Pub Date : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024524
Changhao Liu, Yangan Mo, B. Gao, Tingting Zhang
Appropriate traffic coordination at road intersections plays an important role in modern intelligent transportation systems (ITS). In this paper, we try to propose a low complexity and scalable intersection coordination framework based on a five-collision-set model. Aiming at the essential non-convex problem, we try to reformulate the original problem into an mixed binary integer programming (MBIP) one by proper relaxations. Furthermore, the proposed coordination strategy can be easily extended to the complicated multi-lane intersections. Numeric results verifies the feasibility and scalability of the proposed traffic coordination strategy.
{"title":"Low Complexity Coordination Strategies at Multi-Lane Intersections","authors":"Changhao Liu, Yangan Mo, B. Gao, Tingting Zhang","doi":"10.1109/GCWkshps45667.2019.9024524","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024524","url":null,"abstract":"Appropriate traffic coordination at road intersections plays an important role in modern intelligent transportation systems (ITS). In this paper, we try to propose a low complexity and scalable intersection coordination framework based on a five-collision-set model. Aiming at the essential non-convex problem, we try to reformulate the original problem into an mixed binary integer programming (MBIP) one by proper relaxations. Furthermore, the proposed coordination strategy can be easily extended to the complicated multi-lane intersections. Numeric results verifies the feasibility and scalability of the proposed traffic coordination strategy.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327905","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024525
Hany Khalifa, R. Jäntti
Quantum sensing based on entangled photon pairs is gradually establishing itself as a cornerstone in modern communication networks. The unrivalled capability of quantum sensing techniques in distilling signals plagued by noise, renders them suitable for deployment in backscatter communication networks. Several attempts have been made recently to utilize pairs of entangled signal- idler photons, to enhance the sensitivity of photo- detection in backscatter networks. However, these efforts have always assumed the lossless retention of the idler mode, which is a challenging task from a practical perspective. In this study we examine the extent to which quantum correlations remain after retaining the idler mode in a lossy memory element, while the signal photon propagates through a lossy thermal channel as usual. We also examine briefly two different detection methods, and estimate the received signal-to-noise ratio for them both. This new proposed model is one step further towards realizing quantum backscatter communication.
{"title":"Retrieving Quantum Backscattered Signals in the Presence of Noise","authors":"Hany Khalifa, R. Jäntti","doi":"10.1109/GCWkshps45667.2019.9024525","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024525","url":null,"abstract":"Quantum sensing based on entangled photon pairs is gradually establishing itself as a cornerstone in modern communication networks. The unrivalled capability of quantum sensing techniques in distilling signals plagued by noise, renders them suitable for deployment in backscatter communication networks. Several attempts have been made recently to utilize pairs of entangled signal- idler photons, to enhance the sensitivity of photo- detection in backscatter networks. However, these efforts have always assumed the lossless retention of the idler mode, which is a challenging task from a practical perspective. In this study we examine the extent to which quantum correlations remain after retaining the idler mode in a lossy memory element, while the signal photon propagates through a lossy thermal channel as usual. We also examine briefly two different detection methods, and estimate the received signal-to-noise ratio for them both. This new proposed model is one step further towards realizing quantum backscatter communication.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123701776","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024530
Teng Li, Jianfeng Ma, Xindi Ma, Chenyang Gao, He Wang, Chengyan Ma, Jing Yu, D. Lu, Jiawei Zhang
Due to the open nature of the UAV network, its external environment is complex, potentially risky, and vulnerable to a variety of attacks. The attacker will destroy the connection between the drones, intercept the information transmitted on the communication link, and interfere with the mission messages between the drones, thus requiring us to encrypt the data between the UAV nodes. The current mainstream UAV network communication uses a symmetric encryption algorithm, which is faster but less robust against network packet loss problems. The innovation of this paper is to propose an encryption communication scheme based on SM4 algorithm, and improve the stream encryption mode (CTR) of SM4 algorithm. Compared with the traditional SM4 CTR algorithm, the encryption and decryption speed is improved by 7.7%, and ChaCha20 flow Encryption algorithms are more tolerant of packet loss.
{"title":"Lightweight Secure Communication Mechanism Towards UAV Networks","authors":"Teng Li, Jianfeng Ma, Xindi Ma, Chenyang Gao, He Wang, Chengyan Ma, Jing Yu, D. Lu, Jiawei Zhang","doi":"10.1109/GCWkshps45667.2019.9024530","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024530","url":null,"abstract":"Due to the open nature of the UAV network, its external environment is complex, potentially risky, and vulnerable to a variety of attacks. The attacker will destroy the connection between the drones, intercept the information transmitted on the communication link, and interfere with the mission messages between the drones, thus requiring us to encrypt the data between the UAV nodes. The current mainstream UAV network communication uses a symmetric encryption algorithm, which is faster but less robust against network packet loss problems. The innovation of this paper is to propose an encryption communication scheme based on SM4 algorithm, and improve the stream encryption mode (CTR) of SM4 algorithm. Compared with the traditional SM4 CTR algorithm, the encryption and decryption speed is improved by 7.7%, and ChaCha20 flow Encryption algorithms are more tolerant of packet loss.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123712564","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024419
Hanlin Mou, Yuhong Liu, Li Wang
Caching has attracted a wide range of research interests due to its ability to reduce traffic load and latency. However, reasonable caching strategies are required to further improve caching efficiency and system performance. However, how to predict the content popularity evolution has become a major issue in the design of caching strategies. Moreover, user locations is a non-negligible factor since it is often coupled with content popularity in the practical scenarios, e.g., content popularity may vary along with user's location. Therefore, in this paper, a caching scheme is proposed based on a novel prediction model which jointly considers mobility and content popularity. In specific, Long Short-Term Memory (LSTM) method is utilized as a prediction tool due to its advantage of processing long sequences. Experimental results demonstrate the effectiveness of our proposed scheme with higher prediction accuracy and improved caching efficiency.
{"title":"LSTM for Mobility Based Content Popularity Prediction in Wireless Caching Networks","authors":"Hanlin Mou, Yuhong Liu, Li Wang","doi":"10.1109/GCWkshps45667.2019.9024419","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024419","url":null,"abstract":"Caching has attracted a wide range of research interests due to its ability to reduce traffic load and latency. However, reasonable caching strategies are required to further improve caching efficiency and system performance. However, how to predict the content popularity evolution has become a major issue in the design of caching strategies. Moreover, user locations is a non-negligible factor since it is often coupled with content popularity in the practical scenarios, e.g., content popularity may vary along with user's location. Therefore, in this paper, a caching scheme is proposed based on a novel prediction model which jointly considers mobility and content popularity. In specific, Long Short-Term Memory (LSTM) method is utilized as a prediction tool due to its advantage of processing long sequences. Experimental results demonstrate the effectiveness of our proposed scheme with higher prediction accuracy and improved caching efficiency.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116861557","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024585
Ang Li, Jianxin Chen, B. Kang, Wenqin Zhuang, Xuguang Zhang
Fine-grained recognition is still a difficult task in pattern recognition applications due to the challenge of accurate localization of discriminative parts. Recent CNN-based methods generally utilize attention mechanism to produce attention masks without part labels/annotations and extract corresponding image parts from them. However, these methods extract the attention parts by using fixed-size rectangles to crop images regardless of the size of objects to be recognized, which will hinder the feature expression of the following Part-CNNs. In this paper, we propose an adaptive cropping module based on the information of attention masks to adjust size of cropping rectangles. The trainingprocessofadaptivecroppingmoduleandPart-CNNscan reinforce each other with the proposed rank loss and the classic softmax loss. To further balance and fuse all attention parts, we propose a part weighting module to evaluate part contributions. Under the optimization of sort loss, the part weighting module will produce part weights in the same order as prediction scores learned by attention parts. The backbone of our network is MA-CNN. Different from MA-CNN, the new proposed adaptive cropping module and part weighting module can jointly guide the framework to produce a more discriminative fine-grained feature. Experiments show that the AMA-CNN outperforms MA-CNN by 1.1% on CUB200-2011 bird dataset.
{"title":"Adaptive Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition","authors":"Ang Li, Jianxin Chen, B. Kang, Wenqin Zhuang, Xuguang Zhang","doi":"10.1109/GCWkshps45667.2019.9024585","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024585","url":null,"abstract":"Fine-grained recognition is still a difficult task in pattern recognition applications due to the challenge of accurate localization of discriminative parts. Recent CNN-based methods generally utilize attention mechanism to produce attention masks without part labels/annotations and extract corresponding image parts from them. However, these methods extract the attention parts by using fixed-size rectangles to crop images regardless of the size of objects to be recognized, which will hinder the feature expression of the following Part-CNNs. In this paper, we propose an adaptive cropping module based on the information of attention masks to adjust size of cropping rectangles. The trainingprocessofadaptivecroppingmoduleandPart-CNNscan reinforce each other with the proposed rank loss and the classic softmax loss. To further balance and fuse all attention parts, we propose a part weighting module to evaluate part contributions. Under the optimization of sort loss, the part weighting module will produce part weights in the same order as prediction scores learned by attention parts. The backbone of our network is MA-CNN. Different from MA-CNN, the new proposed adaptive cropping module and part weighting module can jointly guide the framework to produce a more discriminative fine-grained feature. Experiments show that the AMA-CNN outperforms MA-CNN by 1.1% on CUB200-2011 bird dataset.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478326","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024680
Zhe Tu, Huachun Zhou, Kun Li, Guanglei Li, Qihui Shen
As space networks become more and more important, the Space-ground Integration network (SGIN) has received unprecedented attention. However, dynamic changes of topology and link status of satellite networks bring many challenges to routing optimization in the SGIN. Traditional routing optimization methods do not perform well, as they do not consider changes of topology and link status, as well as the association between flows. Since the Machine Learning (ML) technologies have shown significant advantages in dynamic routing optimization, we proposed a Machine Learning-based Space-ground Integration Networking (ML-SSGIN) framework that combines Software-Defined Networking (SDN) technologies to solve this challenge. To evaluate the feasibility of the proposed framework, the Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm, is deployed to perform routing optimization, which can make routing decisions based on real-time link status. In particular, we utilize a neural network that integrates Long Short-Term Memory Network (LSTM) and Dense layers for its actor and critic part to improve perceptual capabilities of contextual correlations between flows. We compared the proposed DDPG neural network with the one only having the Dense layers. The results show that the proposed architecture is feasible and effective. What's more, compared to Open Shortest Path First (OSPF) algorithm, our proposed routing optimization method can adapt to continuously change flows, and link status, which improves end-to-end throughput and latency.
{"title":"A Routing Optimization Method for Software-Defined SGIN Based on Deep Reinforcement Learning","authors":"Zhe Tu, Huachun Zhou, Kun Li, Guanglei Li, Qihui Shen","doi":"10.1109/GCWkshps45667.2019.9024680","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024680","url":null,"abstract":"As space networks become more and more important, the Space-ground Integration network (SGIN) has received unprecedented attention. However, dynamic changes of topology and link status of satellite networks bring many challenges to routing optimization in the SGIN. Traditional routing optimization methods do not perform well, as they do not consider changes of topology and link status, as well as the association between flows. Since the Machine Learning (ML) technologies have shown significant advantages in dynamic routing optimization, we proposed a Machine Learning-based Space-ground Integration Networking (ML-SSGIN) framework that combines Software-Defined Networking (SDN) technologies to solve this challenge. To evaluate the feasibility of the proposed framework, the Deep Deterministic Policy Gradient (DDPG), a Deep Reinforcement Learning (DRL) algorithm, is deployed to perform routing optimization, which can make routing decisions based on real-time link status. In particular, we utilize a neural network that integrates Long Short-Term Memory Network (LSTM) and Dense layers for its actor and critic part to improve perceptual capabilities of contextual correlations between flows. We compared the proposed DDPG neural network with the one only having the Dense layers. The results show that the proposed architecture is feasible and effective. What's more, compared to Open Shortest Path First (OSPF) algorithm, our proposed routing optimization method can adapt to continuously change flows, and link status, which improves end-to-end throughput and latency.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129349180","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024648
Meng-Chun Hou, Der-Jiunn Deng, Chia‐Ling Wu
The boom of unmanned aerial vehicles (UAVs) is projected to fundamentally shift paradigms of transportations, logistics, agricultures, and public safety as a dominating unmanned application in following decades. To optimally process assigned tasks, each UAV requires prompt and ubiquitous information provisioning regarding the varying operation conditions, which renders exploiting base stations (BSs) of existing wireless infrastructures a tractable solution. To receive services from a BS, a UAV should stay within the coverage area of a BS, which however limits the operation range of a UAV. This obstacle thus drives the deployment of a special sort of UAV, known as an aerial base station (ABS), to relay signals between a BS and a UAV. Based on different flight paths of UAVs, an ABS should autonomously decide its own flight trajectory so as to maximize the number of UAVs which can receive wireless services. However, the inherently non-stationary environment renders the optimum autonomous deployment of an ABS a challenging issue. Inspired by the merit of interacting with the environment, we consequently propose a reinforcement learning scheme to optimize the flight trajectory of an ABS. To eliminate the engineering concern in the conventional Q-learning scheme that most state-action pairs may not be fully visited in the deployment of an ABS, in this paper, a state-amount-reduction (SAR) k-step Q-learning scheme is proposed to avoid the issue in the conventional Q-learning, so as to maximize the number of UAVs receiving services from an ABS. Through providing analytical foundations and simulation studies, outstanding performance of the proposed schemes is demonstrated as compared with that of the conventional reinforcement learning based ABS deployment.
{"title":"Optimum Aerial Base Station Deployment for UAV Networks: A Reinforcement Learning Approach","authors":"Meng-Chun Hou, Der-Jiunn Deng, Chia‐Ling Wu","doi":"10.1109/GCWkshps45667.2019.9024648","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024648","url":null,"abstract":"The boom of unmanned aerial vehicles (UAVs) is projected to fundamentally shift paradigms of transportations, logistics, agricultures, and public safety as a dominating unmanned application in following decades. To optimally process assigned tasks, each UAV requires prompt and ubiquitous information provisioning regarding the varying operation conditions, which renders exploiting base stations (BSs) of existing wireless infrastructures a tractable solution. To receive services from a BS, a UAV should stay within the coverage area of a BS, which however limits the operation range of a UAV. This obstacle thus drives the deployment of a special sort of UAV, known as an aerial base station (ABS), to relay signals between a BS and a UAV. Based on different flight paths of UAVs, an ABS should autonomously decide its own flight trajectory so as to maximize the number of UAVs which can receive wireless services. However, the inherently non-stationary environment renders the optimum autonomous deployment of an ABS a challenging issue. Inspired by the merit of interacting with the environment, we consequently propose a reinforcement learning scheme to optimize the flight trajectory of an ABS. To eliminate the engineering concern in the conventional Q-learning scheme that most state-action pairs may not be fully visited in the deployment of an ABS, in this paper, a state-amount-reduction (SAR) k-step Q-learning scheme is proposed to avoid the issue in the conventional Q-learning, so as to maximize the number of UAVs receiving services from an ABS. Through providing analytical foundations and simulation studies, outstanding performance of the proposed schemes is demonstrated as compared with that of the conventional reinforcement learning based ABS deployment.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127288017","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024486
Zhijuan Hu, Danyang Wang, Qifan Fu, Zan Li
Space-ground aided cooperative spectrum monitoring, which combines the benefits of satellite components and terrestrial components for improving monitoring accuracy and enlarging monitoring area, has been becoming an emerging application of the space-ground integrated networks (SGIN). However, a short transmission window is usually provided for satellite components to connect with ground gateway, which means only a limited transmission time is allowed for the satellite component to upload the collected spectrum data. On the other hand, lots of redundancy may exist among the spectrum data collected by a single sensor during one collection period, which may further reduce the data uploading efficiency. In this paper, we investigate the similar data detection which is a matching problem for comparing two data, and it is important to the following data compression for improving data uploading efficiency. Firstly, the definition of the sharing fragment set is given. Then a metric method is presented to measure the redundancy of one data with respect to another data. We propose a Sharing Fragment Set (SFS) algorithm that can select a good sharing fragment set. Theoretical analysis proves that the proposed SFS algorithm is well suited to determine the redundancy between datas. In addition, we conduct an experiment based on the randomly produced synthetic dataset. Numerical results shows that the SFS algorithm performs better in selecting sharing fragment set compared with the Greedy-String-Tiling (GST) and simple greedy algorithm.
{"title":"Similar Data Detection for Cooperative Spectrum Monitoring in Space-Ground Integrated Networks","authors":"Zhijuan Hu, Danyang Wang, Qifan Fu, Zan Li","doi":"10.1109/GCWkshps45667.2019.9024486","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024486","url":null,"abstract":"Space-ground aided cooperative spectrum monitoring, which combines the benefits of satellite components and terrestrial components for improving monitoring accuracy and enlarging monitoring area, has been becoming an emerging application of the space-ground integrated networks (SGIN). However, a short transmission window is usually provided for satellite components to connect with ground gateway, which means only a limited transmission time is allowed for the satellite component to upload the collected spectrum data. On the other hand, lots of redundancy may exist among the spectrum data collected by a single sensor during one collection period, which may further reduce the data uploading efficiency. In this paper, we investigate the similar data detection which is a matching problem for comparing two data, and it is important to the following data compression for improving data uploading efficiency. Firstly, the definition of the sharing fragment set is given. Then a metric method is presented to measure the redundancy of one data with respect to another data. We propose a Sharing Fragment Set (SFS) algorithm that can select a good sharing fragment set. Theoretical analysis proves that the proposed SFS algorithm is well suited to determine the redundancy between datas. In addition, we conduct an experiment based on the randomly produced synthetic dataset. Numerical results shows that the SFS algorithm performs better in selecting sharing fragment set compared with the Greedy-String-Tiling (GST) and simple greedy algorithm.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122367519","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024556
A. Barakabitze, Lingfen Sun, I. Mkwawa, E. Ifeachor
Future softwarized 5G networks have to be robust enough so as to ensure high network reliability and services availability. 5G network architecture should make sure that any failed parts in the network are detected, restored and recovered within a permissible period of time and at the lowest achievable cost. In this paper, we propose a Multipath Protection and Link-Failure free MPTCP/SR-based SDN/NFV architecture that increases survivability, resilience, availability of services in 5G networks. We present our system model and a multiPath protection and dynamic Link- Failure free algorithm called "PathReLief" that greatly reduces the failure recovery time and avoids link congestion in MPTCP/SR SDN/NFV 5G networks. To demonstrate the effectiveness of our proposal, we compare the performance of the proposed algorithm and the conventional topology discovery mechanisms for link/node failures in POX and OpenDaylight controllers. Preliminary results show that, our approach outperforms others used in the commonly used controllers (i.e., POX and OpenDaylight), in terms of reduced failure recovery time and localization time.
{"title":"Multipath Protections and Dynamic Link Recoveryin Softwarized 5G Networks Using Segment Routing","authors":"A. Barakabitze, Lingfen Sun, I. Mkwawa, E. Ifeachor","doi":"10.1109/GCWkshps45667.2019.9024556","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024556","url":null,"abstract":"Future softwarized 5G networks have to be robust enough so as to ensure high network reliability and services availability. 5G network architecture should make sure that any failed parts in the network are detected, restored and recovered within a permissible period of time and at the lowest achievable cost. In this paper, we propose a Multipath Protection and Link-Failure free MPTCP/SR-based SDN/NFV architecture that increases survivability, resilience, availability of services in 5G networks. We present our system model and a multiPath protection and dynamic Link- Failure free algorithm called \"PathReLief\" that greatly reduces the failure recovery time and avoids link congestion in MPTCP/SR SDN/NFV 5G networks. To demonstrate the effectiveness of our proposal, we compare the performance of the proposed algorithm and the conventional topology discovery mechanisms for link/node failures in POX and OpenDaylight controllers. Preliminary results show that, our approach outperforms others used in the commonly used controllers (i.e., POX and OpenDaylight), in terms of reduced failure recovery time and localization time.","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132419303","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 : 2019-12-01DOI: 10.1109/GCWkshps45667.2019.9024638
Fanbo Wei, Ting Zhou, Tianheng Xu, Honglin Hu, Xiaoming Tao
Mobile edge computing (MEC) and fog computing are considered to be a promising technique for the fifth generation (5G) networks. The main features of them are to push computing tasks to network edges. Meanwhile, to achieve low latency and high access speed, it is necessary to adopt an advanced transmission mechanism at network edges. Considering the advantages of large rate and high reliability provided by non-orthogonal multiple access (NOMA) and network coding, in this paper we propose a hybrid round-trip transmission mechanism at network edges. Specifically, we consider a fog node as a relay; and utilize a hybrid concept to design a NOMA-NC method which combines uplink NOMA and downlink network coding. Theoretical derivation and numerical results demonstrate that the proposed method distinctly outperforms both (i) traditional orthogonal multiple access (OMA) method combining OMA and network coding (namely OMA-NC); and (ii) NOMA-based round-trip transmission method (namely NOMA-NOMA).
{"title":"A Joint Mechanism for Fog-Relay Networks Based on NOMA and Network Coding","authors":"Fanbo Wei, Ting Zhou, Tianheng Xu, Honglin Hu, Xiaoming Tao","doi":"10.1109/GCWkshps45667.2019.9024638","DOIUrl":"https://doi.org/10.1109/GCWkshps45667.2019.9024638","url":null,"abstract":"Mobile edge computing (MEC) and fog computing are considered to be a promising technique for the fifth generation (5G) networks. The main features of them are to push computing tasks to network edges. Meanwhile, to achieve low latency and high access speed, it is necessary to adopt an advanced transmission mechanism at network edges. Considering the advantages of large rate and high reliability provided by non-orthogonal multiple access (NOMA) and network coding, in this paper we propose a hybrid round-trip transmission mechanism at network edges. Specifically, we consider a fog node as a relay; and utilize a hybrid concept to design a NOMA-NC method which combines uplink NOMA and downlink network coding. Theoretical derivation and numerical results demonstrate that the proposed method distinctly outperforms both (i) traditional orthogonal multiple access (OMA) method combining OMA and network coding (namely OMA-NC); and (ii) NOMA-based round-trip transmission method (namely NOMA-NOMA).","PeriodicalId":210825,"journal":{"name":"2019 IEEE Globecom Workshops (GC Wkshps)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131946574","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：