Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887781
P. Casas, Sarah Wassermann, Nikolas Wehner, Michael Seufert, T. Hossfeld
Web Quality of Experience (QoE) monitoring is a critical task for Internet Service Providers (ISPs), especially due to the key role played by customer experience in churn management. Previously, we have tackled the problem of Web QoE inference from the ISP perspective, relying on passive measurement of encrypted network traffic and machine learning models. In this paper, we exploit the broad heterogeneity of contents embedded in web pages to improve the state of the art performance in Web QoE inference, relying on web-content learning model tailoring. By analyzing the top-500 most popular web pages of the Internet through unsupervised learning, we discover different web page content classes which realize sig-nificantly different Web QoE inference performance. We train supervised learning inference models separately for each of these classes, using the well-known Speed Index (SI) metric as proxy to Web QoE. Empirical evaluations on a large corpus of Web QoE measurements for top popular websites demonstrate that our combined content-tailored approach improves the inference performance of the SI by almost 30 % with respect to previous single-model approaches, reducing the QoE inference error in terms of mean opinion scores by more than 40%.
{"title":"Not all Web Pages are Born the Same Content Tailored Learning for Web QoE Inference","authors":"P. Casas, Sarah Wassermann, Nikolas Wehner, Michael Seufert, T. Hossfeld","doi":"10.1109/MN55117.2022.9887781","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887781","url":null,"abstract":"Web Quality of Experience (QoE) monitoring is a critical task for Internet Service Providers (ISPs), especially due to the key role played by customer experience in churn management. Previously, we have tackled the problem of Web QoE inference from the ISP perspective, relying on passive measurement of encrypted network traffic and machine learning models. In this paper, we exploit the broad heterogeneity of contents embedded in web pages to improve the state of the art performance in Web QoE inference, relying on web-content learning model tailoring. By analyzing the top-500 most popular web pages of the Internet through unsupervised learning, we discover different web page content classes which realize sig-nificantly different Web QoE inference performance. We train supervised learning inference models separately for each of these classes, using the well-known Speed Index (SI) metric as proxy to Web QoE. Empirical evaluations on a large corpus of Web QoE measurements for top popular websites demonstrate that our combined content-tailored approach improves the inference performance of the SI by almost 30 % with respect to previous single-model approaches, reducing the QoE inference error in terms of mean opinion scores by more than 40%.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134437181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887676
N. Djuric, D. Kljajić, Teodora Gavrilov, Nadja Markovic Golubovic, S. Djuric
The man-made electromagnetic field (EMF) has been extensively used in last several decades, particularly since wireless telecommunication technology experienced substantial expansion. Simultaneously, the exposure to EMF has become an issue and inevitable part of intensive public debate on health ef-fects. Thus, the International Commission for Non-Ionizing Ra-diation Protection (ICNIRP) has published ICNIRP 2020 guide-lines, in order to ensure high-quality protection against all so far acknowledged health risks when population is/can be exposed to EMFs. Beside the latest updates of the exposure limits, which are based on improved scientific accuracy in most recent scien-tific studies, an important task of the ICNIRP 2020 is also a rec-ommendation to national decision-makers to update their EMF legislation. Such accomplishment is fundamental for their activ-ities on EMF investigation, since all activities should be lined up with up-to-date recommendations. In this paper, the standardi-zation update of the most important Serbian EMF legislation acts is considered, highlighting parts of prescribed EMF expo-sure limits that should be altered in the near future.
{"title":"The ICNIRP 2020 Guidelines and Standardization update of Serbian EMF radiation exposure limits","authors":"N. Djuric, D. Kljajić, Teodora Gavrilov, Nadja Markovic Golubovic, S. Djuric","doi":"10.1109/MN55117.2022.9887676","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887676","url":null,"abstract":"The man-made electromagnetic field (EMF) has been extensively used in last several decades, particularly since wireless telecommunication technology experienced substantial expansion. Simultaneously, the exposure to EMF has become an issue and inevitable part of intensive public debate on health ef-fects. Thus, the International Commission for Non-Ionizing Ra-diation Protection (ICNIRP) has published ICNIRP 2020 guide-lines, in order to ensure high-quality protection against all so far acknowledged health risks when population is/can be exposed to EMFs. Beside the latest updates of the exposure limits, which are based on improved scientific accuracy in most recent scien-tific studies, an important task of the ICNIRP 2020 is also a rec-ommendation to national decision-makers to update their EMF legislation. Such accomplishment is fundamental for their activ-ities on EMF investigation, since all activities should be lined up with up-to-date recommendations. In this paper, the standardi-zation update of the most important Serbian EMF legislation acts is considered, highlighting parts of prescribed EMF expo-sure limits that should be altered in the near future.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128821101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887775
Idio Guarino, Giampaolo Bovenzi, Davide Di Monda, Giuseppe Aceto, D. Ciuonzo, A. Pescapé
Current intrusion detection techniques cannot keep up with the increasing amount and complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to apply deep packet inspection approaches. In recent years, Machine Learning techniques have been proposed for post-mortem detection of network attacks, and many datasets have been shared by research groups and organizations for training and validation. Differently from the vast related literature, in this paper we propose an early classification approach conducted on CSE-CIC-IDS2018 dataset, which contains both benign and malicious traffic, for the detection of malicious attacks before they could damage an organization. To this aim, we investigated a different set of features, and the sensitivity of performance of five classification algorithms to the number of observed packets. Results show that ML approaches relying on ten packets provide satisfactory results.
{"title":"On the use of Machine Learning Approaches for the Early Classification in Network Intrusion Detection","authors":"Idio Guarino, Giampaolo Bovenzi, Davide Di Monda, Giuseppe Aceto, D. Ciuonzo, A. Pescapé","doi":"10.1109/MN55117.2022.9887775","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887775","url":null,"abstract":"Current intrusion detection techniques cannot keep up with the increasing amount and complexity of cyber attacks. In fact, most of the traffic is encrypted and does not allow to apply deep packet inspection approaches. In recent years, Machine Learning techniques have been proposed for post-mortem detection of network attacks, and many datasets have been shared by research groups and organizations for training and validation. Differently from the vast related literature, in this paper we propose an early classification approach conducted on CSE-CIC-IDS2018 dataset, which contains both benign and malicious traffic, for the detection of malicious attacks before they could damage an organization. To this aim, we investigated a different set of features, and the sensitivity of performance of five classification algorithms to the number of observed packets. Results show that ML approaches relying on ten packets provide satisfactory results.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125065872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887745
Marco Ghidelli, S. Massardi, Luca Foletti, Alberto Cantón González, M. Lancini
Experimental tests in biomechanics are often composed of several systems and devices, each of them providing information that needs to be acquired and processed. Data synchronization is a key factor for test results that need to be properly analyzed, limiting the influence of time delays between the acquired signals. Standard synchronization protocols are applied in different fields, from industry to telecommunications, but the hardware and software requirements for their implementation are normally difficult to be applied in biomechanics laboratories where instrumentation and protocols are likely to be changed over different experiments. Variability of sensors in the market, experimenter's skills, and test schedules hamper the application of robust standardized synchronization protocols, leading to increase post-processing efforts and the protocol steps for data acquisition. We propose a simple and cheap solution for synchronization that can be applied in experimental scenarios such as biomechanics laboratory based on a raspberry used as a trigger-box. This solution aims to easily synchronize data in a ROS-based network with any devices handling analogic trigger signals. The proposed solution is validated by evaluating time metrics in a system composed of several trigger boxes for a multi-sensor system simulation. The performed validation confirms the applicability of this solution for biomechanic tests with a wide margin of tolerance.
{"title":"Validation of a ROS-Based Synchronization System for Biomechanics Gait Labs","authors":"Marco Ghidelli, S. Massardi, Luca Foletti, Alberto Cantón González, M. Lancini","doi":"10.1109/MN55117.2022.9887745","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887745","url":null,"abstract":"Experimental tests in biomechanics are often composed of several systems and devices, each of them providing information that needs to be acquired and processed. Data synchronization is a key factor for test results that need to be properly analyzed, limiting the influence of time delays between the acquired signals. Standard synchronization protocols are applied in different fields, from industry to telecommunications, but the hardware and software requirements for their implementation are normally difficult to be applied in biomechanics laboratories where instrumentation and protocols are likely to be changed over different experiments. Variability of sensors in the market, experimenter's skills, and test schedules hamper the application of robust standardized synchronization protocols, leading to increase post-processing efforts and the protocol steps for data acquisition. We propose a simple and cheap solution for synchronization that can be applied in experimental scenarios such as biomechanics laboratory based on a raspberry used as a trigger-box. This solution aims to easily synchronize data in a ROS-based network with any devices handling analogic trigger signals. The proposed solution is validated by evaluating time metrics in a system composed of several trigger boxes for a multi-sensor system simulation. The performed validation confirms the applicability of this solution for biomechanic tests with a wide margin of tolerance.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132415532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887777
A. Sârbu, D. Vatamanu, S. Miclaus, G. Mihai, M. Sorecau, E. Sorecau, P. Bechet
With its recent advances, electro-optical (EO) technology stands out as a promising alternative to conventional near field measurement instrumentation due to their miniature size and dielectric structure that does not interfere with the measured field. In this article we have used a type of commercially available EO measurement system to evaluate both in-air and in-liquid electric (E) field strength in the proximity of a custom fabricated antenna operating at 3.5 GHz frequency. Comparative computational and experimental results are presented and analysed with respect to the medium, antenna power, distance from the antenna and based on the guidelines limiting human exposure to EMFs. Present findings suggest that at their current technological development, the investigated EO probe response becomes inadequate for channel bandwidths commonly used in new generation communication standards (20 MHz and higher), especially if low emit powers are used (below 30 mW).
{"title":"Computational and experimental characterization of EMF exposure at 3.5 GHz using electro-optical probes","authors":"A. Sârbu, D. Vatamanu, S. Miclaus, G. Mihai, M. Sorecau, E. Sorecau, P. Bechet","doi":"10.1109/MN55117.2022.9887777","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887777","url":null,"abstract":"With its recent advances, electro-optical (EO) technology stands out as a promising alternative to conventional near field measurement instrumentation due to their miniature size and dielectric structure that does not interfere with the measured field. In this article we have used a type of commercially available EO measurement system to evaluate both in-air and in-liquid electric (E) field strength in the proximity of a custom fabricated antenna operating at 3.5 GHz frequency. Comparative computational and experimental results are presented and analysed with respect to the medium, antenna power, distance from the antenna and based on the guidelines limiting human exposure to EMFs. Present findings suggest that at their current technological development, the investigated EO probe response becomes inadequate for channel bandwidths commonly used in new generation communication standards (20 MHz and higher), especially if low emit powers are used (below 30 mW).","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"251 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113995311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887648
D. D. Prete, F. Arcadio, Chiara Griffo, Dalila Cicatiello, L. Zeni, N. Cennamo
A novel low-cost rain sensor based on a Surface Plasmonic Resonance (SPR) platform has been designed, realized, and tested. The SPR platform has been used to detect the presence of rain by using a simple setup exploiting an LED, a photodiode, and an Arduino microcontroller. The SPR sensor is placed into a specially designed 3D-printed holder to permit rainwater flow upon the sensitive region. Two studies have been carried out to test the sensor system in terms of sensitivity, which resulted equal to 0.057 mV/μ1, and set a threshold to avoid false alarm events.
{"title":"An Arduino-based plasmonic sensor to detect rain and its analysis","authors":"D. D. Prete, F. Arcadio, Chiara Griffo, Dalila Cicatiello, L. Zeni, N. Cennamo","doi":"10.1109/MN55117.2022.9887648","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887648","url":null,"abstract":"A novel low-cost rain sensor based on a Surface Plasmonic Resonance (SPR) platform has been designed, realized, and tested. The SPR platform has been used to detect the presence of rain by using a simple setup exploiting an LED, a photodiode, and an Arduino microcontroller. The SPR sensor is placed into a specially designed 3D-printed holder to permit rainwater flow upon the sensitive region. Two studies have been carried out to test the sensor system in terms of sensitivity, which resulted equal to 0.057 mV/μ1, and set a threshold to avoid false alarm events.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116175309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887691
Won Park, Nicolas Ferland, Wenting Sun
In modern network and telecommunication systems, hundreds of thousands of nodes are interconnected by telecommunication links to exchange information between nodes. The complexity of the system and the stringent requirements on service level agreement makes it necessary to monitor network performance intelligently and enable preventative measures to ensure network performance. Anomaly detection - the task of identifying events that deviate from the normal behavior - continues to be an important task. However, techniques traditionally employed by industry on real-world data - DBSCAN and MAD - have severe limitations, such as the need to manually tune and calibrate the algorithms frequently and limited capacity to capture past history in the model. Lately, there has been much progression in applying machine learning techniques, specifically autoencoders to the problem of AD. However, thus far, few of these techniques have been tested for use in scenarios involving multivariate timeseries data that would be faced by telecommunication companies. We propose a novel auto encoder based deep learning framework called ERICA including a new pipeline to address these shortcomings. Our approach has been demonstrated to achieve better performance (an increase in F-score by over 10%) and significantly enhance the scalability.
{"title":"Autoencoder for Network Anomaly Detection","authors":"Won Park, Nicolas Ferland, Wenting Sun","doi":"10.1109/MN55117.2022.9887691","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887691","url":null,"abstract":"In modern network and telecommunication systems, hundreds of thousands of nodes are interconnected by telecommunication links to exchange information between nodes. The complexity of the system and the stringent requirements on service level agreement makes it necessary to monitor network performance intelligently and enable preventative measures to ensure network performance. Anomaly detection - the task of identifying events that deviate from the normal behavior - continues to be an important task. However, techniques traditionally employed by industry on real-world data - DBSCAN and MAD - have severe limitations, such as the need to manually tune and calibrate the algorithms frequently and limited capacity to capture past history in the model. Lately, there has been much progression in applying machine learning techniques, specifically autoencoders to the problem of AD. However, thus far, few of these techniques have been tested for use in scenarios involving multivariate timeseries data that would be faced by telecommunication companies. We propose a novel auto encoder based deep learning framework called ERICA including a new pipeline to address these shortcomings. Our approach has been demonstrated to achieve better performance (an increase in F-score by over 10%) and significantly enhance the scalability.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116075424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887728
Christopher Vaccaro, Jorge Valverde, Alberto Cruz, M. Torres, Jose Cordova-Garcia
In this paper we focus on analyzing the precision error of electrical parameters as well as the quality of the data transmission of measurements generated by low cost electric meter devices. The study is based on data collected in a large university campus which includes many non-linear electrical loads. Related harmonic distortions are identified as one possible source of measurement error. Also, we evaluate a quality of service proxy for the deployed data network and discuss design changes to robustify data transmission. After evaluating campus measurements using a standardized power quality analyzer we find that many meters presented high measurement errors and while some may be attributed to harmonics and large non-linear loads, other errors can be related to the transducers and the internal code used by the device. The need for open hardware is motivated throughout the study when discussing limitations of the system evaluated.
{"title":"Evaluating Low-cost Networked Energy Metering Systems: A University Campus Study","authors":"Christopher Vaccaro, Jorge Valverde, Alberto Cruz, M. Torres, Jose Cordova-Garcia","doi":"10.1109/MN55117.2022.9887728","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887728","url":null,"abstract":"In this paper we focus on analyzing the precision error of electrical parameters as well as the quality of the data transmission of measurements generated by low cost electric meter devices. The study is based on data collected in a large university campus which includes many non-linear electrical loads. Related harmonic distortions are identified as one possible source of measurement error. Also, we evaluate a quality of service proxy for the deployed data network and discuss design changes to robustify data transmission. After evaluating campus measurements using a standardized power quality analyzer we find that many meters presented high measurement errors and while some may be attributed to harmonics and large non-linear loads, other errors can be related to the transducers and the internal code used by the device. The need for open hardware is motivated throughout the study when discussing limitations of the system evaluated.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134512466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887533
Alessandro Destro, G. Giorgi
The design of suitable clock servo is a well-known problem in the context of network-based synchronization systems. Several approaches can be found in the current literature, typically based on PI-controllers or Kalman filtering. These methods require a thorough knowledge of the environment, i.e. clock model, stability parameters, temperature variations, network traffic load, traffic profile and so on. This a-priori knowledge is required to optimize the servo parameters, such as PI constants or transition matrices in a Kalman filter. In this paper we propose instead a clock servo based on the recent Reinforcement Learning approach. In this case a self-learning algorithm based on a deep-Q network learns how to synchronize a local clock only from experience and by exploiting a limited set of predefined actions. Encouraging preliminary results reported in this paper represent a first step to explore the potentiality of the reinforcement learning in synchronization systems typically characterized by an initial lack of knowledge or by a great environmental variability.
{"title":"Reinforcement Learning applied to Network Synchronization Systems","authors":"Alessandro Destro, G. Giorgi","doi":"10.1109/MN55117.2022.9887533","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887533","url":null,"abstract":"The design of suitable clock servo is a well-known problem in the context of network-based synchronization systems. Several approaches can be found in the current literature, typically based on PI-controllers or Kalman filtering. These methods require a thorough knowledge of the environment, i.e. clock model, stability parameters, temperature variations, network traffic load, traffic profile and so on. This a-priori knowledge is required to optimize the servo parameters, such as PI constants or transition matrices in a Kalman filter. In this paper we propose instead a clock servo based on the recent Reinforcement Learning approach. In this case a self-learning algorithm based on a deep-Q network learns how to synchronize a local clock only from experience and by exploiting a limited set of predefined actions. Encouraging preliminary results reported in this paper represent a first step to explore the potentiality of the reinforcement learning in synchronization systems typically characterized by an initial lack of knowledge or by a great environmental variability.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114285981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-18DOI: 10.1109/MN55117.2022.9887647
Pasquale Beneduce, A. Capozzoli, C. Curcio, A. Liseno, Giovanni Petraglia, Gaetano Prisco, Marcello Ranucci, Chiara Sonatore
This paper discusses design and testing of fully-digital direct-conversion array transmitter in C Band developed as a part of a digital transmitting and receiving module (DTRM) suitable for a future full digital active electrically scanned array (AESA). The DTRM uses RF high-speed converters exploiting 5G technologies mainly developed for massive MIMO applications. A C-Band 4-channel AESA prototype was designed, realized, and characterized in far field.
{"title":"Design and characterization of AESA prototype driven by a DTRM","authors":"Pasquale Beneduce, A. Capozzoli, C. Curcio, A. Liseno, Giovanni Petraglia, Gaetano Prisco, Marcello Ranucci, Chiara Sonatore","doi":"10.1109/MN55117.2022.9887647","DOIUrl":"https://doi.org/10.1109/MN55117.2022.9887647","url":null,"abstract":"This paper discusses design and testing of fully-digital direct-conversion array transmitter in C Band developed as a part of a digital transmitting and receiving module (DTRM) suitable for a future full digital active electrically scanned array (AESA). The DTRM uses RF high-speed converters exploiting 5G technologies mainly developed for massive MIMO applications. A C-Band 4-channel AESA prototype was designed, realized, and characterized in far field.","PeriodicalId":148281,"journal":{"name":"2022 IEEE International Symposium on Measurements & Networking (M&N)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114153207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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