Merima Kulin | Ghent University (original) (raw)

Papers by Merima Kulin

This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated... more This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heteroge... more Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

MDPI Sensors

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heteroge... more Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

Predictable network performance is key in many low-power wireless sensor network applications. In... more Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by the MAC layer. Our approach is data-driven and consists of three steps: extensive experiments for data collection , offline modeling and trace-driven performance evaluation. From our experiments and analysis, we find that a neural networks prediction model shows best performance.

2013 36th International Convention on Information and Communication Technology Electronics and Microelectronics, May 20, 2013

This paper presents an approach to the development of custom agents and their integration with ne... more This paper presents an approach to the development of custom agents and their integration with network management systems. For the development of agents is given one approach, and according to this approach an implementation of the agent using Open Dynamic Management Kit (OpenDMK) libraries in the Java programming language is performed. Within the agents are implemented all standard Simple Network Management Protocol (SNMP) functionality -reading values, setting values and traps sending. Finally, the integration is performed with several network management systems such as Zenoss and Cacti. Tests have confirmed the success of this integration, thus verifying the proposed approach.

Software defined radio (SDR) technology enables implementation of wireless devices that support m... more Software defined radio (SDR) technology enables implementation of wireless devices that support multiple air-interfaces and modulation formats, which is very important if consider proliferation of wireless standards. To enable such functionality SDR is using reconfigurable hardware platform such as Field Programmable Gate Array (FPGA). In this paper, we present design procedure and implementation result of SDR based QPSK modulator on Altera Cyclone IV FPGA. For design and implementation of QPSK modulator we used Altera DSP Builder Tool combined with Matlab/Simulink, Modelsim and Quartus II design tools. As reconfigurable hardware platform we used Altera DE2-115 development and education board with AD/DA daughter card. Software and Hardware-in-the-loop (HIL) simulation was conducted before hardware implementation and verification of designed system. This method of design makes implementation of SDR based modulators simpler ad faster.

2012 IX International Symposium on Telecommunications (BIHTEL), 2012

Data science or " data-driven research " is a research approach that uses real-life data to gain ... more Data science or " data-driven research " is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

Books by Merima Kulin

MSc thesis, 2012

Prisutna je sve veća potražnja i interes za multimedijalnim sadržajem i raznim oblicima multimedi... more Prisutna je sve veća potražnja i interes za multimedijalnim sadržajem i raznim oblicima multimedijalnih usluga. U kontekstu multimedije u telekomunikacijskoj industriji često se spominje IMS (IP Multimedia Subsystem) kao arhitektura za isporuku multimedijalnih sadržaja preko IP mreže. IMS je inicijalno dizajniran kao prijedlog radnog okvira za mobilne mreže treće generacije. Ideja je bila uz uslugu prenosa podataka osigurati kvalitetnu govornu komunikaciju preko IP mreže (Voice over IP- VoIP) uz naplatu govornih poziva. Kao posljedica toga nastala je kompletna platforma za pružanje raznoraznih IP multimedijalnih usluga i to preko različitih pristupnih mreža. U isto vrijeme nastala je platforma koja omogućava brzo, efikasno i jeftino uvođenje novih naprednih usluga uz mogućnost zadržavanja već postojećih usluga. Budući da je realizacija VoIP usluge, temeljene na principima SIP signalizacionog protokola, preko IMS dobro poznata i opisana u brojnoj literaturi i kroz veliki broj radova, cilj ovog rada je dati prijedlog realizacije video multimedijalne usluge preko IMS u smislu da postoji medijski server koji na zahtjev klijenta isporučuje multimedijalni sadržaj. Ova usluga razmatrana je u kontekstu IPTV VoD (Video on Demand) usluge gdje se klijentima u IMS mreži na zahtjev pruža određeni video sadržaj iz liste njemu ponuđenih sadržaja. S tim u vezi, najprije su opisani osnovni protokoli koji se koriste za prenos i upravljanje multimedijalnim sadržajem, kao i način rada IMS sistema sa osvrtom na njegove funkcionalne elemente. Potom je opisan jedan pristup integracije VoD multimedijalne usluge sa IMS platformom kao i primjer njegove praktične realizacije. U radu su istaknuti ključni elementi konfiguracije predloženog sistema i stečena iskustva tokom rada. Takođe su dati prijedlozi za buduće izvedbe i prodiskutovane su potencijalne nove usluge koje je moguće ostvariti korištenjem koncepata IMS arhitekture za proširenje implementirane multimedijalne usluge u nove naprednije usluge.

This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated... more This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heteroge... more Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

MDPI Sensors

Driven by the fast growth of wireless communication, the trend of sharing spectrum among heteroge... more Driven by the fast growth of wireless communication, the trend of sharing spectrum among heterogeneous technologies becomes increasingly dominant. Identifying concurrent technologies is an important step towards efficient spectrum sharing. However, due to the complexity of recognition algorithms and the strict condition of sampling speed, communication systems capable of recognizing signals other than their own type are extremely rare. This work proves that multi-model distribution of the received signal strength indicator (RSSI) is related to the signals' modulation schemes and medium access mechanisms, and RSSI from different technologies may exhibit highly distinctive features. A distinction is made between technologies with a streaming or a non-streaming property, and appropriate feature spaces can be established either by deriving parameters such as packet duration from RSSI or directly using RSSI's probability distribution. An experimental study shows that even RSSI acquired at a sub-Nyquist sampling rate is able to provide sufficient features to differentiate technologies such as Wi-Fi, Long Term Evolution (LTE), Digital Video Broadcasting-Terrestrial (DVB-T) and Bluetooth. The usage of the RSSI distribution-based feature space is illustrated via a sample algorithm. Experimental evaluation indicates that more than 92% accuracy is achieved with the appropriate configuration. As the analysis of RSSI distribution is straightforward and less demanding in terms of system requirements, we believe it is highly valuable for recognition of wideband technologies on constrained devices in the context of dynamic spectrum access.

Predictable network performance is key in many low-power wireless sensor network applications. In... more Predictable network performance is key in many low-power wireless sensor network applications. In this paper, we use machine learning as an effective technique for real-time characterization of the communication performance as observed by the MAC layer. Our approach is data-driven and consists of three steps: extensive experiments for data collection , offline modeling and trace-driven performance evaluation. From our experiments and analysis, we find that a neural networks prediction model shows best performance.

2013 36th International Convention on Information and Communication Technology Electronics and Microelectronics, May 20, 2013

This paper presents an approach to the development of custom agents and their integration with ne... more This paper presents an approach to the development of custom agents and their integration with network management systems. For the development of agents is given one approach, and according to this approach an implementation of the agent using Open Dynamic Management Kit (OpenDMK) libraries in the Java programming language is performed. Within the agents are implemented all standard Simple Network Management Protocol (SNMP) functionality -reading values, setting values and traps sending. Finally, the integration is performed with several network management systems such as Zenoss and Cacti. Tests have confirmed the success of this integration, thus verifying the proposed approach.

Software defined radio (SDR) technology enables implementation of wireless devices that support m... more Software defined radio (SDR) technology enables implementation of wireless devices that support multiple air-interfaces and modulation formats, which is very important if consider proliferation of wireless standards. To enable such functionality SDR is using reconfigurable hardware platform such as Field Programmable Gate Array (FPGA). In this paper, we present design procedure and implementation result of SDR based QPSK modulator on Altera Cyclone IV FPGA. For design and implementation of QPSK modulator we used Altera DSP Builder Tool combined with Matlab/Simulink, Modelsim and Quartus II design tools. As reconfigurable hardware platform we used Altera DE2-115 development and education board with AD/DA daughter card. Software and Hardware-in-the-loop (HIL) simulation was conducted before hardware implementation and verification of designed system. This method of design makes implementation of SDR based modulators simpler ad faster.

2012 IX International Symposium on Telecommunications (BIHTEL), 2012

Data science or " data-driven research " is a research approach that uses real-life data to gain ... more Data science or " data-driven research " is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

MSc thesis, 2012

Prisutna je sve veća potražnja i interes za multimedijalnim sadržajem i raznim oblicima multimedi... more Prisutna je sve veća potražnja i interes za multimedijalnim sadržajem i raznim oblicima multimedijalnih usluga. U kontekstu multimedije u telekomunikacijskoj industriji često se spominje IMS (IP Multimedia Subsystem) kao arhitektura za isporuku multimedijalnih sadržaja preko IP mreže. IMS je inicijalno dizajniran kao prijedlog radnog okvira za mobilne mreže treće generacije. Ideja je bila uz uslugu prenosa podataka osigurati kvalitetnu govornu komunikaciju preko IP mreže (Voice over IP- VoIP) uz naplatu govornih poziva. Kao posljedica toga nastala je kompletna platforma za pružanje raznoraznih IP multimedijalnih usluga i to preko različitih pristupnih mreža. U isto vrijeme nastala je platforma koja omogućava brzo, efikasno i jeftino uvođenje novih naprednih usluga uz mogućnost zadržavanja već postojećih usluga. Budući da je realizacija VoIP usluge, temeljene na principima SIP signalizacionog protokola, preko IMS dobro poznata i opisana u brojnoj literaturi i kroz veliki broj radova, cilj ovog rada je dati prijedlog realizacije video multimedijalne usluge preko IMS u smislu da postoji medijski server koji na zahtjev klijenta isporučuje multimedijalni sadržaj. Ova usluga razmatrana je u kontekstu IPTV VoD (Video on Demand) usluge gdje se klijentima u IMS mreži na zahtjev pruža određeni video sadržaj iz liste njemu ponuđenih sadržaja. S tim u vezi, najprije su opisani osnovni protokoli koji se koriste za prenos i upravljanje multimedijalnim sadržajem, kao i način rada IMS sistema sa osvrtom na njegove funkcionalne elemente. Potom je opisan jedan pristup integracije VoD multimedijalne usluge sa IMS platformom kao i primjer njegove praktične realizacije. U radu su istaknuti ključni elementi konfiguracije predloženog sistema i stečena iskustva tokom rada. Takođe su dati prijedlozi za buduće izvedbe i prodiskutovane su potencijalne nove usluge koje je moguće ostvariti korištenjem koncepata IMS arhitekture za proširenje implementirane multimedijalne usluge u nove naprednije usluge.