128 research outputs found
Indoor radio channel modeling at D-band frequencies
No full textThis paper presents indoor radio channel measurements and models at D-band frequencies. A Line-of-Sight (LOS) alpha-beta-gamma path loss model is created based on indoor measurements up to 8.5 m in a laboratory and office room, resulting in a floating intercept alpha of 34.2 dB, PL exponent beta 1.9 and frequency dependency gamma 1.9. The penetration losses for wood, acrylic, polyvinyl chloride (PVC) and glass are measured, resulting in a respective loss of 8, 3.5, 3 and 12 dB/cm. Furthermore, attenuation due to desk objects obstructing the LOS path is found to range from 3 to 10 dB for one or more universal serial bus (USB) cables, and 8 to 13 dB for a computer keyboard and mouse. A laptop screen completely blocks the LOS path. Therefore, we measured the attenuation of the reflected path when the LOS path is blocked, and conclude that desk objects provide valid fallback paths
Three-stages concatenated Machine Learning model for SFN prediction
No full textThe single frequency network (SFN) has been assumed worldwide by telecommunication operators to save radio frequency resources and homogenize the network. Its applications have transcended the digital terrestrial television and digital radio to become part of the key techniques of the broadband and broadcast convergence for LTE-A, 5G and beyond. However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected up-performance and quality of service. This paper aims to propose a machine learning model to predict the SFN performance from the legacy MFN parameters. The model is based on regression and classification machine learning algorithms concatenated in three consecutive stages to predict SFN electric-field strength, modulation error ratio and gain. The training and test processes are performed with a dataset of 389 samples from an SFN/MFN trial in Ghent, Belgium. The best performance is obtained with concatenating gradient boosting, random forest, and linear regression, which allows predicting the SFN electric-field strength with an R2 of 92%, the modulation error ratio with 95%, and SFN gain with 87% from only MFN and position data. Besides, the model allows classifying the data points according to positive or negative SFN gain with an accuracy of 93%
Outdoor channel modeling at D-band frequencies for future fixed wireless access applications
No full textFixed wireless access networks at millimeter wave frequencies enable an alternative to fiber-optic installations for providing high-throughput Internet connectivity. In this letter, we present outdoor channel measurements at D-band frequencies ranging from 120 GHz to 165 GHz, contributing to the design of future fixed wireless access networks. We measure angular path loss (PL) for both Line-of-Sight (LOS) and non-Line-of-Sight (NLOS) scenarios and calculate angular spread. We also measure building reflection loss for different angles and building facades. Directional LOS PL equals free-space PL, whereas omnidirectional PL is slightly lower. The angular spread of the LOS measurements is 19.7 degrees. The omnidirectional NLOS PL model has a higher PL and the angular spread increases to 54.4 degrees. Losses up to 11 dB should be taken into account for reflection on a fiber cement or building brick facade. and up to 15.6 dB and 18.5 dB for roughcast and stone bricks. Even though wireless communication via the direct path is preferred, reflected paths can enable high-throughput wireless communication if the direct path is obstructed
From MFN to SFN: Performance Prediction Through Machine Learning
No full textIn the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97
Plets: a product line of model-based testing tools
No full textSoftware testing is recognized as a fundamental activity for assuring software quality. Furthermore, testing is also recognized as one of the most time consuming and expensive activities of software development process. A diversity of testing tools has been developed to support this activity, including tools for Model-based Testing (MBT). MBT is a testing technique to automate the generation of testing artifacts from the system model. This technique presents several advantages, such as, lower cost and less effort to generate test cases. Therefore, in the last years a diversity of commercial, academic, and open source tools to support MBT has been developed to better explore these advantages. In spite of the diversity of tools to support MBT, most of them have been individually and independently developed from scratch based on a single architecture. Thus, they face difficulties of integration, evolution, maintenance, and reuse. In another perspective, Software Product Lines (SPL) offers possibility of systematically generating software products at lower costs, in shorter time, and with higher quality. The main contribution of this Ph. D thesis is to present a SPL for testing tools that support MBT (PLeTs) and an automated environment to support the generation of these tools (PlugSPL). Furthermore, our strategy was initially applied to generate some MBT testing tools which were applied in two examples of use performed in collaboration of an IT company. Based on the feedback from the examples of use we can infer that SPL can be considered a relevant approach to improve productivity and reuse during generation of MBT testing tools. Moreover, we also performed an experimental study carried out to evaluate the effort to use an MBT tool derived from our SPL to generate test scripts and scenarios. Thus, the results point out that the effort to generate test scripts, when compared with a Capture and Replay based tool, was reduced considerably.O teste de software é uma atividade fundamental para garantir a qualidade de software. Além disso, teste de software é uma das atividades mais caras e demoradas no processo de desenvolvimento de software. Por esta razão, diversas ferramentas de teste foram desenvolvidas para apoiar esta atividade, incluindo ferramentas para Teste Baseado em Modelos (TBM). TBM é uma técnica de teste para automatizar a geração de artefatos de teste a partir de modelos do sistema. Esta técnica apresenta diversas vantagens, tais como, menor custo e esforço para gerar casos de teste. Por este motivo, nos últimos anos, diversas ferramentas para TBM foram desenvolvidas para melhor explorar essas vantagens. Embora existam diversas ferramentas TBM, a maioria delas tem o seu desenvolvimento baseado em um esforço individual, sem a adoção de técnicas de reuso sistemático e com base em uma única arquitetura, dificultando a integração, evolução, manutenção e reutilização dessas ferramentas. Uma alternativa para mitigar estes problemas é adotar os conceitos de Linhas de Produto de Software (LPS) para desenvolver ferramentas de TBM. LPS possibilitam gerar sistematicamente produtos a custos mais baixos, em menor tempo e com maior qualidade. A principal contribuição desta tese de doutorado é apresentar uma LPS de ferramentas de teste que suportam TBM (PLeTs) e um ambiente automatizado para apoiar a geração dessas ferramentas (PlugSPL). Além disso, esta tese apresenta uma abordagem para gerar ferramentas para TBM, que foram aplicadas em dois exemplos de uso. Com base nos resultados obtidos nos exemplos de uso, podemos inferir que LPS pode ser considerada uma abordagem relevante para melhorar a produtividade e o reuso durante a geração de ferramentas de TBM. Além disso, também foi realizado um estudo experimental com o objetivo de avaliar o esforço para se utilizar uma ferramenta derivada da PLeTs para geração de scripts de teste. Os resultados apontaram que o esforço para gerar scripts de teste foi reduzido consideravelmente, quando comparado com a uma ferramenta de Capture and Replay
Plets: a product line of model-based testing tools
No full textO teste de software é uma atividade fundamental para garantir a qualidade de software. Além disso, teste de software é uma das atividades mais caras e demoradas no processo de desenvolvimento de software. Por esta razão, diversas ferramentas de teste foram desenvolvidas para apoiar esta atividade, incluindo ferramentas para Teste Baseado em Modelos (TBM). TBM é uma técnica de teste para automatizar a geração de artefatos de teste a partir de modelos do sistema. Esta técnica apresenta diversas vantagens, tais como, menor custo e esforço para gerar casos de teste. Por este motivo, nos últimos anos, diversas ferramentas para TBM foram desenvolvidas para melhor explorar essas vantagens. Embora existam diversas ferramentas TBM, a maioria delas tem o seu desenvolvimento baseado em um esforço individual, sem a adoção de técnicas de reuso sistemático e com base em uma única arquitetura, dificultando a integração, evolução, manutenção e reutilização dessas ferramentas. Uma alternativa para mitigar estes problemas é adotar os conceitos de Linhas de Produto de Software (LPS) para desenvolver ferramentas de TBM.LPS possibilitam gerar sistematicamente produtos a custos mais baixos, em menor tempo e com maior qualidade. A principal contribuição desta tese de doutorado é apresentar uma LPS de ferramentas de teste que suportam TBM (PLeTs) e um ambiente automatizado para apoiar a geração dessas ferramentas (PlugSPL). Além disso, esta tese apresenta uma abordagem para gerar ferramentas para TBM, que foram aplicadas em dois exemplos de uso. Com base nos resultados obtidos nos exemplos de uso, podemos inferir que LPS pode ser considerada uma abordagem relevante para melhorar a produtividade e o reuso durante a geração de ferramentas de TBM. Além disso, também foi realizado um estudo experimental com o objetivo de avaliar o esforço para se utilizar uma ferramenta derivada da PLeTs para geração de scripts de teste. Os resultados apontaram que o esforço para gerar scripts de teste foi reduzido consideravelmente, quando comparado com a uma ferramenta de Capture and Replay.Software testing is recognized as a fundamental activity for assuring software quality. Furthermore, testing is also recognized as one of the most time consuming and expensive activities of software development process. A diversity of testing tools has been developed to support this activity, including tools for Model-based Testing (MBT). MBT is a testing technique to automate the generation of testing artifacts from the system model. This technique presents several advantages, such as, lower cost and less effort to generate test cases. Therefore, in the last years a diversity of commercial, academic, and open source tools to support MBT has been developed to better explore these advantages. In spite of the diversity of tools to support MBT, most of them have been individually and independently developed from scratch based on a single architecture. Thus, they face difficulties of integration, evolution, maintenance, and reuse. In another perspective, Software Product Lines (SPL) offers possibility of systematically generating software products at lower costs, in shorter time, and with higher quality.The main contribution of this Ph. D thesis is to present a SPL for testing tools that support MBT (PLeTs) and an automated environment to support the generation of these tools (PlugSPL). Furthermore, our strategy was initially applied to generate some MBT testing tools which were applied in two examples of use performed in collaboration of an IT company. Based on the feedback from the examples of use we can infer that SPL can be considered a relevant approach to improve productivity and reuse during generation of MBT testing tools. Moreover, we also performed an experimental study carried out to evaluate the effort to use an MBT tool derived from our SPL to generate test scripts and scenarios. Thus, the results point out that the effort to generate test scripts, when compared with a Capture and Replay based tool, was reduced considerably
Improved cattle behaviour monitoring by combining ultra-wideband location and accelerometer data
No full textCattle behaviour is fundamentally linked to the cows' health, (re)production, and welfare. The aim of this study was to present an efficient method to incorporate Ultra-Wideband (UWB) indoor location and accelerometer data for improved cattle behaviour monitoring systems. In total, 30 dairy cows were fitted with UWB Pozyx wearable tracking tags (Pozyx, Ghent, Belgium) on the upper (dorsal) side of the cow's neck. In addition to the location data, the Pozyx tag reports accelerometer data as well. The combination of both sensor data was performed in two steps. In the first step, the actual time spent in the different barn areas was calculated using location data. In the second step, accelerometer data were used to clas-sify cow behaviour using the location information of step 1 (e.g., a cow located in the cubicles cannot be classified as feeding, or drinking). A total of 156 hours of video recordings were used for the validation. For each hour of data, the total time each cow spent in each area and performing which behaviours (feed-ing, drinking, ruminating, resting, and eating concentrates) were computed using the sensors and com-pared against annotated video recordings. Bland-Altman plots for the correlation and difference between the sensors and the video recording were then computed for the performance analysis. The overall performance of locating the animals into the correct functional areas was very high. The R2 was 0.99 (P < 0.001), and the root-mean-square error (RMSE) was 1.4 min (7.5% of the total time). The best performance was obtained for the feeding and lying areas (R2 = 0.99, P < 0.001). Performance was lower in the drinking area (R2 = 0.90, P < 0.01) and the concentrate feeder (R2 = 0.85, P < 0.05). For the combined location + accelerometer data, high overall performance (all behaviours) was obtained with an R2 of 0.99 (P < 0.001) and a RMSE of 1.6 min (12% of the total time). The combination of location and accelerometer data improved the RMSE of the feeding time and ruminating time compared to the accelerometer data alone (2.6-1.4 min). Moreover, the combination of location and accelerometer enabled accurate classification of additional behaviours that are difficult to detect using the accelerom-eter alone, such as eating concentrates and drinking (R2 = 0.85 and 0.90, respectively). This study demon-strates the potential of combining accelerometer and UWB location data for the design of a robust monitoring system for dairy cattle.(c) 2023 The Author(s). Published by Elsevier B.V. on behalf of The Animal Consortium. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Directional sub-THz antenna-channel modelling for indoor scenarios
No full textIn this paper, sub-THz channel sounding is discussed for indoor scenarios. A directional D-band channel sounder, operational in the full band ranging from 110 to 170 GHz, is presented. We examine the influence of the antenna characteristics on the channel sounder. The channel sounder is used for Line-of-Sight path loss (PL) modelling at sub-THz frequencies for distances up to 4 m. Validation measurements confirm the small beamwidth of the antenna, ranging from 12 degrees for 110 GHz to 11 degrees for 170 GHz. An antenna de-embedding methodology is presented. Fitting Line-of-Sight PL at center frequency 140 GHz to a one-slope model gives a reference PL of 76.0 dB at 1 m and a PL exponent of 1.9. The reference PL is slightly higher than free space PL, whereas the PL exponent is slightly lower
Material characterization and radio channel modeling at D-band frequencies
No full textAs the throughput requirements for wireless communication links keep rising, characterization of sub-THz radio channels is necessary. This paper presents the results of a radio channel measurement campaign in which we characterize the full D-band, ranging from 110 to 170 GHz, for distances up to 5 m. We measured penetration and reflection loss for a broad set of materials that are commonly used in indoor environments, including wood, glass, acrylic, and concrete, and measured corner diffraction losses. Measurements over the full 60 GHz bandwidth reveal frequency selectivity as well as a periodic variation of both penetration and reflection loss, which is attributed to the thin film effect. Based on measurements in a conference room and outdoors, we create a spatio-temporal channel model for the conference room and an outdoor path loss model. The channel models show that the radio channel is extremely sparse to multipath components, containing only a Line-of-Sight path with signal attenuation close to path loss in free space, and first-order reflections with a measured attenuation that corresponds to the sum of the path and reflection loss
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