Pub Date : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065866
Suozai Li, Ming-jun Huang, Qinghao Wang, Yongxin Zhang, Ning Lu, Wenbo Shi, Hong Lei
Cryptocurrencies such as Bitcoin and Ethereum achieve decentralized payment by maintaining a globally distributed and append-only ledger. Recently, several researchers have sought to achieve privacy-preserving auditing, which is a crucial function for scenarios that require regulatory compliance, for decentralized payment systems. However, those proposed schemes usually cost much time for the cooperation between the auditor and the user due to leveraging complex cryptographic tools such as zero-knowledge proof. To tackle the problem, we present T-PPA, a privacy-preserving decentralized payment system, which provides customizable and efficient auditability by leveraging trusted execution environments (TEEs). T-PPA demands the auditor construct audit programs based on request and execute them in the TEE to protect the privacy of transactions. Then, identity-based encryption (IBE) is employed to construct the separation of power between the agency nodes and the auditor and to protect the privacy of transactions out of TEE. The experimental results show that T-PPA can achieve privacy-preserving audits with acceptable overhead.
{"title":"T-PPA: A Privacy-Preserving Decentralized Payment System with Efficient Auditability Based on TEE","authors":"Suozai Li, Ming-jun Huang, Qinghao Wang, Yongxin Zhang, Ning Lu, Wenbo Shi, Hong Lei","doi":"10.1109/ICCC56324.2022.10065866","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065866","url":null,"abstract":"Cryptocurrencies such as Bitcoin and Ethereum achieve decentralized payment by maintaining a globally distributed and append-only ledger. Recently, several researchers have sought to achieve privacy-preserving auditing, which is a crucial function for scenarios that require regulatory compliance, for decentralized payment systems. However, those proposed schemes usually cost much time for the cooperation between the auditor and the user due to leveraging complex cryptographic tools such as zero-knowledge proof. To tackle the problem, we present T-PPA, a privacy-preserving decentralized payment system, which provides customizable and efficient auditability by leveraging trusted execution environments (TEEs). T-PPA demands the auditor construct audit programs based on request and execute them in the TEE to protect the privacy of transactions. Then, identity-based encryption (IBE) is employed to construct the separation of power between the agency nodes and the auditor and to protect the privacy of transactions out of TEE. The experimental results show that T-PPA can achieve privacy-preserving audits with acceptable overhead.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293368","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065651
Zhiming Liu, Shuangfeng Han
MAC scheduling plays an important role in affecting the performance of wireless communication system and the fairness among users. A significant number of algorithms have been put forward for increasingly complex scenarios in recent years. In this paper, we designed a novel MAC scheduling algorithm for intelligent 6G wireless network based on cross-layer mathematical algorithm and deep learning with Multi-Layer Perceptron (MLP). We define the cross-layer mathematical algorithm part as HI (Human Intelligence) engine and the deep learning part as AI (Artificial Intelligence) engine. Also, we respectively designed the coordination mechanism, required data, respective workflows and specific algorithms for the two engines. To demonstrate the superior performance gains of our proposed methodology, the use case was analyzed with simulation in various traffic scenarios and made performance comparison with traditional solution.
{"title":"A Novel MAC Scheduling Based on Cross-layer Algorithm and Deep Learning","authors":"Zhiming Liu, Shuangfeng Han","doi":"10.1109/ICCC56324.2022.10065651","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065651","url":null,"abstract":"MAC scheduling plays an important role in affecting the performance of wireless communication system and the fairness among users. A significant number of algorithms have been put forward for increasingly complex scenarios in recent years. In this paper, we designed a novel MAC scheduling algorithm for intelligent 6G wireless network based on cross-layer mathematical algorithm and deep learning with Multi-Layer Perceptron (MLP). We define the cross-layer mathematical algorithm part as HI (Human Intelligence) engine and the deep learning part as AI (Artificial Intelligence) engine. Also, we respectively designed the coordination mechanism, required data, respective workflows and specific algorithms for the two engines. To demonstrate the superior performance gains of our proposed methodology, the use case was analyzed with simulation in various traffic scenarios and made performance comparison with traditional solution.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126399620","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065683
Kunyu Shen, Lei Zhang, Ruisong Zhang, Qihao Fang
Asymmetric group key agreement allows a group of users to negotiate a public encryption key that corresponds to several decryption keys, and each decryption key can only be computed by one group member. This novel notion ensures the confidentiality of communication among group members and allows any external entity to send messages to the group. However, the existing authenticated asymmetric group key agreement protocols are designed in identity-based cryptosystem or certificateless public key cryptosystem, which are not widely deployed. In this paper, we propose an efficient authenticated asymmetric group key agreement protocol. The protocol captures the security of secrecy, known-key security, key-compromise impersonation, unknown key-share and key control while being resistant to active attacks. The security of our protocol is reduced to the k-BDHE problem.
{"title":"Asymmetric Group Key Agreement Protocol from Short Signatures","authors":"Kunyu Shen, Lei Zhang, Ruisong Zhang, Qihao Fang","doi":"10.1109/ICCC56324.2022.10065683","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065683","url":null,"abstract":"Asymmetric group key agreement allows a group of users to negotiate a public encryption key that corresponds to several decryption keys, and each decryption key can only be computed by one group member. This novel notion ensures the confidentiality of communication among group members and allows any external entity to send messages to the group. However, the existing authenticated asymmetric group key agreement protocols are designed in identity-based cryptosystem or certificateless public key cryptosystem, which are not widely deployed. In this paper, we propose an efficient authenticated asymmetric group key agreement protocol. The protocol captures the security of secrecy, known-key security, key-compromise impersonation, unknown key-share and key control while being resistant to active attacks. The security of our protocol is reduced to the k-BDHE problem.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122302870","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}
In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.
{"title":"Point-Level and Set-Level Deep Representation Learning for Cross-Modal Person Re-identification","authors":"Jihui Hu, Pengfei Ye, Danyang Li, Lingyun Dong, Xiaopan Chen, Xiaoke Zhu","doi":"10.1109/ICCC56324.2022.10065694","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065694","url":null,"abstract":"In practice, significant modality differences usually exist between visible and infrared images, which makes visible-infrared Person Re-Identification (VI-ReID) a challenging research task. Due to the existing influence of pose variation, background changes, and occlusion, there are often outlier samples within the set of images from the same person. These outlier samples can adversely affect the process of learning the cross-modal matching model. The existing VI-ReID methods mainly focus on learning cross-modal feature representation by using image-level discriminant constraints, i.e., the distance between the truly-matching cross-modal images should be smaller than that between wrong-matching cross-modal images. However, most of these methods ignore the adverse influence caused by outliers. To solve the above problems, we proposed a Point-level and Set-level Deep Representation Learning (PSDRL) approach for VI-ReID in this paper. By using the set-level constraint in the process of deep representation learning, the discrepancy between visible and infrared modalities can be decreased, and the adverse effect of outliers can be weakened. By employing the image-level constraint, the discriminability of the obtained deep feature representations can be improved. Extensive experiments are conducted on the publicly available cross-modal Person Re-Identification datasets, including SYSU-MM01 and RegDB. Experimental results demonstrate the effectiveness of the proposed approach.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125912846","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10066048
Yuqian Song
Taxi demand prediction has recently attracted increasing research interest due to the growing availability of large-scale traffic data, which could empower various real-world applications. Accurate taxi demand prediction can improve vehicle utilization, reduce the time for passengers to wait for taxis, and mitigate traffic congestion. Although both spatial dependencies and temporal dynamics have been considered, most of the previous methods with over-complicated models might easily achieve suboptimal performance due to the overfitting issue. Contrastive unsupervised learning has recently shown encouraging progress, which has great potential to learn effective data representations without extensive manual labeling. In this paper, we utilize contrastive learning to construct an effective auxiliary task to learn feature representations of data in a self-supervised manner. The model learned via contrastive learning can be subsequently applied for downstream tasks, which is proven to be more robust against overfitting. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our proposed model over other compared methods for taxi demand prediction.
{"title":"Effective Traffic Prediction with Self-Supervised Contrastive Learning","authors":"Yuqian Song","doi":"10.1109/ICCC56324.2022.10066048","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10066048","url":null,"abstract":"Taxi demand prediction has recently attracted increasing research interest due to the growing availability of large-scale traffic data, which could empower various real-world applications. Accurate taxi demand prediction can improve vehicle utilization, reduce the time for passengers to wait for taxis, and mitigate traffic congestion. Although both spatial dependencies and temporal dynamics have been considered, most of the previous methods with over-complicated models might easily achieve suboptimal performance due to the overfitting issue. Contrastive unsupervised learning has recently shown encouraging progress, which has great potential to learn effective data representations without extensive manual labeling. In this paper, we utilize contrastive learning to construct an effective auxiliary task to learn feature representations of data in a self-supervised manner. The model learned via contrastive learning can be subsequently applied for downstream tasks, which is proven to be more robust against overfitting. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our proposed model over other compared methods for taxi demand prediction.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121096227","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065972
Yanming Zhao, Ping Guo, Chang Liu, Wanying Song
In view of the increasing multidimensional, detailed and dynamic nature of modern battlefield situation, the traditional two-dimensional (2D) battlefield situation visualization can no longer satisfy the needs of battlefield command and decision-making. Based on the in-depth study of OpenSceneGraph (OSG) and Qt. This paper designs and implements a three-dimensional (3D) visualization system of battlefield situation. This system combined with the radar system, uses User Datagram Protocol (UDP) protocol for real-time data communication. It is designed and implemented from four parts: resource management database, 3D visualization platform, radar system and interactive function realization. And the interactive function, such as radar beam dynamic scanning display, battlefield situation information display and playback, and real-time data communication, etc, are mainly designed in this system. The visualization results shows that the 3D visualization system has good real-time performance, comprehensive functions and strong sense of immersion. It has reference and guiding significance for the research and development of various application systems of 3D situation.
{"title":"Design and Implementation of Battlefield Situation Visualization System Based on OSG","authors":"Yanming Zhao, Ping Guo, Chang Liu, Wanying Song","doi":"10.1109/ICCC56324.2022.10065972","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065972","url":null,"abstract":"In view of the increasing multidimensional, detailed and dynamic nature of modern battlefield situation, the traditional two-dimensional (2D) battlefield situation visualization can no longer satisfy the needs of battlefield command and decision-making. Based on the in-depth study of OpenSceneGraph (OSG) and Qt. This paper designs and implements a three-dimensional (3D) visualization system of battlefield situation. This system combined with the radar system, uses User Datagram Protocol (UDP) protocol for real-time data communication. It is designed and implemented from four parts: resource management database, 3D visualization platform, radar system and interactive function realization. And the interactive function, such as radar beam dynamic scanning display, battlefield situation information display and playback, and real-time data communication, etc, are mainly designed in this system. The visualization results shows that the 3D visualization system has good real-time performance, comprehensive functions and strong sense of immersion. It has reference and guiding significance for the research and development of various application systems of 3D situation.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121059665","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}
Aiming at the problems that convolution neural network is not sufficient to extract text features, it is difficult to capture long text structure information and sentence semantic relationship, and neural network can only extract the surface features of text, and it is difficult to obtain the implicit features of sentences when classifying text, this paper proposes a hybrid neural network text classification method based on a knowledge base. By extracting keywords from the category text, the keyword weight is calculated according to the co-occurrence relationship, and the category keyword knowledge base is constructed. It integrates CNN and bilstm, introduces attention mechanism, enhances the ability to extract local and temporal features of text, and improves the accuracy of text classification in combination with knowledge base information. The experimental results on the CNews dataset show that compared with TextCNN, TextRCNN, BILSTM and Bert models, this model can effectively improve the accuracy of text classification.
{"title":"Research on Sensitive Text Classification Based on Knowledge Base and Hybrid Network","authors":"Yongfeng Li, Hongliang Wang, Xueting Li, Pengfei Xiu","doi":"10.1109/ICCC56324.2022.10065790","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065790","url":null,"abstract":"Aiming at the problems that convolution neural network is not sufficient to extract text features, it is difficult to capture long text structure information and sentence semantic relationship, and neural network can only extract the surface features of text, and it is difficult to obtain the implicit features of sentences when classifying text, this paper proposes a hybrid neural network text classification method based on a knowledge base. By extracting keywords from the category text, the keyword weight is calculated according to the co-occurrence relationship, and the category keyword knowledge base is constructed. It integrates CNN and bilstm, introduces attention mechanism, enhances the ability to extract local and temporal features of text, and improves the accuracy of text classification in combination with knowledge base information. The experimental results on the CNews dataset show that compared with TextCNN, TextRCNN, BILSTM and Bert models, this model can effectively improve the accuracy of text classification.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116536233","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065788
C. An, Donglei Zhang, Xinjie Gao, Xueqiong Zhu
As the dramatic increase of both number and types of IoT devices, especially the ones have stringent requirements on complexity, development period and cost, cryptography misuses are becoming increasingly common which will have a tremendous influence on the security and privacy protection. To address the above question, this paper studies the feature of 73 cryptography misuses, proposes a cryptography detection method based on Bi-LSTM which can learns forward and backward simultaneously, and then developes a prototype tool called CryptoDetection for Java source code. Experimental results have shown that the proposed detection method achieves 92% accuracy, 92% precision, 91% recall and 92% f1-score, which respectively outperforms the best one among all the compared state-of-the-art techniques.
{"title":"CryptoDetection: A Cryptography Misuse Detection Method Based on Bi-LSTM","authors":"C. An, Donglei Zhang, Xinjie Gao, Xueqiong Zhu","doi":"10.1109/ICCC56324.2022.10065788","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065788","url":null,"abstract":"As the dramatic increase of both number and types of IoT devices, especially the ones have stringent requirements on complexity, development period and cost, cryptography misuses are becoming increasingly common which will have a tremendous influence on the security and privacy protection. To address the above question, this paper studies the feature of 73 cryptography misuses, proposes a cryptography detection method based on Bi-LSTM which can learns forward and backward simultaneously, and then developes a prototype tool called CryptoDetection for Java source code. Experimental results have shown that the proposed detection method achieves 92% accuracy, 92% precision, 91% recall and 92% f1-score, which respectively outperforms the best one among all the compared state-of-the-art techniques.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116713918","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065761
Yufei Qiao, Xu Qiao, Run Luo, Zhencong Dai, Longxiang Yang
The cell-free massive multiple-input multiple-output (MIMO) system is a promising technology for B5G and 6G, it owns large spectral and power efficiency. However, the precoding scheme can affect the system downlink spectral efficiency obviously. Maximum-ratio transmission (MRT) is the most common precoding scheme used in cell-free massive MIMO systems, which maximizes the power of the desired signal, but is unable to suppress interference. Centralized zero-forcing (CZF) is a precoding scheme with strong interference cancellation ability, but can not support too many users because of the high computational complexity and limited front-haul capacity. In order to suppress interference and satisfy the requirement of system scalability, we propose a joint precoding scheme with enhanced spectral efficiency, named Scalable Joint Zero-Forcing Precoding (SJZF), which deploys different zero-forcing precoding schemes with the degree of inter-APs interference. We derive a closed-form expression of the achievable spectral efficiency (SE) for SJZF, under the assumption of independent rayleigh fading channel and pilot contamination. Based on the characteristics of the SE expression, we propose a novel random pilot assignment method, and a max-min fairness power optimization algorithm, to improve the system's SE obviously. Numerical results show that the SE of our SJZF outperforms other conventional precoding schemes, approaching CZF, but have affordable computation complexity.
{"title":"Joint Precoding Scheme with Enhanced Spectral Efficiency for Cell-Free Massive MIMO Downlink","authors":"Yufei Qiao, Xu Qiao, Run Luo, Zhencong Dai, Longxiang Yang","doi":"10.1109/ICCC56324.2022.10065761","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065761","url":null,"abstract":"The cell-free massive multiple-input multiple-output (MIMO) system is a promising technology for B5G and 6G, it owns large spectral and power efficiency. However, the precoding scheme can affect the system downlink spectral efficiency obviously. Maximum-ratio transmission (MRT) is the most common precoding scheme used in cell-free massive MIMO systems, which maximizes the power of the desired signal, but is unable to suppress interference. Centralized zero-forcing (CZF) is a precoding scheme with strong interference cancellation ability, but can not support too many users because of the high computational complexity and limited front-haul capacity. In order to suppress interference and satisfy the requirement of system scalability, we propose a joint precoding scheme with enhanced spectral efficiency, named Scalable Joint Zero-Forcing Precoding (SJZF), which deploys different zero-forcing precoding schemes with the degree of inter-APs interference. We derive a closed-form expression of the achievable spectral efficiency (SE) for SJZF, under the assumption of independent rayleigh fading channel and pilot contamination. Based on the characteristics of the SE expression, we propose a novel random pilot assignment method, and a max-min fairness power optimization algorithm, to improve the system's SE obviously. Numerical results show that the SE of our SJZF outperforms other conventional precoding schemes, approaching CZF, but have affordable computation complexity.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121715542","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 : 2022-12-09DOI: 10.1109/ICCC56324.2022.10065779
Liancheng Zhang, Wenwen Du, Lanxin Cheng, Yi Guo, Wenbo Ding, Xupan Ma
Current IPv6 node neighbor cache teaching practices focus on theoretical introductions and graphical explanations, and lack targeted experiments on neighbor cache states change. Since the neighbor cache states of IPv6 nodes change rapidly and some specific states are difficult to observe, the EVE-NG platform, which can combine Cisco router debugging with Wireshark packet capture and analysis, is introduced to experimental teaching to set up two comparative verification experiments of IPv6 neighbor cache states change, which are states observation of IPv6 node neighbor cache based on address resolution and packet parsing, and refined observation of IPv6 node neighbor cache states based on router debugging. Through these two phased experiments, students can not only visually observe the changes of the neighbor cache states of IPv6 nodes, but also deeply understand the timer and event-triggered change mechanism of the neighbor cache states of IPv6 nodes. Judging from actual teaching practices of two different classes, the EVE-NG experimental platform and two phased experiments have been well evaluated by the students, which have achieved good experimental teaching effects.
{"title":"Experimental Design of Router Debugging based Neighbor Cache States Change of IPv6 Nodes","authors":"Liancheng Zhang, Wenwen Du, Lanxin Cheng, Yi Guo, Wenbo Ding, Xupan Ma","doi":"10.1109/ICCC56324.2022.10065779","DOIUrl":"https://doi.org/10.1109/ICCC56324.2022.10065779","url":null,"abstract":"Current IPv6 node neighbor cache teaching practices focus on theoretical introductions and graphical explanations, and lack targeted experiments on neighbor cache states change. Since the neighbor cache states of IPv6 nodes change rapidly and some specific states are difficult to observe, the EVE-NG platform, which can combine Cisco router debugging with Wireshark packet capture and analysis, is introduced to experimental teaching to set up two comparative verification experiments of IPv6 neighbor cache states change, which are states observation of IPv6 node neighbor cache based on address resolution and packet parsing, and refined observation of IPv6 node neighbor cache states based on router debugging. Through these two phased experiments, students can not only visually observe the changes of the neighbor cache states of IPv6 nodes, but also deeply understand the timer and event-triggered change mechanism of the neighbor cache states of IPv6 nodes. Judging from actual teaching practices of two different classes, the EVE-NG experimental platform and two phased experiments have been well evaluated by the students, which have achieved good experimental teaching effects.","PeriodicalId":263098,"journal":{"name":"2022 IEEE 8th International Conference on Computer and Communications (ICCC)","volume":"7 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113944447","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：