Pub Date : 2020-02-01DOI: 10.1109/NCC48643.2020.9056064
Avijit Mandal, Avhishek Chatterjee, A. Thangaraj
Motivated by the classical synchronization problem and emerging applications in bioinformatics, we study noisy deletion channels in a regime of practical interest: short code length, low decoding complexity and low SNR. Our work is inspired by an important insight from information theory and Markov chains: appropriately parametrized Markov codewords can correct deletions and errors (due to noise) simultaneously. We extend this idea to practice by developing a low complexity decoder for short Markov codes, which displays competitive performance in simulations at low SNRs. Our decoder design combines the sequence prediction capability of recurrent neural networks with the assured performance of maximum a posteriori (MAP) decoders like the BCJR decoder.
{"title":"Noisy Deletion, Markov Codes and Deep Decoding","authors":"Avijit Mandal, Avhishek Chatterjee, A. Thangaraj","doi":"10.1109/NCC48643.2020.9056064","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056064","url":null,"abstract":"Motivated by the classical synchronization problem and emerging applications in bioinformatics, we study noisy deletion channels in a regime of practical interest: short code length, low decoding complexity and low SNR. Our work is inspired by an important insight from information theory and Markov chains: appropriately parametrized Markov codewords can correct deletions and errors (due to noise) simultaneously. We extend this idea to practice by developing a low complexity decoder for short Markov codes, which displays competitive performance in simulations at low SNRs. Our decoder design combines the sequence prediction capability of recurrent neural networks with the assured performance of maximum a posteriori (MAP) decoders like the BCJR decoder.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527811","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056038
R. Hatibaruah, V. K. Nath, D. Hazarika
In this paper, we introduce a new feature descriptor 3D local oriented zigzag fused pattern (3D-LOZFP) for retrieval of medical CT images. The existing local patterns such as local binary pattern (LBP), local tetra pattern (LTrP) etc. captures the relationship between the reference and its surrounding pixels in a circular fashion in a 2D plane. The proposed descriptor encodes the relation between the reference pixel and its neighboring pixels using three unique 3D zigzag patterns in four different directions in a 3D plane. Therefore a total of 12 effective 3D zigzag patterns are introduced to capture the relationship between the reference and its neighbors in a 3D plane. The 3D plane is constructed by passing the input image through a Gaussian filter bank producing multiple filtered images containing multi-scale information. The feature dimensions are reduced using quantization and a fusion based scheme. The retrieval performance of the proposed descriptor is investigated by conducting experiments on two benchmark CT image datasets and then compared it with several recent techniques. The experimental results in terms of average retrieval precision (ARP) and average retrieval recall (ARR) across two databases validate the retrieval supremacy of the proposed descriptor over other techniques in CT image retrieval.
{"title":"Biomedical CT Image Retrieval Using 3D Local Oriented Zigzag Fused Pattern","authors":"R. Hatibaruah, V. K. Nath, D. Hazarika","doi":"10.1109/NCC48643.2020.9056038","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056038","url":null,"abstract":"In this paper, we introduce a new feature descriptor 3D local oriented zigzag fused pattern (3D-LOZFP) for retrieval of medical CT images. The existing local patterns such as local binary pattern (LBP), local tetra pattern (LTrP) etc. captures the relationship between the reference and its surrounding pixels in a circular fashion in a 2D plane. The proposed descriptor encodes the relation between the reference pixel and its neighboring pixels using three unique 3D zigzag patterns in four different directions in a 3D plane. Therefore a total of 12 effective 3D zigzag patterns are introduced to capture the relationship between the reference and its neighbors in a 3D plane. The 3D plane is constructed by passing the input image through a Gaussian filter bank producing multiple filtered images containing multi-scale information. The feature dimensions are reduced using quantization and a fusion based scheme. The retrieval performance of the proposed descriptor is investigated by conducting experiments on two benchmark CT image datasets and then compared it with several recent techniques. The experimental results in terms of average retrieval precision (ARP) and average retrieval recall (ARR) across two databases validate the retrieval supremacy of the proposed descriptor over other techniques in CT image retrieval.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130984587","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056058
M. D. Lakshmi, S. Santhanam
Underwater navigation and intelligent object recognition require robust machine learning algorithms to operate in turbid water. Modern life created the man-made pollution in oceans, rivers, and lakes, which contaminate our water resources. Despite environmental regulations solid waste in the form of trash, litter and garbage are thrown directly into sea spoiling the existence of underwater living creatures. The underwater vehicle can be used for survey purposes. The key challenge of underwater image-based localization comes from the unstructured nature of the seabed terrain. So, there is a need for robust detection of the features in such environments is essential. Hence, this paper proposes the automated underwater image recognition detector for submersible imagery. We train a Convolutional neural Network (ConvNet) to classify input 64 × 64 images and considered the classifier as an object feature detector. The features of the image from underwater-bed can be extracted and forward into a network. The output of the three-layer ConvNet with deeply connected network results in a probability distribution over N classes. A Stochastic gradient descent with ADAM optimizer uses the squared gradients to scale the learning rate and reduces the difference between the actual and predicted output. The evaluations are done on the precision, recall, F-Score, macro and weighted Average accuracy for both the detectors. It is observed that our proposed network, achieved an overall accuracy of 93.9 % for correct detections with a binary detector and 90.1% with a multiclass detector compared to existing detectors.
{"title":"Underwater Image Recognition Detector using Deep ConvNet","authors":"M. D. Lakshmi, S. Santhanam","doi":"10.1109/NCC48643.2020.9056058","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056058","url":null,"abstract":"Underwater navigation and intelligent object recognition require robust machine learning algorithms to operate in turbid water. Modern life created the man-made pollution in oceans, rivers, and lakes, which contaminate our water resources. Despite environmental regulations solid waste in the form of trash, litter and garbage are thrown directly into sea spoiling the existence of underwater living creatures. The underwater vehicle can be used for survey purposes. The key challenge of underwater image-based localization comes from the unstructured nature of the seabed terrain. So, there is a need for robust detection of the features in such environments is essential. Hence, this paper proposes the automated underwater image recognition detector for submersible imagery. We train a Convolutional neural Network (ConvNet) to classify input 64 × 64 images and considered the classifier as an object feature detector. The features of the image from underwater-bed can be extracted and forward into a network. The output of the three-layer ConvNet with deeply connected network results in a probability distribution over N classes. A Stochastic gradient descent with ADAM optimizer uses the squared gradients to scale the learning rate and reduces the difference between the actual and predicted output. The evaluations are done on the precision, recall, F-Score, macro and weighted Average accuracy for both the detectors. It is observed that our proposed network, achieved an overall accuracy of 93.9 % for correct detections with a binary detector and 90.1% with a multiclass detector compared to existing detectors.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115789539","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056030
Sujatha Allipuram, Shabnam Parmar, Parthajit Mohapatra, N. Pappas, S. Chakrabarti
In this paper, we study the role of multiple antennas in mitigating jamming attack under Rayleigh fading environment with random arrival of data at the transmitter. The jammer is assumed to have energy harvesting capability with infinite battery size. The outage probabilities under jamming attack are derived for Rayleigh fading scenario with different assumptions on the number of antennas at the transmitter and receiver. The outage probability is also derived for the Alamouti space-time code under the jamming attack. The average service rate and delay performance of the system are characterized with random arrival of data and energy at the transmitter and jammer, respectively. The derived results help to explore the benefits of using multiple antennas in improving average service rate and delay of the system under jamming attack. It is also found that exploitation of space and time diversity with the use of space-time code can improve the performance of the system significantly even under the jamming attack.
{"title":"Mitigating Jamming Attacks in a MIMO System with Bursty Traffic","authors":"Sujatha Allipuram, Shabnam Parmar, Parthajit Mohapatra, N. Pappas, S. Chakrabarti","doi":"10.1109/NCC48643.2020.9056030","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056030","url":null,"abstract":"In this paper, we study the role of multiple antennas in mitigating jamming attack under Rayleigh fading environment with random arrival of data at the transmitter. The jammer is assumed to have energy harvesting capability with infinite battery size. The outage probabilities under jamming attack are derived for Rayleigh fading scenario with different assumptions on the number of antennas at the transmitter and receiver. The outage probability is also derived for the Alamouti space-time code under the jamming attack. The average service rate and delay performance of the system are characterized with random arrival of data and energy at the transmitter and jammer, respectively. The derived results help to explore the benefits of using multiple antennas in improving average service rate and delay of the system under jamming attack. It is also found that exploitation of space and time diversity with the use of space-time code can improve the performance of the system significantly even under the jamming attack.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124811713","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 : 2020-02-01DOI: 10.1109/ncc48643.2020.9056017
{"title":"Twenty Sixth National Conference on Communications","authors":"","doi":"10.1109/ncc48643.2020.9056017","DOIUrl":"https://doi.org/10.1109/ncc48643.2020.9056017","url":null,"abstract":"","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116626251","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056016
P. Barik, Chetna Singhal, R. Datta
5G multimedia mobile wireless network is designed to support on-demand encoding of rich mobile multimedia content for heterogeneous users. Due to the heterogeneity of the users, adaptive multimedia services are essential to provide a satisfactory Quality of Experience (QoE). In this paper, we propose a utility-based dynamic adaptive multimedia streaming scheme, named UDAS, for heterogeneous users that helps in extending the battery life of the low-battery users and also uses the bandwidth of the wireless channel efficiently. At each scheduling interval, the adaptation algorithm considers four utility functions of the user devices, namely, quality utility, power consumption utility, packet error ratio utility, and remaining battery utility to adapt the data rate dynamically. We formulate an optimization problem to maximize a joint utility function of these four utilities. The solution of the problem provides the best adaptive multimedia content that is selected for transmission to the end-users in every scheduling interval. The mobile edge computing (MEC) server situated at the base station performs an on-demand HEVC (high efficiency video coding) encoding of videos and select the best suitable videos for different users. Simulation results verify the improved performance of UDAS in terms of saved battery energy and the number of unserved low-battery users in comparison with state-of-the-art non-adaptive multimedia streaming schemes and a popular scheme ESDOAS from the literature.
{"title":"Energy-efficient User-centric Dynamic Adaptive Multimedia Streaming in 5G Cellular Networks","authors":"P. Barik, Chetna Singhal, R. Datta","doi":"10.1109/NCC48643.2020.9056016","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056016","url":null,"abstract":"5G multimedia mobile wireless network is designed to support on-demand encoding of rich mobile multimedia content for heterogeneous users. Due to the heterogeneity of the users, adaptive multimedia services are essential to provide a satisfactory Quality of Experience (QoE). In this paper, we propose a utility-based dynamic adaptive multimedia streaming scheme, named UDAS, for heterogeneous users that helps in extending the battery life of the low-battery users and also uses the bandwidth of the wireless channel efficiently. At each scheduling interval, the adaptation algorithm considers four utility functions of the user devices, namely, quality utility, power consumption utility, packet error ratio utility, and remaining battery utility to adapt the data rate dynamically. We formulate an optimization problem to maximize a joint utility function of these four utilities. The solution of the problem provides the best adaptive multimedia content that is selected for transmission to the end-users in every scheduling interval. The mobile edge computing (MEC) server situated at the base station performs an on-demand HEVC (high efficiency video coding) encoding of videos and select the best suitable videos for different users. Simulation results verify the improved performance of UDAS in terms of saved battery energy and the number of unserved low-battery users in comparison with state-of-the-art non-adaptive multimedia streaming schemes and a popular scheme ESDOAS from the literature.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128948103","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056085
Vipin Kumar, Basant Subba
E-commerce and social networking sites are very much dependent on the available data which can be analyzed in real time to predict their future business strategies. However, analyzing huge amount of data manually is not possible in time context of business. Therefore, automated sentimental analysis, which can automatically determine the sentiments from the text data corpus plays an important role in today's world. Many sentimental analysis frameworks with state of the art results have been proposed in the literature. However, many of these frameworks have low accuracy on the textual data corpus contains emoticons and special texts. In addition, many of these frameworks are also energy and computation intensive with which puts limitation in their real time deployment. In this paper, we aim to address these issues by proposing a novel sentimental analysis framework based on Support Vector Machine (SVM). The proposed framework uses a novel technique to tokenize the text documents, wherein stop words, special characters, emoticons present in the text documents are eliminated. In addition, words with similar meanings and annotations are clubbed together into one type, using the concept of stemming. The pre-processed tokenized documents are then vectorized into n-gram integers vectors using the ‘TfidfVectorizer’ for use as input to the SVM based machine learning classifier model. Experimental results on the Amazon's electronics item review dataset and IMDB's movie review data corpus show that the proposed sentimental analysis framework achieves high performance compared to other similar frameworks proposed in the literature.
{"title":"A TfidfVectorizer and SVM based sentiment analysis framework for text data corpus","authors":"Vipin Kumar, Basant Subba","doi":"10.1109/NCC48643.2020.9056085","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056085","url":null,"abstract":"E-commerce and social networking sites are very much dependent on the available data which can be analyzed in real time to predict their future business strategies. However, analyzing huge amount of data manually is not possible in time context of business. Therefore, automated sentimental analysis, which can automatically determine the sentiments from the text data corpus plays an important role in today's world. Many sentimental analysis frameworks with state of the art results have been proposed in the literature. However, many of these frameworks have low accuracy on the textual data corpus contains emoticons and special texts. In addition, many of these frameworks are also energy and computation intensive with which puts limitation in their real time deployment. In this paper, we aim to address these issues by proposing a novel sentimental analysis framework based on Support Vector Machine (SVM). The proposed framework uses a novel technique to tokenize the text documents, wherein stop words, special characters, emoticons present in the text documents are eliminated. In addition, words with similar meanings and annotations are clubbed together into one type, using the concept of stemming. The pre-processed tokenized documents are then vectorized into n-gram integers vectors using the ‘TfidfVectorizer’ for use as input to the SVM based machine learning classifier model. Experimental results on the Amazon's electronics item review dataset and IMDB's movie review data corpus show that the proposed sentimental analysis framework achieves high performance compared to other similar frameworks proposed in the literature.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130806010","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056028
Samarjeet Das, S. Dandapat
Analysis of heart sounds (HSs) plays a vital role in the early detection and diagnosis of cardiovascular diseases. In this paper, we propose a multi-component oscillatory model for the representation of both normal and pathological heart sound segments. A half-period sine wave is fitted between every two consecutive zero-crossing points to extract parameters for the proposed model. The segment-representation gets improved with the recursive use of multiple half-wave oscillations. The proposed method is tested and validated with the Computing in Cardiology Challenge (CinC) 2016 database, available publicly on the Physionet archive. The efficiency of the model is demonstrated for the synthesis of HS segments. The performance results of synthesis show that the multi-component oscillatory model provides a highly accurate approximation of the original HS segments. Further, the model parameters are employed for the classification of normal and abnormal HS segments. The proposed method achieves a better performance using support vector machine classifier with RBF kernel.
{"title":"Synthesis and Classification of Heart Sounds Using Multi-component Oscillatory Model","authors":"Samarjeet Das, S. Dandapat","doi":"10.1109/NCC48643.2020.9056028","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056028","url":null,"abstract":"Analysis of heart sounds (HSs) plays a vital role in the early detection and diagnosis of cardiovascular diseases. In this paper, we propose a multi-component oscillatory model for the representation of both normal and pathological heart sound segments. A half-period sine wave is fitted between every two consecutive zero-crossing points to extract parameters for the proposed model. The segment-representation gets improved with the recursive use of multiple half-wave oscillations. The proposed method is tested and validated with the Computing in Cardiology Challenge (CinC) 2016 database, available publicly on the Physionet archive. The efficiency of the model is demonstrated for the synthesis of HS segments. The performance results of synthesis show that the multi-component oscillatory model provides a highly accurate approximation of the original HS segments. Further, the model parameters are employed for the classification of normal and abnormal HS segments. The proposed method achieves a better performance using support vector machine classifier with RBF kernel.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121403585","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 : 2020-02-01DOI: 10.1109/NCC48643.2020.9056080
V. M. Sruthi, Abhishek Chakraborty, B. Thanudas, S. Sreelal, B. S. Manoj
System security is becoming an indispensable part of our daily life due to the rapid proliferation of unknown malware attacks. Recent malware found to have a very complicated structure that is hard to detect by the traditional malware detection techniques such as antivirus, intrusion detection systems, and network scanners. In this paper, we propose a complex network-based malware detection technique, Malware Detection using Complex Network (MDCN), that considers Application Program Interface Call Transition Matrix (API-CTM) to generate complex network topology and then extracts various feature set by analyzing different metrics of the complex network to distinguish malware and benign applications. The generated feature set is then sent to several machine learning classifiers, which include naive-Bayes, support vector machine, random forest, and multilayer perceptron, to comparatively analyze the performance of MDCN-based technique. The analysis reveals that MDCN shows higher accuracy, with lower false-positive cases, when the multilayer perceptron-based classifier is used for the detection of malware. MDCN technique can efficiently be deployed in the design of an integrated enterprise network security system.
{"title":"An Efficient Malware Detection Technique using Complex Network-based Approach","authors":"V. M. Sruthi, Abhishek Chakraborty, B. Thanudas, S. Sreelal, B. S. Manoj","doi":"10.1109/NCC48643.2020.9056080","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056080","url":null,"abstract":"System security is becoming an indispensable part of our daily life due to the rapid proliferation of unknown malware attacks. Recent malware found to have a very complicated structure that is hard to detect by the traditional malware detection techniques such as antivirus, intrusion detection systems, and network scanners. In this paper, we propose a complex network-based malware detection technique, Malware Detection using Complex Network (MDCN), that considers Application Program Interface Call Transition Matrix (API-CTM) to generate complex network topology and then extracts various feature set by analyzing different metrics of the complex network to distinguish malware and benign applications. The generated feature set is then sent to several machine learning classifiers, which include naive-Bayes, support vector machine, random forest, and multilayer perceptron, to comparatively analyze the performance of MDCN-based technique. The analysis reveals that MDCN shows higher accuracy, with lower false-positive cases, when the multilayer perceptron-based classifier is used for the detection of malware. MDCN technique can efficiently be deployed in the design of an integrated enterprise network security system.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126770660","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 : 2020-01-15DOI: 10.1109/NCC48643.2020.9056048
P. Sharma, B. Yogesh, Deepika Gupta
In this paper, we consider an overlay satellite-terrestrial network (OSTN) where an opportunistically selected terrestrial IoT network assist primary satellite communications as well as access the spectrum for its own communications in the presence of combined interference from extra-terrestrial and terrestrial sources. Hereby, a power domain multiplexing is adopted by the IoT network by splitting its power appropriately among the satellite and IoT signals. Relying upon an amplify-and-forward (AF)-based opportunistic IoT network selection strategy that minimizes the outage probability (OP) of satellite network, we derive the closed-form lower bound OP expressions for both the satellite and IoT networks. We further derive the corresponding asymptotic OP expressions to examine the achievable diversity order of two networks. We show that the proposed OSTN with adaptive power splitting factor benefits IoT network while guaranteeing the quality of service (QoS) of satellite network. We verify the numerical results by simulations.
{"title":"Internet of Things-Enabled Overlay Satellite-Terrestrial Networks in the Presence of Interference","authors":"P. Sharma, B. Yogesh, Deepika Gupta","doi":"10.1109/NCC48643.2020.9056048","DOIUrl":"https://doi.org/10.1109/NCC48643.2020.9056048","url":null,"abstract":"In this paper, we consider an overlay satellite-terrestrial network (OSTN) where an opportunistically selected terrestrial IoT network assist primary satellite communications as well as access the spectrum for its own communications in the presence of combined interference from extra-terrestrial and terrestrial sources. Hereby, a power domain multiplexing is adopted by the IoT network by splitting its power appropriately among the satellite and IoT signals. Relying upon an amplify-and-forward (AF)-based opportunistic IoT network selection strategy that minimizes the outage probability (OP) of satellite network, we derive the closed-form lower bound OP expressions for both the satellite and IoT networks. We further derive the corresponding asymptotic OP expressions to examine the achievable diversity order of two networks. We show that the proposed OSTN with adaptive power splitting factor benefits IoT network while guaranteeing the quality of service (QoS) of satellite network. We verify the numerical results by simulations.","PeriodicalId":183772,"journal":{"name":"2020 National Conference on Communications (NCC)","volume":"417 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123092085","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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