Pub Date : 2020-06-01DOI: 10.1109/ICC40277.2020.9149031
I. A. Ridhawi, M. Aloqaily, A. Boukerche, Y. Jararweh
Diversified Internet of Things services are becoming more complex and strictly user-defined. Traditional cloud solutions proved to be both costly in terms of resources and time efficiency. To overcome such a burden, researchers developed fog solutions for faster service responsiveness. Fog-to-Fog communication and cooperation was then introduced to compose services on-the-go for user-specific requests with the aid of mobile edge devices. This paper introduces a blockchain-based decentralized service composition solution for complex multimedia service delivery to cloud subscribers. The proposed work dynamically creates user-defined services without requiring any intermediary service or network provider entities to authenticate and deliver composite services. The composition process uses a reinforcement learning technique to construct secure and reliable composition paths. Participants are rewarded by cloud and fog entities for solving complex composition processes. Simulation results conducted on the system show that by adapting the proposed technique, fog and cloud entities require less resources and reduced power usage with increased service delivery success rates to cloud subscribers.
{"title":"A Blockchain-Based Decentralized Composition Solution for IoT Services","authors":"I. A. Ridhawi, M. Aloqaily, A. Boukerche, Y. Jararweh","doi":"10.1109/ICC40277.2020.9149031","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149031","url":null,"abstract":"Diversified Internet of Things services are becoming more complex and strictly user-defined. Traditional cloud solutions proved to be both costly in terms of resources and time efficiency. To overcome such a burden, researchers developed fog solutions for faster service responsiveness. Fog-to-Fog communication and cooperation was then introduced to compose services on-the-go for user-specific requests with the aid of mobile edge devices. This paper introduces a blockchain-based decentralized service composition solution for complex multimedia service delivery to cloud subscribers. The proposed work dynamically creates user-defined services without requiring any intermediary service or network provider entities to authenticate and deliver composite services. The composition process uses a reinforcement learning technique to construct secure and reliable composition paths. Participants are rewarded by cloud and fog entities for solving complex composition processes. Simulation results conducted on the system show that by adapting the proposed technique, fog and cloud entities require less resources and reduced power usage with increased service delivery success rates to cloud subscribers.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124757990","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149143
Yanchen Qiao, Bin Zhang, Weizhe Zhang
The traditional machine learning-based malware classification methods are mainly based on feature engineering. In order to improve accuracy, many features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve this issue, this paper proposes a malware classification method based on the word vector of bytes in the malware sample and Multilayer Perception (MLP). A malware sample consists of large number of bytes with values ranging from $0{x}00$ to 0xFF. Therefore, every malware sample could be considered as a document written by bytes. And this document could be divided into sentences based on padding or meaningless bytes. In this paper, first, we use Word2Vec to calculate a 256 dimensions word vector for each byte. Second, we combine them into a matrix in ascending order. Third, we use MLP to train the model on the training samples. Finally, we use the trained model to classify the testing samples. The experimental results show that the method has a high accuracy of 98.89%.
{"title":"Malware Classification Method Based on Word Vector of Bytes and Multilayer Perception","authors":"Yanchen Qiao, Bin Zhang, Weizhe Zhang","doi":"10.1109/ICC40277.2020.9149143","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149143","url":null,"abstract":"The traditional machine learning-based malware classification methods are mainly based on feature engineering. In order to improve accuracy, many features will be extracted from malware files in these methods. That brings a high complexity to the classification. To solve this issue, this paper proposes a malware classification method based on the word vector of bytes in the malware sample and Multilayer Perception (MLP). A malware sample consists of large number of bytes with values ranging from $0{x}00$ to 0xFF. Therefore, every malware sample could be considered as a document written by bytes. And this document could be divided into sentences based on padding or meaningless bytes. In this paper, first, we use Word2Vec to calculate a 256 dimensions word vector for each byte. Second, we combine them into a matrix in ascending order. Third, we use MLP to train the model on the training samples. Finally, we use the trained model to classify the testing samples. The experimental results show that the method has a high accuracy of 98.89%.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780402","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149294
Wenti Yang, Ruimiao Wang, Zhitao Guan, Longfei Wu, Xiaojiang Du, M. Guizani
The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large nusmber of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.
{"title":"A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things","authors":"Wenti Yang, Ruimiao Wang, Zhitao Guan, Longfei Wu, Xiaojiang Du, M. Guizani","doi":"10.1109/ICC40277.2020.9149294","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149294","url":null,"abstract":"The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large nusmber of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129842834","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148708
Hadeel Elayan, A. Eckford, R. Adve
Nanosized devices operating inside the human body open up new prospects in the healthcare domain. On the one hand, molecular communication enables biological nanomachines to communicate by exchanging molecules and performing application-dependent tasks. On the other hand, electromagnetic (EM) nano-communication points to the Terahertz Band (0.1-10 THz) as the frequency range for communication among nano-biosensors. In this paper, we propose a stimuli-responsive paradigm which integrates EM and molecular communication by stimulating proteins in the human body. Our model capitalizes on the fact that proteins act as an interface between both mediums, in which triggering proteins by THz waves changes their conformational structure. This allows biochemical and biomechanical activities to be carried out in a controlled manner. The stochasticity involved in the folding and unfolding of proteins is modeled using a Markov chain. A closed form expression for the mutual information rate by which proteins receive information is derived and maximized to find the capacity. By illustrating the information rates theoretically achievable, we hope to spark research into the EM-based control of protein networks.
{"title":"Regulating Molecular Interactions Using Terahertz Communication","authors":"Hadeel Elayan, A. Eckford, R. Adve","doi":"10.1109/ICC40277.2020.9148708","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148708","url":null,"abstract":"Nanosized devices operating inside the human body open up new prospects in the healthcare domain. On the one hand, molecular communication enables biological nanomachines to communicate by exchanging molecules and performing application-dependent tasks. On the other hand, electromagnetic (EM) nano-communication points to the Terahertz Band (0.1-10 THz) as the frequency range for communication among nano-biosensors. In this paper, we propose a stimuli-responsive paradigm which integrates EM and molecular communication by stimulating proteins in the human body. Our model capitalizes on the fact that proteins act as an interface between both mediums, in which triggering proteins by THz waves changes their conformational structure. This allows biochemical and biomechanical activities to be carried out in a controlled manner. The stochasticity involved in the folding and unfolding of proteins is modeled using a Markov chain. A closed form expression for the mutual information rate by which proteins receive information is derived and maximized to find the capacity. By illustrating the information rates theoretically achievable, we hope to spark research into the EM-based control of protein networks.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392590","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149445
Shuai Zhang, Bo Yin, Suyang Wang, Y. Cheng
Wireless optimization involves repeatedly solving difficult optimization problems, and data-driven deep learning techniques have great promise to alleviate this issue through its pattern matching capability: past optimal solutions can be used as the training data in a supervised learning paradigm so that the neural network can generate an approximate solution using a fraction of the computational cost, due to its high representing power and parallel implementation. However, making this approach practical in networking scenarios requires careful, domain-specific consideration, currently lacking in similar works. In this paper, we use deep learning in a wireless network scheduling and routing to predict if subsets of the network links are going to be used, so that the effective problem scale is reduced. A real-world concern is the varying data importance: training samples are not equally important due to class imbalance or different label quality. To compensate for this fact, we develop an adaptive sample weighting scheme which dynamically weights the batch samples in the training process. In addition, we design a novel loss function that uses additional network-layer feature information to improve the solution quality. We also discuss a post-processing step that gives a good threshold value to balance the trade-off between prediction quality and problem scale reduction. By numerical simulations, we demonstrate that these measures improve both the prediction quality and scale reduction when training from data of varied importance.
{"title":"Robust Deep Learning for Wireless Network Optimization","authors":"Shuai Zhang, Bo Yin, Suyang Wang, Y. Cheng","doi":"10.1109/ICC40277.2020.9149445","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149445","url":null,"abstract":"Wireless optimization involves repeatedly solving difficult optimization problems, and data-driven deep learning techniques have great promise to alleviate this issue through its pattern matching capability: past optimal solutions can be used as the training data in a supervised learning paradigm so that the neural network can generate an approximate solution using a fraction of the computational cost, due to its high representing power and parallel implementation. However, making this approach practical in networking scenarios requires careful, domain-specific consideration, currently lacking in similar works. In this paper, we use deep learning in a wireless network scheduling and routing to predict if subsets of the network links are going to be used, so that the effective problem scale is reduced. A real-world concern is the varying data importance: training samples are not equally important due to class imbalance or different label quality. To compensate for this fact, we develop an adaptive sample weighting scheme which dynamically weights the batch samples in the training process. In addition, we design a novel loss function that uses additional network-layer feature information to improve the solution quality. We also discuss a post-processing step that gives a good threshold value to balance the trade-off between prediction quality and problem scale reduction. By numerical simulations, we demonstrate that these measures improve both the prediction quality and scale reduction when training from data of varied importance.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126890431","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149112
A. Khan, G. Abbas, Z. Abbas, M. Waqas, Shanshan Tu, Alamgir Naushad
Cognitive radio networks (CRNs) promise to accommodate billions of Internet of Things (IoT) devices within scarce spectrum by allowing secondary users (SUs) to use licensed spectrum. However, the devices need uninterruptible communication, which the conventional CRNs cannot fulfill. This necessitates successful service completion probability (SSCP) enhancement in CRNs. Further, maintaining fairness among SUs, in terms of availing network services, is a matter of consideration for ensuring the network services to be fairly available to all SUs. In this paper, we propose a hybrid CRN (HCRN) scheme to analyze two problems. Firstly, we investigate SSCP enhancement by utilizing hybrid underlay-interweave mode of CRNs and propose a dynamic channel reservation algorithm to support interrupted users. Secondly, we propose a multi-attributes based fairness-driven channel determination (MFD) algorithm for channel interruption, which ensures fairness among SUs in availing network services. Furthermore, continuous-time Markov chain is used for modelling, and mathematical formulations are derived for SSCP. The proposed scheme is evaluated under various network traffic loads and channel failure rates. Numerical results show significant improvement in SSCP and reduction in forced termination rate as compared to the benchmark. Similarly, the MFD algorithm brings a prominent improvement in fairness.
{"title":"Service Completion Probability Enhancement and Fairness for SUs using Hybrid Mode CRNs","authors":"A. Khan, G. Abbas, Z. Abbas, M. Waqas, Shanshan Tu, Alamgir Naushad","doi":"10.1109/ICC40277.2020.9149112","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149112","url":null,"abstract":"Cognitive radio networks (CRNs) promise to accommodate billions of Internet of Things (IoT) devices within scarce spectrum by allowing secondary users (SUs) to use licensed spectrum. However, the devices need uninterruptible communication, which the conventional CRNs cannot fulfill. This necessitates successful service completion probability (SSCP) enhancement in CRNs. Further, maintaining fairness among SUs, in terms of availing network services, is a matter of consideration for ensuring the network services to be fairly available to all SUs. In this paper, we propose a hybrid CRN (HCRN) scheme to analyze two problems. Firstly, we investigate SSCP enhancement by utilizing hybrid underlay-interweave mode of CRNs and propose a dynamic channel reservation algorithm to support interrupted users. Secondly, we propose a multi-attributes based fairness-driven channel determination (MFD) algorithm for channel interruption, which ensures fairness among SUs in availing network services. Furthermore, continuous-time Markov chain is used for modelling, and mathematical formulations are derived for SSCP. The proposed scheme is evaluated under various network traffic loads and channel failure rates. Numerical results show significant improvement in SSCP and reduction in forced termination rate as compared to the benchmark. Similarly, the MFD algorithm brings a prominent improvement in fairness.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126897869","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149161
Sushmit Bhattacharjee, Robert Schmidt, Kostas Katsalis, Chia-Yu Chang, T. Bauschert, N. Nikaein
In 5G radio access networks, meeting the performance requirements of the fronthaul network is quite challenging. Recent standardization and research activities are focusing on exploiting the IEEE Time Sensitive Networking (TSN) technology for fronthaul networks. In this work we evaluate the performance of Ethernet TSN networks based on IEEE 802.1Qbv and IEEE 802.1Qbu for carrying real fronthaul traffic and benchmark it against Ethernet with Strict priority and Round Robin scheduling. We demonstrate that both 802.1Qbv and 802.1Qbu can be well used to protect high-priority traffic flows even in overload conditions.
{"title":"Time-Sensitive Networking for 5G Fronthaul Networks","authors":"Sushmit Bhattacharjee, Robert Schmidt, Kostas Katsalis, Chia-Yu Chang, T. Bauschert, N. Nikaein","doi":"10.1109/ICC40277.2020.9149161","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149161","url":null,"abstract":"In 5G radio access networks, meeting the performance requirements of the fronthaul network is quite challenging. Recent standardization and research activities are focusing on exploiting the IEEE Time Sensitive Networking (TSN) technology for fronthaul networks. In this work we evaluate the performance of Ethernet TSN networks based on IEEE 802.1Qbv and IEEE 802.1Qbu for carrying real fronthaul traffic and benchmark it against Ethernet with Strict priority and Round Robin scheduling. We demonstrate that both 802.1Qbv and 802.1Qbu can be well used to protect high-priority traffic flows even in overload conditions.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129238444","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149356
G. Kollias, A. Antonopoulos
Content caching has been considered by both academia and industry as an efficient solution to tackle the problem of the back-haul becoming the bottleneck in the service of users in future heterogeneous cellular networks. Most of the related caching-oriented studies are based on the content popularity, overlooking the impact of content size on their analysis. In this context, this work studies content caching in an environment where cellular users are equipped with cache memories. In particular, we formulate the content caching as an optimization problem, where the objective is to minimize the average download latency of popular videos through self-caching and device-to-device (D2D) caching and, consequently, increase the network throughput. In addition, in order to solve this problem in real-time scenarios, we introduce a low-complexity utility-based algorithm, which accounts for parameters such as the size and the popularity of the requested contents, as well as the density of the end users. Finally, we provide extensive simulation results that validate our analysis and prove that our innovative scheme outperforms other existing solutions.
{"title":"Joint Consideration of Content Popularity and Size in Device-to-Device Caching Scenarios","authors":"G. Kollias, A. Antonopoulos","doi":"10.1109/ICC40277.2020.9149356","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149356","url":null,"abstract":"Content caching has been considered by both academia and industry as an efficient solution to tackle the problem of the back-haul becoming the bottleneck in the service of users in future heterogeneous cellular networks. Most of the related caching-oriented studies are based on the content popularity, overlooking the impact of content size on their analysis. In this context, this work studies content caching in an environment where cellular users are equipped with cache memories. In particular, we formulate the content caching as an optimization problem, where the objective is to minimize the average download latency of popular videos through self-caching and device-to-device (D2D) caching and, consequently, increase the network throughput. In addition, in order to solve this problem in real-time scenarios, we introduce a low-complexity utility-based algorithm, which accounts for parameters such as the size and the popularity of the requested contents, as well as the density of the end users. Finally, we provide extensive simulation results that validate our analysis and prove that our innovative scheme outperforms other existing solutions.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123537197","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9148784
R. Soua, M. Palattella, André Stemper, T. Engel
Given that several services can benefit from the adoption of a group communication model, the IETF has specifically standardized the usage of CoAP group communication. However, CoAP responses are still sent in unicast from each single CoAP server to the CoAP client, which results in a substantial traffic load. Such problem becomes more severe in integrated IoT-Satellite networks given the limited bandwidth of the satellite return channel and the large number of IoT devices in a massive MTC (mMTC) scenario. To reduce network traffic overhead in group communication and improve the network responsiveness, this paper proposes an aggregation scheme for the CoAP group communication in combination with Observer pattern and proxying. Results obtained by using the openSAND emulator and CoAPthon library corroborate the merit of our optimization in terms of overhead reduction and delay.
{"title":"Enhancing CoAP Group Communication to Support mMTC Over Satellite Networks","authors":"R. Soua, M. Palattella, André Stemper, T. Engel","doi":"10.1109/ICC40277.2020.9148784","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9148784","url":null,"abstract":"Given that several services can benefit from the adoption of a group communication model, the IETF has specifically standardized the usage of CoAP group communication. However, CoAP responses are still sent in unicast from each single CoAP server to the CoAP client, which results in a substantial traffic load. Such problem becomes more severe in integrated IoT-Satellite networks given the limited bandwidth of the satellite return channel and the large number of IoT devices in a massive MTC (mMTC) scenario. To reduce network traffic overhead in group communication and improve the network responsiveness, this paper proposes an aggregation scheme for the CoAP group communication in combination with Observer pattern and proxying. Results obtained by using the openSAND emulator and CoAPthon library corroborate the merit of our optimization in terms of overhead reduction and delay.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121386632","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 : 2020-06-01DOI: 10.1109/ICC40277.2020.9149121
Li You, Ke-Xin Li, Jiaheng Wang, Xiqi Gao, X. Xia, Björn Otterstenx
Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO downlink (DL) transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems by exploiting statistical channel state information (sCSI) at the transmitter. We first establish a massive MIMO channel model for LEO satellite communications and propose Doppler and time delay compensation techniques at user terminals (UTs). Then, we develop a closed-form low-complexity sCSI based DL precoder by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Motivated by the DL ASLNR upper bound, we further propose a space angle based user grouping algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems.
{"title":"LEO Satellite Communications with Massive MIMO","authors":"Li You, Ke-Xin Li, Jiaheng Wang, Xiqi Gao, X. Xia, Björn Otterstenx","doi":"10.1109/ICC40277.2020.9149121","DOIUrl":"https://doi.org/10.1109/ICC40277.2020.9149121","url":null,"abstract":"Low earth orbit (LEO) satellite communications are expected to be incorporated in future wireless networks to provide global wireless access with enhanced data rates. Massive multiple-input multiple-output (MIMO) techniques, though widely used in terrestrial communication systems, have not been applied to LEO satellite communication systems. In this paper, we propose a massive MIMO downlink (DL) transmission scheme with full frequency reuse (FFR) for LEO satellite communication systems by exploiting statistical channel state information (sCSI) at the transmitter. We first establish a massive MIMO channel model for LEO satellite communications and propose Doppler and time delay compensation techniques at user terminals (UTs). Then, we develop a closed-form low-complexity sCSI based DL precoder by maximizing the average signal-to-leakage-plus-noise ratio (ASLNR). Motivated by the DL ASLNR upper bound, we further propose a space angle based user grouping algorithm to schedule the served UTs into different groups, where each group of UTs use the same time and frequency resource. Numerical results demonstrate that the proposed massive MIMO transmission scheme with FFR significantly enhances the data rate of LEO satellite communication systems.","PeriodicalId":106560,"journal":{"name":"ICC 2020 - 2020 IEEE International Conference on Communications (ICC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114313680","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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