Pub Date : 2020-05-26DOI: 10.1109/BlackSeaCom48709.2020.9235000
Eduardo H. P. de Arruda, R. C. Lunardi, Henry C. Nunes, A. Zorzo, Regio A. Michelin
In the last few years, different researchers presented proposals for using blockchain in the Internet of Things (IoT) environments. These proposals consider that IoT environments can be benefited from different blockchain characteristics, such as: resilience, distributed processing, integrity and non-repudiation of produced information. However, researchers faced some challenges to use blockchain in IoT, e.g., latency, hardware and energy constraints, and performance requirements. One of the prominent solutions is the appendable-block blockchain, which uses a hierarchical peer-to-peer (p2p) gateway-based architecture. Additionally, current proposals present simplified evaluation scenarios, usually performed in controlled environments, which do not include important network features, for example, latency. Consequently, a model to evaluate a geographically distributed environment, for example, in a situation in which health data have to be collected from different countries in a pandemic situation, can help to understand the behavior and possible flaws of blockchains. In order to evaluate appendable-block blockchains in a realistic scenario, this paper presents an analysis of different consensus algorithms in geographically distributed hosts, in which latency can impact the performance of main operations in a blockchain, such as block and transaction insertion.
{"title":"Appendable-block Blockchain Evaluation over Geographically-Distributed IoT Networks","authors":"Eduardo H. P. de Arruda, R. C. Lunardi, Henry C. Nunes, A. Zorzo, Regio A. Michelin","doi":"10.1109/BlackSeaCom48709.2020.9235000","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235000","url":null,"abstract":"In the last few years, different researchers presented proposals for using blockchain in the Internet of Things (IoT) environments. These proposals consider that IoT environments can be benefited from different blockchain characteristics, such as: resilience, distributed processing, integrity and non-repudiation of produced information. However, researchers faced some challenges to use blockchain in IoT, e.g., latency, hardware and energy constraints, and performance requirements. One of the prominent solutions is the appendable-block blockchain, which uses a hierarchical peer-to-peer (p2p) gateway-based architecture. Additionally, current proposals present simplified evaluation scenarios, usually performed in controlled environments, which do not include important network features, for example, latency. Consequently, a model to evaluate a geographically distributed environment, for example, in a situation in which health data have to be collected from different countries in a pandemic situation, can help to understand the behavior and possible flaws of blockchains. In order to evaluate appendable-block blockchains in a realistic scenario, this paper presents an analysis of different consensus algorithms in geographically distributed hosts, in which latency can impact the performance of main operations in a blockchain, such as block and transaction insertion.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"1 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":"127103250","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/BlackSeaCom48709.2020.9235008
Ekin Basak Bektas, E. Panayirci
In this paper a new computationally efficient and high performance channel estimation algorithm is proposed for DC-biased optical OFDM (DCO-OFDM) systems in indoor visible light communications (VLC) in the presence of a clipping noise. The sparse structure of the channel is taken into consideration in the channel estimation algorithm. The algorithm has an iterative structure and aims at reducing the effect of the clipping noise, inevitably generated by the DCO-OFDM systems. In te algorithm, the clipping noise is estimated in the time-domain and compensated for its effect in the frequency-domain. The initial values of the channel, including sparse channel path gains and the path delays, are determined by the least-squares (LS) and the ESPRIT algorithms, respectively, by making use of the pilots. Computer simulations indicate that the proposed algorithm converge in 3 iterations at most and yields excellent bit error rate (BER) and mean-square error (MSE) performances for DC-biased optical OFDM (DCO-OFDM) based systems.
{"title":"Sparse Channel Estimation with Clipping Noise in DCO-OFDM Based VLC Systems","authors":"Ekin Basak Bektas, E. Panayirci","doi":"10.1109/BlackSeaCom48709.2020.9235008","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235008","url":null,"abstract":"In this paper a new computationally efficient and high performance channel estimation algorithm is proposed for DC-biased optical OFDM (DCO-OFDM) systems in indoor visible light communications (VLC) in the presence of a clipping noise. The sparse structure of the channel is taken into consideration in the channel estimation algorithm. The algorithm has an iterative structure and aims at reducing the effect of the clipping noise, inevitably generated by the DCO-OFDM systems. In te algorithm, the clipping noise is estimated in the time-domain and compensated for its effect in the frequency-domain. The initial values of the channel, including sparse channel path gains and the path delays, are determined by the least-squares (LS) and the ESPRIT algorithms, respectively, by making use of the pilots. Computer simulations indicate that the proposed algorithm converge in 3 iterations at most and yields excellent bit error rate (BER) and mean-square error (MSE) performances for DC-biased optical OFDM (DCO-OFDM) based systems.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"11 12 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":"125763133","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 : 2020-05-04DOI: 10.1109/BlackSeaCom48709.2020.9235002
Shruti Bothe, Usama Masood, H. Farooq, A. Imran
Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training data.
{"title":"Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks","authors":"Shruti Bothe, Usama Masood, H. Farooq, A. Imran","doi":"10.1109/BlackSeaCom48709.2020.9235002","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235002","url":null,"abstract":"Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training data.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114291582","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 : 2020-05-04DOI: 10.1109/BlackSeaCom48709.2020.9235020
Joel Shodamola, Usama Masood, Marvin Manalastas, A. Imran
Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach.
{"title":"A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters","authors":"Joel Shodamola, Usama Masood, Marvin Manalastas, A. Imran","doi":"10.1109/BlackSeaCom48709.2020.9235020","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235020","url":null,"abstract":"Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117332423","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 : 2020-04-29DOI: 10.1109/BlackSeaCom48709.2020.9234988
E. Kalantari, S. Loyka, H. Yanikomeroglu, A. Yongaçoğlu
An optimal base station (BS) location depends on the traffic (user) distribution, propagation pathloss and many system parameters, which renders its analytical study difficult so that numerical algorithms are widely used instead. In this paper, the problem is studied ana¬lytically. First, it is formulated as a convex optimization problem to minimize the total BS transmit power subject to quality-of-service (QoS) constraints, which also account for fairness among users. Due to its convex nature, Karush-Kuhn-Tucker (KKT) conditions are used to characterize a globally-optimum location as a convex combination of user locations, where convex weights depend on user parameters, pathloss exponent and overall geometry of the problem. Based on this characterization, a number of closed-form solutions are obtained. In particular, the optimum BS location is the mean of user locations in the case of free-space propagation and identical user parameters. If the user set is symmetric (as defined in the paper), the optimal BS location is independent of pathloss exponent, which is not the case in general. The analytical results show the impact of propagation conditions as well as system and user parameters on optimal BS location and can be used to develop design guidelines.
{"title":"Optimal Location of Cellular Base Station via Convex Optimization","authors":"E. Kalantari, S. Loyka, H. Yanikomeroglu, A. Yongaçoğlu","doi":"10.1109/BlackSeaCom48709.2020.9234988","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234988","url":null,"abstract":"An optimal base station (BS) location depends on the traffic (user) distribution, propagation pathloss and many system parameters, which renders its analytical study difficult so that numerical algorithms are widely used instead. In this paper, the problem is studied ana¬lytically. First, it is formulated as a convex optimization problem to minimize the total BS transmit power subject to quality-of-service (QoS) constraints, which also account for fairness among users. Due to its convex nature, Karush-Kuhn-Tucker (KKT) conditions are used to characterize a globally-optimum location as a convex combination of user locations, where convex weights depend on user parameters, pathloss exponent and overall geometry of the problem. Based on this characterization, a number of closed-form solutions are obtained. In particular, the optimum BS location is the mean of user locations in the case of free-space propagation and identical user parameters. If the user set is symmetric (as defined in the paper), the optimal BS location is independent of pathloss exponent, which is not the case in general. The analytical results show the impact of propagation conditions as well as system and user parameters on optimal BS location and can be used to develop design guidelines.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125510532","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 : 2020-04-26DOI: 10.1109/BlackSeaCom48709.2020.9234956
F. Kara, Hakan Kaya
Non-orthogonal multiple access (NOMA) is very promising for future wireless systems thanks to its spectral efficiency. In NOMA schemes, the imperfect successive interference canceler (SIC) has dominant effect on the error performances. In addition to this imperfect SIC effect, the error performance will get worse with the channel estimation errors just as in all wireless communications systems. However, all literature has been devoted to analyze error performance of NOMA systems with the perfect channel state information (CSI) at the receivers which is very strict/unreasonable assumption. In this paper, we analyze error performance of NOMA systems with imperfect SIC and CSI, as a much more practical scenario. We derive exact bit error probabilities (BEPs) in closed-forms. All theoretical analysis is validated via computer simulations. Then, we discuss optimum power allocation for user fairness in terms of error performances of users and propose a novel power allocation scheme which achieves maximum user fairness.
{"title":"Error Probability Analysis of Non-Orthogonal Multiple Access with Channel Estimation Errors","authors":"F. Kara, Hakan Kaya","doi":"10.1109/BlackSeaCom48709.2020.9234956","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234956","url":null,"abstract":"Non-orthogonal multiple access (NOMA) is very promising for future wireless systems thanks to its spectral efficiency. In NOMA schemes, the imperfect successive interference canceler (SIC) has dominant effect on the error performances. In addition to this imperfect SIC effect, the error performance will get worse with the channel estimation errors just as in all wireless communications systems. However, all literature has been devoted to analyze error performance of NOMA systems with the perfect channel state information (CSI) at the receivers which is very strict/unreasonable assumption. In this paper, we analyze error performance of NOMA systems with imperfect SIC and CSI, as a much more practical scenario. We derive exact bit error probabilities (BEPs) in closed-forms. All theoretical analysis is validated via computer simulations. Then, we discuss optimum power allocation for user fairness in terms of error performances of users and propose a novel power allocation scheme which achieves maximum user fairness.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114152008","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 : 2020-04-04DOI: 10.1109/BlackSeaCom48709.2020.9235024
Askar Mandali Kundu, Rudrashish Pal, Mayank Kumar, Sreejith T. Veetil
With Full Duplex (FD), wireless terminal is capable of transmitting and receiving data simultaneously in the same frequency resources, however, it introduces self interference and co-channel interference. Even though various signal processing techniques are emerged to cancel the self interference, the bottleneck for FD performance in cellular systems is the co-channel interference from the other uplink and downlink signals. In this work we have studied both the uplink and downlink performances of a FD cellular network, where users employ fractional power control in uplink. We use Matern Cluster Process to model the network, which provides a tractable and realistic model to characterize the user-base station distances which are needed for uplink power control. Based on the obtained coverage probabilities, rates and their robust approximations, we show that while FD improves downlink performance, it severely hurts the uplink performance. Also, we provide a trade-off between uplink and downlink performances. Our study suggests dense deployment of low power base stations can improve the performance of FD system.
{"title":"Uplink and Downlink Performance Bounds for Full Duplex Cellular Networks","authors":"Askar Mandali Kundu, Rudrashish Pal, Mayank Kumar, Sreejith T. Veetil","doi":"10.1109/BlackSeaCom48709.2020.9235024","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9235024","url":null,"abstract":"With Full Duplex (FD), wireless terminal is capable of transmitting and receiving data simultaneously in the same frequency resources, however, it introduces self interference and co-channel interference. Even though various signal processing techniques are emerged to cancel the self interference, the bottleneck for FD performance in cellular systems is the co-channel interference from the other uplink and downlink signals. In this work we have studied both the uplink and downlink performances of a FD cellular network, where users employ fractional power control in uplink. We use Matern Cluster Process to model the network, which provides a tractable and realistic model to characterize the user-base station distances which are needed for uplink power control. Based on the obtained coverage probabilities, rates and their robust approximations, we show that while FD improves downlink performance, it severely hurts the uplink performance. Also, we provide a trade-off between uplink and downlink performances. Our study suggests dense deployment of low power base stations can improve the performance of FD system.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125690066","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 : 2019-10-29DOI: 10.1109/BlackSeaCom48709.2020.9234989
M. S. Iqbal, Yalçin Sadi, S. Ergen
In this paper, we consider a full duplex wireless powered communication network where multiple users with radio frequency energy harvesting capability communicate to an energy broadcasting hybrid access point. We investigate the minimum length scheduling and sum throughput maximization problems considering on-off transmission scheme in which users either transmit at a constant power or remain silent. For minimum length scheduling problem, we propose a polynomial-time optimal scheduling algorithm. For sum throughput maximization, we first derive the characteristics of an optimal schedule and then to avoid intractable complexity. We propose a polynomial-time heuristic algorithm which is illustrated to perform nearly optimal through numerical analysis.
{"title":"Optimal On-Off Transmission Schemes for Full Duplex Wireless Powered Communication Networks","authors":"M. S. Iqbal, Yalçin Sadi, S. Ergen","doi":"10.1109/BlackSeaCom48709.2020.9234989","DOIUrl":"https://doi.org/10.1109/BlackSeaCom48709.2020.9234989","url":null,"abstract":"In this paper, we consider a full duplex wireless powered communication network where multiple users with radio frequency energy harvesting capability communicate to an energy broadcasting hybrid access point. We investigate the minimum length scheduling and sum throughput maximization problems considering on-off transmission scheme in which users either transmit at a constant power or remain silent. For minimum length scheduling problem, we propose a polynomial-time optimal scheduling algorithm. For sum throughput maximization, we first derive the characteristics of an optimal schedule and then to avoid intractable complexity. We propose a polynomial-time heuristic algorithm which is illustrated to perform nearly optimal through numerical analysis.","PeriodicalId":186939,"journal":{"name":"2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)","volume":"25 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126172706","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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