Pub Date : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9234994
E. Avdotin, D. Bankov, E. Khorov, A. Lyakhov
In 2019 IEEE 802 LAN/MAN Standards Committee started the development of the next major amendment of the Wi-Fi standard: the IEEE 802.11be, also known as Wi-Fi 7. This new amendment will introduce many new functions and will improve the existing ones that will make Wi-Fi more efficient in many new scenarios. One of the scenarios is the service of Real-Time Applications with strict requirements on latency and reliability of communications. Providing low latencies can be challenging in Wi-Fi because of the unlicensed spectrum and related interference from neighboring devices. In this paper, we consider the usage of OFDMA transmissions for Real-Time Applications and design resource allocation algorithms that can provide the required latency and reliability in the presence of interference.
{"title":"Resource Allocation Strategies for Real-Time Applications in Wi-Fi 7","authors":"E. Avdotin, D. Bankov, E. Khorov, A. Lyakhov","doi":"10.1109/BlackSeaCom48709.2020.9234994","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234994","url":null,"abstract":"In 2019 IEEE 802 LAN/MAN Standards Committee started the development of the next major amendment of the Wi-Fi standard: the IEEE 802.11be, also known as Wi-Fi 7. This new amendment will introduce many new functions and will improve the existing ones that will make Wi-Fi more efficient in many new scenarios. One of the scenarios is the service of Real-Time Applications with strict requirements on latency and reliability of communications. Providing low latencies can be challenging in Wi-Fi because of the unlicensed spectrum and related interference from neighboring devices. In this paper, we consider the usage of OFDMA transmissions for Real-Time Applications and design resource allocation algorithms that can provide the required latency and reliability in the presence of interference.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125027449","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9234964
A. Yudnikov, D. Zabirov, V. Lyashev, Mikhail Kirichenko
Doppler spread appears when user equipment (UE) moves relative to gNodeB base station (gNB) in multipath propagation environment. It results in phase rotation of a transmitted signal, demodulation errors and channel capacity decreasing. The article discusses the utilization of UE location and velocity obtained from UE on-board sensors (e.g. Global Positioning System (GPS) and Inertial Measurement Unit (IMU) sensors in the case of smartphones) as well as scatters positions data acquired from map services for Doppler spread estimation. The algorithm for its impact minimization is presented. The computational experiments conducted in MATLAB using Quadriga channel model demonstrate the efficiency of proposed technique in terms of Modulation Error Ratio (MER).
{"title":"Doppler Spread Impact Minimization via Terminal Sensors Usage","authors":"A. Yudnikov, D. Zabirov, V. Lyashev, Mikhail Kirichenko","doi":"10.1109/BlackSeaCom48709.2020.9234964","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234964","url":null,"abstract":"Doppler spread appears when user equipment (UE) moves relative to gNodeB base station (gNB) in multipath propagation environment. It results in phase rotation of a transmitted signal, demodulation errors and channel capacity decreasing. The article discusses the utilization of UE location and velocity obtained from UE on-board sensors (e.g. Global Positioning System (GPS) and Inertial Measurement Unit (IMU) sensors in the case of smartphones) as well as scatters positions data acquired from map services for Doppler spread estimation. The algorithm for its impact minimization is presented. The computational experiments conducted in MATLAB using Quadriga channel model demonstrate the efficiency of proposed technique in terms of Modulation Error Ratio (MER).","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131137390","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9235001
L. Globa, Nataliia Gvozdetska
The energy consumption of modern data centers is currently growing rapidly, due to the development of new services and the spread of the ICT (Information & Communication Technologies) in all areas of human life. It causes a huge demand in new energy efficient computing technologies which would simultaneously improve data processing performance meeting the requirements of SLA.
{"title":"Energy Efficient Workload Processing Technology for Distributed Computing Environment","authors":"L. Globa, Nataliia Gvozdetska","doi":"10.1109/BlackSeaCom48709.2020.9235001","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235001","url":null,"abstract":"The energy consumption of modern data centers is currently growing rapidly, due to the development of new services and the spread of the ICT (Information & Communication Technologies) in all areas of human life. It causes a huge demand in new energy efficient computing technologies which would simultaneously improve data processing performance meeting the requirements of SLA.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130810219","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9235019
Ampratwum Isaac Owusu, A. Nayak
In recent years, there has been a sharp increase in IoT devices. Majority of these IoT devices have strict QoS requirements. This has made it very difficult for network providers to provide good network solutions whiles keeping cost in check. To meet the QoS demands in IoT networks, a new paradigm, SDN-IoT, leveraging the advantages of SDN architecture on IoT networks have been proposed to improve network quality. The programmability of the SDN controller allows the application of Machine learning in networks. This paper proposes a Machine learning model that classifies traffic in SDN-IoT networks for traffic engineering. The classification process compares the random forest algorithm, decision tree algorithm, and the K-nearest neighbors’ algorithm. The paper also compares the impact of two feature selection methods, Sequential Feature Selection (SFS) and Shapley additive explanations (SHAP) on the accuracies of the classifiers to reduce the number of features needed for classification. The algorithms are accessed based on their accuracy and F1 score. The best performing algorithm is random forest classifier with SFS which achieves accuracy of 0.833 with six features.
{"title":"An Intelligent Traffic Classification in SDN-IoT: A Machine Learning Approach","authors":"Ampratwum Isaac Owusu, A. Nayak","doi":"10.1109/BlackSeaCom48709.2020.9235019","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235019","url":null,"abstract":"In recent years, there has been a sharp increase in IoT devices. Majority of these IoT devices have strict QoS requirements. This has made it very difficult for network providers to provide good network solutions whiles keeping cost in check. To meet the QoS demands in IoT networks, a new paradigm, SDN-IoT, leveraging the advantages of SDN architecture on IoT networks have been proposed to improve network quality. The programmability of the SDN controller allows the application of Machine learning in networks. This paper proposes a Machine learning model that classifies traffic in SDN-IoT networks for traffic engineering. The classification process compares the random forest algorithm, decision tree algorithm, and the K-nearest neighbors’ algorithm. The paper also compares the impact of two feature selection methods, Sequential Feature Selection (SFS) and Shapley additive explanations (SHAP) on the accuracies of the classifiers to reduce the number of features needed for classification. The algorithms are accessed based on their accuracy and F1 score. The best performing algorithm is random forest classifier with SFS which achieves accuracy of 0.833 with six features.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126341450","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}
In order to improve the spectrum sensing performance in the fading channel, a real-time likelihood ratio-based sensing algorithm is proposed. In the algorithm, we set the decision threshold according to the weighted factors of false alarm probability and detection probability. And then, we count the likelihood ratio of the cyclic spectrum peak of the signal received, which is based on the historical information of cognitive networks and the real-time information of the primary user and channel. We compare the likelihood ratio with the decision threshold to make the decision whether the primary user is present or absent. It overcomes the effect of channel fading on the spectrum sensing, and effectively trades off between the false alarm probability and detection probability of cognitive networks. Simulation results show that the algorithm proposed has about 3 dB signal-noise-ratio advantage over the conventional one in the fading channel.
{"title":"Spectrum Sensing Algorithm Based on Real-time Likelihood Ratio in Cognitive Networks","authors":"Yanyan Ge, Jiajun Chen, Dongmei Li, Shibing Zhang, Lili Guo, Yonghong Chen","doi":"10.1109/BlackSeaCom48709.2020.9235013","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235013","url":null,"abstract":"In order to improve the spectrum sensing performance in the fading channel, a real-time likelihood ratio-based sensing algorithm is proposed. In the algorithm, we set the decision threshold according to the weighted factors of false alarm probability and detection probability. And then, we count the likelihood ratio of the cyclic spectrum peak of the signal received, which is based on the historical information of cognitive networks and the real-time information of the primary user and channel. We compare the likelihood ratio with the decision threshold to make the decision whether the primary user is present or absent. It overcomes the effect of channel fading on the spectrum sensing, and effectively trades off between the false alarm probability and detection probability of cognitive networks. Simulation results show that the algorithm proposed has about 3 dB signal-noise-ratio advantage over the conventional one in the fading channel.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129293904","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9234991
I. Nevliudov, O. Filipenko, O. Sychova
In this paper, the dependence of the optical signal loss on the displacement of photonic crystal fibers during the connection is considered. The method based on the principles of matched filtration and autoconvolution was used for the positioning of photonic crystal fibers and quality control of their connection. This method has high noise immunity. Experimental studies have shown that even with the presence of significant distortions of the optical field image of the photonic crystal fiber, this method allows to determine with sufficient accuracy the position of the photonic crystal fiber axis.
{"title":"Mathematical Technique for Photonic Crystal Fibers Automated Positioning Module","authors":"I. Nevliudov, O. Filipenko, O. Sychova","doi":"10.1109/BlackSeaCom48709.2020.9234991","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234991","url":null,"abstract":"In this paper, the dependence of the optical signal loss on the displacement of photonic crystal fibers during the connection is considered. The method based on the principles of matched filtration and autoconvolution was used for the positioning of photonic crystal fibers and quality control of their connection. This method has high noise immunity. Experimental studies have shown that even with the presence of significant distortions of the optical field image of the photonic crystal fiber, this method allows to determine with sufficient accuracy the position of the photonic crystal fiber axis.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124294357","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9234966
Talha Akyıldız, T. Duman
We propose a search-free precoder selection method with neural networks motivated by the fact that large codebook sizes are adopted in 5G New Radio (5G-NR). The proposed method does not require an explicit codebook search unlike the traditional selection algorithms. Instead, it aims at finding the precoder matrix index that maximizes the corresponding channel capacity using a neural network directly. The network is trained off-line using extensive simulated data with the underlying channel statistics; however, the actual selection algorithm is based on simple calculations with the neural network, hence it is feasible for real time implementation. We demonstrate that the proposed search-free selection algorithm is highly efficient, i.e., it results in a performance very close to optimal precoder in the codebook while its complexity is significantly lower. Simulations with realistic channel models of 5G-NR corroborate these observations as well. We also show that pruning of the trained neural network gives a way to achieve further complexity reduction with a very small reduction in the system performance.
{"title":"Search-Free Precoder Selection for 5G New Radio using Neural Networks","authors":"Talha Akyıldız, T. Duman","doi":"10.1109/BlackSeaCom48709.2020.9234966","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234966","url":null,"abstract":"We propose a search-free precoder selection method with neural networks motivated by the fact that large codebook sizes are adopted in 5G New Radio (5G-NR). The proposed method does not require an explicit codebook search unlike the traditional selection algorithms. Instead, it aims at finding the precoder matrix index that maximizes the corresponding channel capacity using a neural network directly. The network is trained off-line using extensive simulated data with the underlying channel statistics; however, the actual selection algorithm is based on simple calculations with the neural network, hence it is feasible for real time implementation. We demonstrate that the proposed search-free selection algorithm is highly efficient, i.e., it results in a performance very close to optimal precoder in the codebook while its complexity is significantly lower. Simulations with realistic channel models of 5G-NR corroborate these observations as well. We also show that pruning of the trained neural network gives a way to achieve further complexity reduction with a very small reduction in the system performance.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125220509","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9234957
Halil Ertan, Selver Ezgi Küçükbay, Amir Yavariabdi, Nuri Kangöz, Ali Emre Tiryaki, Iren Berk Özalp
Anomaly detection in Digital Subscriber Line (DSL) networks is a vital task to immediately detect unusual network behavior caused by cyber security threats, faulty hardware or software. Generally, to make this process automatic, state-of-the-art methods use machine learning techniques to analyze data collected from either Customer Premises Equipment (CPE) or from devices in Access Network. In contrast to the existing methods, this paper utilizes network traffic data collected from multiple static Broadband Network Gateways (BNGs) which are core network devices at Network Service Provider (NSP). To automatically detect anomalies in BNG traffic data, a new framework is proposed which consists of three steps: data acquisition, feature extraction, and modeling anomalies using a random forest-based framework. The proposed method is compared with state-of-the-art methods and the results show the effectiveness and robustness of our method.
{"title":"Anomaly Detection on Broadband Network Gateway","authors":"Halil Ertan, Selver Ezgi Küçükbay, Amir Yavariabdi, Nuri Kangöz, Ali Emre Tiryaki, Iren Berk Özalp","doi":"10.1109/BlackSeaCom48709.2020.9234957","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234957","url":null,"abstract":"Anomaly detection in Digital Subscriber Line (DSL) networks is a vital task to immediately detect unusual network behavior caused by cyber security threats, faulty hardware or software. Generally, to make this process automatic, state-of-the-art methods use machine learning techniques to analyze data collected from either Customer Premises Equipment (CPE) or from devices in Access Network. In contrast to the existing methods, this paper utilizes network traffic data collected from multiple static Broadband Network Gateways (BNGs) which are core network devices at Network Service Provider (NSP). To automatically detect anomalies in BNG traffic data, a new framework is proposed which consists of three steps: data acquisition, feature extraction, and modeling anomalies using a random forest-based framework. The proposed method is compared with state-of-the-art methods and the results show the effectiveness and robustness of our method.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115423916","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9235003
K. Topyan, M. Ulema
5G based Fixed Wireless Access (FWA) technology is considered a superior choice in offering broadband communication services in a variety of markets. However, there are several different ways of deploying 5G WFA. This paper discusses various deployment scenarios, business opportunities, and competition for the FWA use case. Deploying 5G FWA networks is not a small undertaking; in addition to many designs, development, migration, and deployment challenges, there are also many economic and financial options that must be carefully explored. The paper identifies four deployment alternatives based on the spectrum, infrastructure, region, and mobility requirements. The paper also introduces a framework for identifying the best financing option for each of the four alternatives.
{"title":"Architectural and Financial Considerations for Deploying 5G Based Fixed Wireless Access","authors":"K. Topyan, M. Ulema","doi":"10.1109/BlackSeaCom48709.2020.9235003","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235003","url":null,"abstract":"5G based Fixed Wireless Access (FWA) technology is considered a superior choice in offering broadband communication services in a variety of markets. However, there are several different ways of deploying 5G WFA. This paper discusses various deployment scenarios, business opportunities, and competition for the FWA use case. Deploying 5G FWA networks is not a small undertaking; in addition to many designs, development, migration, and deployment challenges, there are also many economic and financial options that must be carefully explored. The paper identifies four deployment alternatives based on the spectrum, infrastructure, region, and mobility requirements. The paper also introduces a framework for identifying the best financing option for each of the four alternatives.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133976832","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 : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9235005
E. Gelenbe, Mihajlo Pavloski
We propose an access control scheme to improve the security of a cross border health data access running under distributed systems such as OpenNCP that cooperate through message exchanges. The scheme allows messages that request data and services to cross different regional or national nodes after they are verified for security issues both by the sender and the receiver. It rejects those access requests that are detected to be malicious, and consequently throttles down the flow of permit messages via explicit feedback from threat detection software placed at the sender and the receiver. An analytical model is used to evaluate the message delay to determine the overhead caused by the enhanced security system, and numerical results illustrate the impact of attack rates and on the system’s performance.
{"title":"Performance of a Security Control Scheme for a Health Data Exchange System","authors":"E. Gelenbe, Mihajlo Pavloski","doi":"10.1109/BlackSeaCom48709.2020.9235005","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235005","url":null,"abstract":"We propose an access control scheme to improve the security of a cross border health data access running under distributed systems such as OpenNCP that cooperate through message exchanges. The scheme allows messages that request data and services to cross different regional or national nodes after they are verified for security issues both by the sender and the receiver. It rejects those access requests that are detected to be malicious, and consequently throttles down the flow of permit messages via explicit feedback from threat detection software placed at the sender and the receiver. An analytical model is used to evaluate the message delay to determine the overhead caused by the enhanced security system, and numerical results illustrate the impact of attack rates and on the system’s performance.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115498943","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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