In this review paper, a comprehensive study on the concept, theory, and applications of composite right/left-handed transmission lines (CRLH-TLs) by considering their use in antenna system designs have been provided. It is shown that CRLH-TLs with negative permittivity (ε < 0) and negative permeability (μ < 0) have unique properties that do not occur naturally. Therefore, they are referred to as artificial structures called "metamaterials". These artificial structures include series left-handed (LH) capacitances (CL), shunt LH inductances (LL), series right-handed (RH) inductances (LR), and shunt RH capacitances (CR) that are realized by slots or interdigital capacitors, stubs or via-holes, unwanted current flowing on the surface, and gap distance between the surface and ground-plane, respectively. In the most cases, it is also shown that structures based on CRLH metamaterial-TLs are superior than their conventional alternatives, since they have smaller dimensions, lower-profile, wider bandwidth, better radiation patterns, higher gain and efficiency, which make them easier and more cost-effective to manufacture and mass produce. Hence, a broad range of metamaterial-based design possibilities are introduced to highlight the improvement of the performance parameters that are rare and not often discussed in available literature. Therefore, this survey provides a wide overview of key early-stage concepts of metematerial-based designs as a thorough reference for specialist antennas and microwave circuits designers. To analyze the critical features of metamaterial theory and concept, several examples are used. Comparisons on the basis of physical size, bandwidth, materials, gain, efficiency, and radiation patterns are made for all the examples that are based on CRLH metamaterial-TLs. As revealed in all the metematerial design examples, foot-print area decrement is an important issue of study that have a strong impact for the enlargement of the next generation wireless communication systems.

Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.

Software-defined networks (SDNs) offer robust network architectures for current and future Internet of Things (IoT) applications. At the same time, SDNs constitute an attractive target for cyber attackers due to their global network view and programmability. One of the major vulnerabilities of typical SDN architectures is their susceptibility to Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks can render SDN controllers unavailable to their underlying infrastructure, causing service disruption or a complete outage in many cases. In this paper, machine learning-based detection and classification of DDoS flooding attacks on SDNs is investigated using popular machine learning (ML) algorithms. The ML algorithms, classifiers and methods investigated are quadratic discriminant analysis (QDA), Gaussian Naïve Bayes (GNB), k-nearest neighbor (k-NN), and classification and regression tree (CART). The general principle is illustrated through a case study, in which, experimental data (i.e. jitter, throughput, and response time metrics) from a representative SDN architecture suitable for typical mid-sized enterprise-wide networks is used to build classification models that accurately identify and classify DDoS flooding attacks. The SDN model used was emulated in Mininet and the DDoS flooding attacks (i.e. hypertext transfer protocol (HTTP), transmission control protocol (TCP), and user datagram protocol (UDP) attacks) have been launched on the SDN model using low orbit ion cannon (LOIC). Although all the ML methods investigated show very good efficacy in detecting and classifying DDoS flooding attacks, CART demonstrated the best performance on average in terms of prediction accuracy (98%), prediction speed ( 5.3 × 10 ⁵ observations per second), training time (12.4 ms), and robustness.

Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods.

Optimization efficiency is a major challenge for electromagnetic (EM) device, circuit, and machine design. Although both surrogate model-assisted evolutionary algorithms (SAEAs) and parallel computing are playing important roles in addressing this challenge, there is little research that investigates their integration to benefit from both techniques. In this paper, a new method, called parallel SAEA for electromagnetic design (PSAED), is proposed. A state-of-the-art SAEA framework, surrogate model-aware evolutionary search, is used as the foundation of PSAED. Considering the landscape characteristics of EM design problems, three differential evolution mutation operators are selected and organized in a particular way. A new SAEA framework is then proposed to make use of the selected mutation operators in a parallel computing environment. PSAED is tested by a micromirror and a dielectric resonator antenna as well as four mathematical benchmark problems of various complexity. Comparisons with state-of-the-art methods verify the advantages of PSAED in terms of efficiency and optimization capacity.

In this communication, a novel dual-polarized highly folded self-grounded Bowtie antenna that is excited through I-shaped slots is proposed for applications in sub-6 GHz 5G multiple-input-multiple-output (MIMO) antenna systems. The antenna consists of two pairs of folded radiation petals whose base is embedded in a double layer of FR-4 substrate with a common ground-plane which is sandwiched between the two substrate layers. The ground-plane is defected with two I-shaped slots located under the radiation elements. Each pair of radiation elements are excited through a microstrip line on the top layer with RF signal that is 180° out of phase with respect to each other. The RF signal is coupled to the pair of feedlines on the top layer through the I-shaped slots from the two microstrip feedlines on the underside of the second substrate. The proposed feed mechanism gets rid of the otherwise bulky balun. The Bowtie antenna is a compact solution with dimensions of 32 $\times $ 32 $\times\,\,33.8$ mm ³ . Measured results have verified that the antenna operates over a frequency range of 3.1–5 GHz and exhibits an average gain and antenna efficiency in the vertical and horizontal polarizations of 7.5 dBi and 82.6%, respectively.