solve the horizontal placement problem by finding the point that minimizes the sum distances. The vertical placement problem is calculated as a function of the optimal elevation angle and the radius of the coverage area. In order to evaluate the performance of the proposed approach, we carried out extensive evaluations and compare our proposed approach against two similar approaches. The evaluation results show the feasibility of the proposed approach in minimizing the required transmission power and maintaining fairly good performance in terms of user connectivity.

The design of a massive MIMO network requires a channel model that captures the Spatio-temporal dimensions of the propagation environment. In this paper, we propose a novel method combining Hybrid Raytracing - Finite difference time domain (FDTD) and network planner tools to address this requirement. This method provides accurate and realistic EMF exposure models for the design of a massive MIMO network. Using this method, we proceed with the optimization of the BS's locations under the low power consumption and low EMF exposure constraints. Assuming equal preference of the optimization objectives, the simulations show that the uplink localized 10g dose appears to be the dominant factor of the localized 10g EMF exposure. Moreover, a massive MIMO network designed to serve 224 simultaneous active users at the same time-frequency resource is subject to an increase of the total whole-body dose (2 times higher in downlink and +18% in uplink), compared to a design with 14 active users. However, in the same conditions, the downlink localized 10g dose reduces (20 times lower) whereas the uplink localized 10g dose increases (+23%) in comparison with the scenario with fewer users (14). Besides, the electromagnetic field strength in all locations obtained with this new method is 2 times weaker compared to a 4G LTE network, while complying with the international guidelines.

The new expected fifth generation (5G) communication systems will serve users with data rates of several Gbps and will allow running new applications in mobile devices. In order to do so, several design challenges have to be met. One of these is the antenna design and fabrication for the 5G mobile devices, especially in the millimeter wave frequencies. In this paper, we address the above issue by designing two different antennas for 3.7 and 26 GHz. We apply a design procedure based on a new proposed evolutionary algorithm, the Grey Wolf Optimizer-Jaya (GWO-Jaya), which combines features from both GWO and Jaya. We evaluate the new algorithm on a set of widely exploited benchmark functions. We design both antennas using the new proposed algorithm. Moreover, we evaluate their performance characteristics through fabrication and measurements. Both antennas are circularly polarized at an acceptable bandwidth and also exhibit wide-band behavior. In both cases, simulation and measurement results are in good agreement.

Our main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user for 4th Generation (4G) and 5th Generation (5G) Massive MIMO network configurations. Moreover, we present and address the problem of designing green LTE networks with the Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. In this context, we apply emerging evolutionary algorithms in the context of green network design. We investigate three different cases to show the performance of the new proposed algorithm, namely the 4G, 5G Massive MIMO, and the NB-IoT technologies. More specifically, we investigate the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, the self-adaptive differential evolution jDE algorithm, and other hybrid algorithms. We introduce a new hybrid algorithm named Jaya-jDE that uses concepts from both Jaya and jDE algorithms in an effective way. The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. Moreover, Jaya-jDE emerges as the best algorithm based on the results.

Cognitive radio networks are a promising technology for the improvement of the spectrum utilisation. The basic idea is to maximise the utilisation of the available spectrum by dynamically assigning available channels to secondary users. This problem known as the spectrum allocation problem is non‐deterministic polynomial‐time hard (NP‐hard). Chaotic biogeography‐based optimisation (CBBO) is a recently proposed evolutionary algorithm that can be applied to the above‐mentioned problem. The authors compare CBBO with other popular algorithms in different spectrum allocation problem cases. The results show that CBBO performs in general better or similar to the other algorithms.

Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies.

Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands.

Frequency Selective Surfaces (FSSs) have become increasingly popular during the last years due to their combined characteristics, which meet, in general, the requirements of the next-generation wireless communication networks. In this work, a cross-platform design framework for FSS structures is presented and evaluated by utilizing a recently introduced evolutionary optimization algorithm, namely, the Multi-Variant Differential Evolution (MVDE). To the best of the authors knowledge, this is the first time that the MVDE algorithm is applied to a design problem in Electromagnetics. The proposed design framework is described in detail and the utilized evolutionary algorithm is assessed in terms of its performance by applying several benchmark functions. In this context, the MVDE is comparatively evaluated against other popular evolutionary algorithms. Moreover, it is applied to the design and optimization of two different representative examples of FSS structures based on three use cases of unit cell geometry. Optimization results indicate the efficacy of the proposed framework by quantifying the performance of the designed FSS structures in terms of several system metrics. The optimized FSS structures exhibit dual-band operation and quite acceptable results in the ISM frequency bands of 2.45 GHz and 5.8 GHz.

Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.

A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shaped patch antenna is used. The receiving module (antenna) of the rectenna system is optimized in terms of its reflection coefficient to match the RF-to-DC rectifier. The final geometry of the proposed antenna is derived by the application of the Moth Search Algorithm and a commercial electromagnetic solver. The impedance matching network of the proposed system is obtained based on a three-step process, including the minimization of the reflection coefficient versus frequency, as well as the minimization of the reflection coefficient variations and the maximization of the DC output voltage versus RF input power. The proposed RF-to-DC rectifier is designed based on the Greinacher topology. The designed rectenna is fabricated on a single layer of FR-4 substrate. Measured results show that our proposed rectenna can harvest RF energy from outdoor (ambient and dedicated) sources with an efficiency of greater than 52%.

In body-centric communications, energy efficiency is a critical performance metric, while the achievable data rate is not of primary concern. In this paper we present a novel modulation scheme, which can be efficiently used in body-centric terahertz (THz) nanonetworks. The proposed scheme is a combination of the time-spread On-Off keying (TS–OOK) and the pulse position modulation (PPM) and presents lower energy consumption, compared to other existing methods as TS–OOK, at a minor cost to the data rate. Furthermore, another important aspect is that the proposed modulation scheme can be effectively used to mitigate the impact of the specific kind of noise in THz body-centric communications, thus leading to better error performance. Finally, we present analytical and simulation results in order to compare the new scheme with the existing TS–OOK.

A review of evolutionary algorithms (EAs) with applications to antenna and propagation problems is presented. EAs have emerged as viable candidates for global optimization problems and have been attracting the attention of the research community interested in solving real-world engineering problems, as evidenced by the fact that very large number of antenna design problems have been addressed in the literature in recent years by using EAs. In this paper, our primary focus is on Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Differential Evolution (DE), though we also briefly review other recently introduced nature-inspired algorithms. An overview of case examples optimized by each family of algorithms is included in the paper.

Array thinning is a common discrete-valued combinatorial optimization problem. Evolutionary algorithms are suitable techniques for above-mentioned problem. Biogeography-Based Optimization (BBO), which is inspired by the science of biogeography, is a stochastic population-based evolutionary algorithm (EA). The original BBO uses a linear migration model to describe how species migrate from one island to another. Other nonlinear migration models have been proposed in the literature. In this paper, we apply BBO with four different migration models to five different large array design cases. Additionally we compare results with five other popular algorithms. The problems dimensions range from 150 to 300. The results show that BBO with sinusoidal migration model generally performs better than the other algorithms. However, these results are considered to be indicative and do not generally apply to all optimization problems in antenna design.

This paper addresses the problem of designing shaped beam patterns with arbitrary arrays subject to constraints. The constraints could include the sidelobe level suppression in specified angular intervals, the mainlobe halfpower beamwidth, and the predefined number of elements. In this paper, we propose a new Differential Evolution algorithm, which combines Composite DE with an eigenvector-based crossover operator (CODE-EIG). This operator utilizes eigenvectors of covariance matrix of individual solutions, which makes the crossover rotationally invariant. We apply this novel design method to shaped beam pattern synthesis for linear and conformal arrays. We compare this algorithm with other popular algorithms and DE variants. The results show CODE-EIG outperforms the other DE algorithms in terms of statistical results and convergence speed.