Pub Date : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945440
Arindam Ghosh, Anubrata Sanyal, Amartya Chakraborty, P. K. Sharma, M. Saha, S. Nandi, Sujoy Saha
Human activity recognition is an important problem in health care, ambient-assisted living, surveillance-based security, etc. and has crucial applications in smart environment. A non-invasive, automated system for monitoring human activity using array of heterogeneous ultrasonic sensors has been proposed in this work. Ultrasonic sensors are widely used for distance measurement in many applications. In the proposed system experiments have been conducted using ten volunteers in a controlled laboratory environment. The data collection unit has two kinds of setups of ultrasonic sensors: the former with five HC-SR04 sensors, and the latter with four HC-SR04 ultrasonic sensors and an LV-MaxSonar-EZ0 sensor. The proposed method is found capable of detecting standing, sitting and falling of a person, and also the movements in different directions. Based on the collected data, we have performed classification analysis using multiple machine learning algorithms. The experimental results show 81% to 90% correct detection of different activities of the volunteers.
{"title":"On automatizing recognition of multiple human activities using ultrasonic sensor grid","authors":"Arindam Ghosh, Anubrata Sanyal, Amartya Chakraborty, P. K. Sharma, M. Saha, S. Nandi, Sujoy Saha","doi":"10.1109/COMSNETS.2017.7945440","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945440","url":null,"abstract":"Human activity recognition is an important problem in health care, ambient-assisted living, surveillance-based security, etc. and has crucial applications in smart environment. A non-invasive, automated system for monitoring human activity using array of heterogeneous ultrasonic sensors has been proposed in this work. Ultrasonic sensors are widely used for distance measurement in many applications. In the proposed system experiments have been conducted using ten volunteers in a controlled laboratory environment. The data collection unit has two kinds of setups of ultrasonic sensors: the former with five HC-SR04 sensors, and the latter with four HC-SR04 ultrasonic sensors and an LV-MaxSonar-EZ0 sensor. The proposed method is found capable of detecting standing, sitting and falling of a person, and also the movements in different directions. Based on the collected data, we have performed classification analysis using multiple machine learning algorithms. The experimental results show 81% to 90% correct detection of different activities of the volunteers.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121341133","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945460
Satadal Sengupta
Proliferation of online social networks (OSNs) has resulted in an unprecedented surge in the volume of multimedia content consumed by users on a daily basis. Popular OSNs such as Facebook enable users to view and share embedded videos and images on their feeds, which increases visibility, prompting repeated requests for the same piece of content. Maintaining desirable quality of service for all users becomes challenging in such a scenario, especially when low-bandwidth cellular network is being used for data download. Such problems have prompted the research community to focus heavily on the emerging paradigm of Information-or Content-Centric Networking (ICN/CCN), where in-network content management (e.g., content distribution, caching, etc.) forms the crux of an enhanced user experience. In this abstract, we argue that social dynamics among OSN users can provide concrete hints regarding future popularity of content. We propose a strategy to identify viewing and sharing patterns of Facebook users served by a cellular base station, by analyzing network traffic. We utilize these patterns to infer social dynamics among cellular users (mapped to cellphone numbers). We validate our strategy with proof-of-concept experiments on real data, and extensive simulations on a simulation framework proposed by us.
{"title":"Predicting social dynamics based on network traffic analysis for CCN/ICN management","authors":"Satadal Sengupta","doi":"10.1109/COMSNETS.2017.7945460","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945460","url":null,"abstract":"Proliferation of online social networks (OSNs) has resulted in an unprecedented surge in the volume of multimedia content consumed by users on a daily basis. Popular OSNs such as Facebook enable users to view and share embedded videos and images on their feeds, which increases visibility, prompting repeated requests for the same piece of content. Maintaining desirable quality of service for all users becomes challenging in such a scenario, especially when low-bandwidth cellular network is being used for data download. Such problems have prompted the research community to focus heavily on the emerging paradigm of Information-or Content-Centric Networking (ICN/CCN), where in-network content management (e.g., content distribution, caching, etc.) forms the crux of an enhanced user experience. In this abstract, we argue that social dynamics among OSN users can provide concrete hints regarding future popularity of content. We propose a strategy to identify viewing and sharing patterns of Facebook users served by a cellular base station, by analyzing network traffic. We utilize these patterns to infer social dynamics among cellular users (mapped to cellphone numbers). We validate our strategy with proof-of-concept experiments on real data, and extensive simulations on a simulation framework proposed by us.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"106 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123390176","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945417
M. Giri, S. Jyothi, C. Vorugunti
Online social message classification is an important task for E-Commerce companies to mine and classify the customer opinions. In this paper, we have proposed a first of its kind of an efficient message classification algorithm which is independent of tweet content and considers the set of followers who will retweet during the retweet peaks. By including the followers who will retweet during retweet peaks will get a better sampling of the followers set and reduces the computation and storage complexities drastically. Also, we have eliminated the heavy weight operations like DTW to perform the comparison task between the test vector and training vector. The preliminary experiment results authorize that the proposed system attains an accuracy of 95.96% in classification of tweet messages.
{"title":"A novel online social network (Twitter)message (Tweet)classifier based on message diffusion in the network","authors":"M. Giri, S. Jyothi, C. Vorugunti","doi":"10.1109/COMSNETS.2017.7945417","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945417","url":null,"abstract":"Online social message classification is an important task for E-Commerce companies to mine and classify the customer opinions. In this paper, we have proposed a first of its kind of an efficient message classification algorithm which is independent of tweet content and considers the set of followers who will retweet during the retweet peaks. By including the followers who will retweet during retweet peaks will get a better sampling of the followers set and reduces the computation and storage complexities drastically. Also, we have eliminated the heavy weight operations like DTW to perform the comparison task between the test vector and training vector. The preliminary experiment results authorize that the proposed system attains an accuracy of 95.96% in classification of tweet messages.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122683835","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945361
Yan Shi, S. Biswas
This paper presents a framework for a firewall to analyze and block BitTorrent file-sharing protocol using Traffic Analysis (TA) methods. BitTorrent traffic can be a concern of network administrators and is a valuable target for TA based investigation. In this work, the ability of a TA based classifier to identify the existence of BitTorrent traffic is tested under the condition that it is not only encrypted by a Virtual Private Network (VPN) tunnel but also mixed with other types of network traffic (including video streaming traffic and web traffic). The TA based classifier is comprised of 2 steps: a pre-filtering step and the actual classification step. The test results show that not only is it possible for the TA based classifier to distinguish BitTorrent traffic from the encrypted mixture, but the classifier can also tell the source of the streaming video in the mixture with high accuracy. The 2-step classifier is also proven to have boosted the accuracy by 15%. The results indicate the possibility of implementing a TA based firewall for monitoring BitTorrent traffic.
{"title":"Using traffic analysis for simultaneous detection of BitTorrent and streaming video traffic sources","authors":"Yan Shi, S. Biswas","doi":"10.1109/COMSNETS.2017.7945361","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945361","url":null,"abstract":"This paper presents a framework for a firewall to analyze and block BitTorrent file-sharing protocol using Traffic Analysis (TA) methods. BitTorrent traffic can be a concern of network administrators and is a valuable target for TA based investigation. In this work, the ability of a TA based classifier to identify the existence of BitTorrent traffic is tested under the condition that it is not only encrypted by a Virtual Private Network (VPN) tunnel but also mixed with other types of network traffic (including video streaming traffic and web traffic). The TA based classifier is comprised of 2 steps: a pre-filtering step and the actual classification step. The test results show that not only is it possible for the TA based classifier to distinguish BitTorrent traffic from the encrypted mixture, but the classifier can also tell the source of the streaming video in the mixture with high accuracy. The 2-step classifier is also proven to have boosted the accuracy by 15%. The results indicate the possibility of implementing a TA based firewall for monitoring BitTorrent traffic.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125719564","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945457
S. Woo
NFV (Network Function Virtualization) promises reducing management cost by moving network functions (NFs) from proprietary hardware to software on VMs (Virtual Machines) running on commodity servers [1]. NFV promises the benefit of virtualization to network applications. New NFs are easily deployed as VMs without specially packaged hardware. Virtualization ensures high availability through failover, and high resource utilization through elastic scaling of VM instances.
{"title":"Design and implementation of a network function framework for performance scalability","authors":"S. Woo","doi":"10.1109/COMSNETS.2017.7945457","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945457","url":null,"abstract":"NFV (Network Function Virtualization) promises reducing management cost by moving network functions (NFs) from proprietary hardware to software on VMs (Virtual Machines) running on commodity servers [1]. NFV promises the benefit of virtualization to network applications. New NFs are easily deployed as VMs without specially packaged hardware. Virtualization ensures high availability through failover, and high resource utilization through elastic scaling of VM instances.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126616612","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945383
Mohanad Al-Ibadi, A. Dutta
Vehicle to Infrastructure (V2I) channels are particularly difficult to analyze because of high mobility and localized scattering from nearby vehicles and road-side features. The spatio-temporal variation of the scattering environment makes the channel a non-stationary stochastic process, which renders conventional, receiver-side channel conditioning techniques ineffective for this emerging application. Our work takes a radically different approach to introduce predictive analytics at the Road-Side Unit (RSU) to proactively compensate for channel variations over time and frequency while precisely fitting into contemporary protocols like Dedicated Short Range Communication (DSRC) and Wireless Access in Vehicular Environment (WAVE). By assimilating the channel state feedback built into these protocols, we employ an iterative learning algorithm to gather localized knowledge of the channel profile. This acquired knowledge is used to pre-condition the downlink waveform to lower the Bit Error Rate (BER) by ≈ 100 times, when compared to the current vehicular communication standards even at a relatively high Signal to Noise (SNR) of 17 dB. Further, our algorithm is able to predict the non-stationary V2I channel with an average absolute error of 10−2 in dense scattering environment.
{"title":"Predictive analytics for non-stationary V2I channel","authors":"Mohanad Al-Ibadi, A. Dutta","doi":"10.1109/COMSNETS.2017.7945383","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945383","url":null,"abstract":"Vehicle to Infrastructure (V2I) channels are particularly difficult to analyze because of high mobility and localized scattering from nearby vehicles and road-side features. The spatio-temporal variation of the scattering environment makes the channel a non-stationary stochastic process, which renders conventional, receiver-side channel conditioning techniques ineffective for this emerging application. Our work takes a radically different approach to introduce predictive analytics at the Road-Side Unit (RSU) to proactively compensate for channel variations over time and frequency while precisely fitting into contemporary protocols like Dedicated Short Range Communication (DSRC) and Wireless Access in Vehicular Environment (WAVE). By assimilating the channel state feedback built into these protocols, we employ an iterative learning algorithm to gather localized knowledge of the channel profile. This acquired knowledge is used to pre-condition the downlink waveform to lower the Bit Error Rate (BER) by ≈ 100 times, when compared to the current vehicular communication standards even at a relatively high Signal to Noise (SNR) of 17 dB. Further, our algorithm is able to predict the non-stationary V2I channel with an average absolute error of 10−2 in dense scattering environment.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127362261","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}
Cloud federation has emerged as a new paradigm in which different service providers collaborate to overcome limitation of cloud resource during sudden spikes in demand and improve quality of service (QoS) for delivered cloud services. The growing number of cloud federation to provide cloud services has made service selection a complex task due to varied nature of parameters like price, QoS and trust of different federations. In this context, formal decision making methodology is required to find the best federation which can deliver a services with high QoS and trust in a cost effective way. The proposed model conducts a multi-criteria decision analysis to select best cloud federation in specific time period in accordance with user preference over each parameter. The experimental result and analysis validate the effectiveness of our proposed model.
{"title":"Multi-criteria based federation selection in cloud","authors":"Benay Kumar Ray, Asif Iqbal Middya, Sarbani Roy, Sunirmal Khatua","doi":"10.1109/COMSNETS.2017.7945375","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945375","url":null,"abstract":"Cloud federation has emerged as a new paradigm in which different service providers collaborate to overcome limitation of cloud resource during sudden spikes in demand and improve quality of service (QoS) for delivered cloud services. The growing number of cloud federation to provide cloud services has made service selection a complex task due to varied nature of parameters like price, QoS and trust of different federations. In this context, formal decision making methodology is required to find the best federation which can deliver a services with high QoS and trust in a cost effective way. The proposed model conducts a multi-criteria decision analysis to select best cloud federation in specific time period in accordance with user preference over each parameter. The experimental result and analysis validate the effectiveness of our proposed model.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126897389","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945430
Punith B. Kotagi, Gowri Asaithambi
Most of the Indian urban roads are bi-directional in nature consists of mix up of different vehicle types with weak lane discipline. A mathematical or analytical treatment of such condition is found infeasible due to its complex nature. Hence, simulation has become inevitable tool for analysis and interpretation of such real world situations. There are only few studies which focuses exclusively on developing a bidirectional traffic simulation model considering the longitudinal and lateral behaviour of vehicles for urban undivided roads. With the above motivation, the present study focuses on development of simulation models for bi-directional mixed traffic flow using object oriented programming (OOP) concepts. The proposed model would be of significant assistance to traffic engineers while making key decisions in traffic control and management policies.
{"title":"Simulation framework for modeling bidirectional mixed traffic","authors":"Punith B. Kotagi, Gowri Asaithambi","doi":"10.1109/COMSNETS.2017.7945430","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945430","url":null,"abstract":"Most of the Indian urban roads are bi-directional in nature consists of mix up of different vehicle types with weak lane discipline. A mathematical or analytical treatment of such condition is found infeasible due to its complex nature. Hence, simulation has become inevitable tool for analysis and interpretation of such real world situations. There are only few studies which focuses exclusively on developing a bidirectional traffic simulation model considering the longitudinal and lateral behaviour of vehicles for urban undivided roads. With the above motivation, the present study focuses on development of simulation models for bi-directional mixed traffic flow using object oriented programming (OOP) concepts. The proposed model would be of significant assistance to traffic engineers while making key decisions in traffic control and management policies.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122692796","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945372
Paul C. Wood, S. Bagchi, Alefiya Hussain
The smart grid (SG) promises to revolutionize power grid efficiency and reliability by bringing wide-area control and coordination between both power producers and widely distributed consumers. Such improvements, however, depend on reliable communication infrastructures for cooperation, thus creating an interdependence between wide area networks and the power grid. Real-time pricing (RTP) systems coordinate producers and consumers via price signals, and recent research has shown that network disruptions in RTPs can significantly harm or disrupt power grid operation. In this paper, we theorize and demonstrate how strategic network disruptions can further disrupt grid operations in ways that are profitable to a strategic adversary. We quantify the economic impacts of a strategic adversary that utilizes denial of service (DoS) attacks to gain a financial advantage in the power market, without compromising the integrity of the RTP signals. The adversary develops a strategy of when and where to launch DoS attacks by utilizing our algorithm that optimizes prices in her favor. A defender minimizes these financial gains by obfuscating the network targets, reducing the effectiveness of attacks. Our results provide insights to the dependability of RTP when deployed across disruptable wide-area best-effort communication networks.
{"title":"Profiting from attacks on real-time price communications in smart grids","authors":"Paul C. Wood, S. Bagchi, Alefiya Hussain","doi":"10.1109/COMSNETS.2017.7945372","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945372","url":null,"abstract":"The smart grid (SG) promises to revolutionize power grid efficiency and reliability by bringing wide-area control and coordination between both power producers and widely distributed consumers. Such improvements, however, depend on reliable communication infrastructures for cooperation, thus creating an interdependence between wide area networks and the power grid. Real-time pricing (RTP) systems coordinate producers and consumers via price signals, and recent research has shown that network disruptions in RTPs can significantly harm or disrupt power grid operation. In this paper, we theorize and demonstrate how strategic network disruptions can further disrupt grid operations in ways that are profitable to a strategic adversary. We quantify the economic impacts of a strategic adversary that utilizes denial of service (DoS) attacks to gain a financial advantage in the power market, without compromising the integrity of the RTP signals. The adversary develops a strategy of when and where to launch DoS attacks by utilizing our algorithm that optimizes prices in her favor. A defender minimizes these financial gains by obfuscating the network targets, reducing the effectiveness of attacks. Our results provide insights to the dependability of RTP when deployed across disruptable wide-area best-effort communication networks.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123885691","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 : 1900-01-01DOI: 10.1109/COMSNETS.2017.7945409
K. P. K. Reddy, Yoganandam Yeleswarapu, S. Darak
In this paper, a USRP based testbed has been developed for evaluating the performance of cumulant feature based automatic modulation classifier (AMC) in a real radio environment. The proposed testbed consists of conventional radio transmitter with a capability to choose any one of BPSK, QPSK, QAM16 and QAM64 modulation schemes. The receiver extracts appropriate order cumulants from the received signal which are then used as features by support vector machine (SVM) based machine learning classifier. Experimental results demonstrate that the Probability of correct classification (Pec) in varying signal-to-noise ratios (SNR) follow the same increasing pattern as in case of simulation results.
{"title":"Performance evaluation of cumulant feature based automatic modulation classifier on USRP testbec","authors":"K. P. K. Reddy, Yoganandam Yeleswarapu, S. Darak","doi":"10.1109/COMSNETS.2017.7945409","DOIUrl":"https://doi.org/10.1109/COMSNETS.2017.7945409","url":null,"abstract":"In this paper, a USRP based testbed has been developed for evaluating the performance of cumulant feature based automatic modulation classifier (AMC) in a real radio environment. The proposed testbed consists of conventional radio transmitter with a capability to choose any one of BPSK, QPSK, QAM16 and QAM64 modulation schemes. The receiver extracts appropriate order cumulants from the received signal which are then used as features by support vector machine (SVM) based machine learning classifier. Experimental results demonstrate that the Probability of correct classification (Pec) in varying signal-to-noise ratios (SNR) follow the same increasing pattern as in case of simulation results.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127669936","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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