Yes / In this work, a compact 4-element multiple-input multiple-output (MIMO) antenna system is presented for sub-6 GHz applications. A modified M-shaped strip is used to form each antenna element in the MIMO system. To improve performance, a rectangular-shaped area is etched on the opposite side of each element in the ground plane. The antenna size is 100 × 60 mm2. Most interestingly, the port isolation is improved by rotating the etched areas and the corresponding radiating elements. This one-of-a-kind approach aided in the development of a highly isolated MIMO antenna with a small footprint. The theory of characteristic modes (TCM) is used to analyze the behavior of rotating the etched areas in the ground of the antenna. The antenna provides significant port isolation above 20 dB, stable radiation patterns, and an outstanding ECC of less than 0.01. The design is simple and compact, making it suitable for MIMO operation on handheld devices. / The full-text of this article will be released for public view at the end of the publisher embargo on 6 Jun 2024.

Yes / A dual-port transparent multiple-input multiple-output (MIMO) antenna resonating at sub-6 GHz 5G band is proposed by using patch/ground material as transparent conductive oxide (AgHT-8) and a transparent Plexiglas substrate. Two identical circular-shaped radiating elements fed by using a microstrip feedline are designed using the finite element method (FEM) based highfrequency structure simulator (HFSS) software. The effect of the isolation mechanism is discussed using two cases. In case 1, the two horizontally positioned elements are oriented in a similar direction with a separate ground plane, whereas in case 2, the elements are vertically placed facing opposite to each other with an allied ground. In both cases, the transparent antennas span over a −10 dB band of 4.65 to 4.97 GHz (300 MHz) with isolation greater than 15 dB among two elements. The diversity parameters are also analyzed for both the cases covering the correlation coefficient (ECC), mean effective gain (MEG), diversity gain (DG), and channel capacity loss (CCL). The average gain and efficiency above 1 dBi and 45%, respectively with satisfactory MIMO diversity performance, makes the transparent MIMO antenna an appropriate choice for smart IoT devices working in the sub-6 GHz 5G band by mitigating the co-site location and visual clutter issues. / This work is supported by the Moore4Medical project, funded within ECSEL JU in collaboration with the EU H2020 Framework Programme (H2020/2014-2020) under grant agreement H2020-ECSEL-2019-IA-876190, and Fundação para a Ciência e Tecnologia (ECSEL/0006/2019).

Yes / In this paper, a planar multiple input, multiple output (MIMO) dipole antenna for a future sub-6 GHz 5G application is proposed. The planar MIMO structure consists of 4 antenna elements with an overall size of 150×82×1 mm3. The single antenna element is characterized by a size of 32.5×33.7×1 mm3 printed on an FR-4 dielectric substrate with εr=4.4 and tanδ=0.02. The suggested antenna structure exhibits good impedance bandwidth equal to 3.24 GHz starting from 3.3 to 6.6 GHz with an S11 value of less than -10 dB (S11≤-10 dB) with antenna gain varying from 5.2 up to 7.05 dB in the entire band, which covers all the sub-6 GHz frequency band of the 5G application. Good isolation is achieved between the MIMO elements due to low surface waves inside the MIMO antenna substrate. The radiation of the MIMO antenna structure can be manipulated and many beam-types can be achieved as desired. The high-frequency structure simulator (HFSS) software package is used to design and simulate the proposed structure, while the CST MWS is used to validate the results.

During this project we have designed a new type of antenna that uses an array of antenna elements in order to emit electromagnetic radiation as signals and to be able to control the beam. After an extended time the design yielded a simulation which had the correct characteristics. After printing and constructing a prototype of the antenna it was tested in an anechoic chamber at Uppsala University. The array was divided into two different sub-arrays: the upper and the lower sub-arrays. Each of these consisted in itself of two sides: the long and the short sides. Each side had seven radiating elements, during the tests only one of the two sub-arrays (upper or lower) was running. Both sub-arrays are excited via a rat-race or 90 degree coupler. While the antenna was running it had 14 radiating elements and two phase shifters, two per sub-array and two per side. The idea was for a signal to travel passing the radiating elements and the phase shifter which would steer the induced electromagnetic signal in one direction, a traveling-wave array. This direction could be changed since the phase shifters were configurable in three different states per phase shifter, hence the induced electromagnetic beam was steerable. The beam was also steerable through the feed which was re-configurable, since there were two feeds per sub-array a phase shift could be introduced between the long and the short side. The beam steering range was between -2 degrees and 11 degrees oriented as 0 degrees would be a perpendicular line from the array to the receiving end. The design itself worked which could be stated from the results in the upper part of the array, the test results from the lower part however did not match the simulated results. This is likely due to an error in the construction of the antenna rather than the theory since the upper and lower part of the array was mirrored versions of each other. The phase shifters worked as intended in the bigger picture but were slightly different in the simulations compared to the physical ones, likely due to the same type of error source as regarding the full antenna.

Yes / To support various fifth generation (5G) wireless applications, a small, printed bowtie-shaped microstrip antenna with meandered arms is reported in this article. Because it spans the broad legal range, the developed antenna can serve or reject a variety of applications such as wireless fidelity (Wi-Fi), sub-6 GHz, and ultra-wideband (UWB) 5G communications due to its multiband characterization and optimized rejection bands. The antenna is built on an FR-4 substrate and powered via a 50-Ω microstrip feed line linked to the right bowtie’s side. The bowtie’s left side is coupled via a shorting pin to a partial ground at the antenna’s back side. A gradually increasing meandering microstrip line is connected to both sides of the bowtie to enhance the rejection and operating bands. The designed antenna has seven operating frequency bands of (2.43–3.03) GHz, (3.71–4.23) GHz, (4.76–5.38) GHz, (5.83–6.54) GHz, (6.85–7.44) GHz, (7.56–8.01) GHz, and (9.27–13.88) GHz. The simulated scattering parameter S11 reveals six rejection bands with percentage bandwidths of 33.87%, 15.73%, 11.71, 7.63%, 6.99%, and 12.22%, respectively. The maximum gain of the proposed antenna is 4.46 dB. The suggested antenna has been built, and the simulation and measurement results are very similar. The reported antenna is expanded to a four-element design to investigate its MIMO characteristics. / Partially funded by British Council “2019 UK-China-BRI Countries Partnership Initiative” program, with project titled “Adapting to Industry 4.0 oriented International Education and Research Collaboration.

[pt] A perda de percurso (PL) é um parâmetro essencial em modelos de propagação e crucial na determinação da área de cobertura de sistemas móveis. Os métodos de aprendizado de máquina (ML) tornaram-se ferramentas promissoras para a previsão de propagação de rádio. No entanto, ainda existem alguns desafios para sua implantação completa, relacionados à seleção das entradas mais significativas do modelo, à compreensão de suas contribuições para as previsões do modelo e à avaliação adicional da capacidade de generalização para amostras desconhecidas. Esta tese tem como objetivo projetar modelos de PL baseados em ML otimizados para diferentes aplicações das tecnologias 5G e além. Essas aplicações abrangem links de ondas milimétricas (mmWave) para ambientes indoor e outdoor na faixa de frequência de 26,5 a 40 GHz, cobertura de macrocélulas no espectro sub-6 GHz e comunicações veiculares usando campanhas de medições desenvolvidas em CETUC, Rio de Janeiro, Brazil. Vários algoritmos de ML são explorados, como redes neurais artificiais (ANN), regressão de vetor de suporte (SVR), floresta aleatória (RF) e aumento de árvore de gradiente (GTB). Além disso, estendemos dois modelos empíricos para mmWave com previsão de PL melhorada. Propomos uma metodologia para seleção robusta de modelos de ML e uma metodologia para selecionar os preditores mais adequados para as máquinas consideradas com base na melhoria de desempenho e na interpretabilidade do modelo. Além disso, para o canal veículo-veículo (V2V), uma técnica de rede neural convolucional (CNN) também é proposta usando uma abordagem de aprendizado por transferência para lidar com conjuntos de dados pequenos. Os testes de generalização propostos mostram a capacidade dos modelos de ML de aprender o padrão entre as entradas do modelo e a PL, mesmo em ambientes e cenários mais desafiadores de amostras desconhecidas. / [en] Path loss (PL) is an essential parameter in propagation models and critical in determining mobile systems’ coverage area. Machine learning (ML) methods have become promising tools for radio propagation prediction. However, there are still some challenges for its full deployment, concerning to selection of the most significant model s inputs, understanding their contributions to the model s predictions, and a further evaluation of the generalization capacity for unknown samples. This thesis aims to design optimized ML-based PL models for different applications for the 5G and beyond technologies. These applications encompass millimeter wave (mmWave) links for indoor and outdoor environments in the frequency band from 26.5 to 40 GHz, macrocell coverage in the sub-6 GHz spectrum, and vehicular communications using measurements campaign carried out by the Laboratory of Radio-propagation, CETUC, in Rio de Janeiro, Brazil. Several ML algorithms are exploited, such as artificial neural network (ANN), support vector regression (SVR), random forest (RF), and gradient tree boosting (GTB). Furthermore, we have extended two empirical models for mmWave with improved PL prediction. We proposes a methodology for robust ML model selection and a methodology to select the most suitable predictors for the machines considered based on performance improvement and the model’s interpretability. In adittion, for the vehicle-to-vehicle (V2V) channel, a convolutional neural network (CNN) technique is also proposed using a transfer learning approach to deal with small datasets. The generalization tests proposed shows the ability of the ML models to learn the pattern between the model’s inputs and PL, even in more challenging environments and scenarios of unknown samples.

[pt] A perda de percurso (PL) é um parâmetro essencial em modelos de propagação e crucial na determinação da área de cobertura de sistemas móveis. Osmétodos de aprendizado de máquina (ML) tornaram-se ferramentas promissoras para a previsão de propagação de rádio. No entanto, ainda existem algunsdesafios para sua implantação completa, relacionados à seleção das entradasmais significativas do modelo, à compreensão de suas contribuições para asprevisões do modelo e à avaliação adicional da capacidade de generalizaçãopara amostras desconhecidas. Esta tese tem como objetivo projetar modelosde PL baseados em ML otimizados para diferentes aplicações das tecnologias5G e além. Essas aplicações abrangem links de ondas milimétricas (mmWave)para ambientes indoor e outdoor na faixa de frequência de 26,5 a 40 GHz,cobertura de macrocélulas no espectro sub-6 GHz e comunicações veicularesusando campanhas de medições desenvolvidas em CETUC, Rio de Janeiro,Brazil. Vários algoritmos de ML são explorados, como redes neurais artificiais(ANN), regressão de vetor de suporte (SVR), floresta aleatória (RF) e aumentode árvore de gradiente (GTB). Além disso, estendemos dois modelos empíricospara mmWave com previsão de PL melhorada. Propomos uma metodologiapara seleção robusta de modelos de ML e uma metodologia para selecionar ospreditores mais adequados para as máquinas consideradas com base na melhoria de desempenho e na interpretabilidade do modelo. Além disso, para o canalveículo-veículo (V2V), uma técnica de rede neural convolucional (CNN) também é proposta usando uma abordagem de aprendizado por transferência paralidar com conjuntos de dados pequenos. Os testes de generalização propostosmostram a capacidade dos modelos de ML de aprender o padrão entre as entradas do modelo e a PL, mesmo em ambientes e cenários mais desafiadoresde amostras desconhecidas. / [en] Path loss (PL) is an essential parameter in propagation models and critical in determining mobile systems coverage area. Machine learning (ML) methods have become promising tools for radio propagation prediction. However, there are still some challenges for its full deployment, concerning to selection of the most significant model s inputs, understanding their contributions to the model s predictions, and a further evaluation of the generalization capacity for unknown samples. This thesis aims to design optimized ML-based PL models for different applications for the 5G and beyond technologies. These applications encompass millimeter wave (mmWave) links for indoor and outdoor environments in the frequency band from 26.5 to 40 GHz, macrocell coverage in the sub-6 GHz spectrum, and vehicular communications using measurements campaign carried out by the Laboratory of Radio-propagation, CETUC, in Rio de Janeiro, Brazil. Several ML algorithms are exploited, such as artificial neural network (ANN), support vector regression (SVR), random forest (RF), and gradient tree boosting (GTB). Furthermore, we have extended two empirical models for mmWave with improved PL prediction. We proposes a methodology for robust ML model selection and a methodology to select the most suitable predictors for the machines considered based on performance improvement and the model s interpretability. In adittion, for the vehicle-to-vehicle (V2V) channel, a convolutional neural network (CNN) technique is also proposed using a transfer learning approach to deal with small datasets. The generalization tests proposed shows the ability of the ML models to learn the pattern between the model’s inputs and PL, even in more challenging environments and scenarios of unknown samples.

Yes / The paper presents the general requirements and an exemplary design of the RF front-end system that in today's handset is a key consumer of power. The design is required to minimize the carbon footprint in mobile handsets devices, whilst facilitating cooperation, and providing the energy-efficient operation of multi-standards for 5G communications. It provides the basis of hardware solutions for RF front-end integration challenges and offers design features covering energy efficiency for power amplifiers (PAs), Internet of Things (IoT) controlled tunable filters and compact highly isolated multiple-input and multiple-output (MIMO) antennas. An optimum design requires synergetic collaboration between academic institutions and industry in order to satisfy the key requirements of sub-6 GHz energy-efficient 5G transceivers, incorporating energy efficiency, good linearity and the potential for low-cost manufacturing. A highly integrated RF transceiver was designed and implemented to transmit and receive a picture using compact MIMO antennas integrated with efficient tunable filters and high linearity PAs. The proposed system has achieved a bit error rate (BER) of less than 10-10 at a data rate of 600 Mb/s with a wireless communication distance of more than 1 meter and power dissipation of 18-20 mW using hybrid beamforming technology and 64-QAM modulation. / 10.13039/100010665-H2020 Marie Skodowska Curie

Yes / In this paper, a novel dual-polarized highly-folded self-grounded Bowtie antenna that is excited through I-shaped slots is proposed for applications in sub-6GHz 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 32×32×33.8 mm3. 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.