Pub Date : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211245
Ziqi Liu, Xun Zhang, Bastien Béchadergue
In recent years, with the rapid deployment of 5G and the planning for future 6G multi-network convergence, interest in optical wireless communication (OWC) technology as a promising complementary technology to Radio Frequency (RF) technology has gained new impetus. In this paper, aiming at the OWC communication system, an indoor OWC channel measurement testbed is built to measure the infrared (IR) channel under the testbed and evaluate its communication performance. Through experiments, the channel response of the IR device is measured. The OWC channel measurement testbed can achieve a data rate above 16Mbps, with the minimum value of Error Vector Magnitude (EVM) 18.8338%, and the minimum value of Bit Error Ratio (BER) 0.0301. According to the Digital Cinema System Specification (DCSS) issued by Digital Cinema Initiatives (DCI), the system meets the transmission standard of 4K radio.
{"title":"OWC channel measurement testbed building in indoor environment with m-CAP modulation","authors":"Ziqi Liu, Xun Zhang, Bastien Béchadergue","doi":"10.1109/BMSB58369.2023.10211245","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211245","url":null,"abstract":"In recent years, with the rapid deployment of 5G and the planning for future 6G multi-network convergence, interest in optical wireless communication (OWC) technology as a promising complementary technology to Radio Frequency (RF) technology has gained new impetus. In this paper, aiming at the OWC communication system, an indoor OWC channel measurement testbed is built to measure the infrared (IR) channel under the testbed and evaluate its communication performance. Through experiments, the channel response of the IR device is measured. The OWC channel measurement testbed can achieve a data rate above 16Mbps, with the minimum value of Error Vector Magnitude (EVM) 18.8338%, and the minimum value of Bit Error Ratio (BER) 0.0301. According to the Digital Cinema System Specification (DCSS) issued by Digital Cinema Initiatives (DCI), the system meets the transmission standard of 4K radio.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"15 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83700212","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211110
Sujie Shao, Lili Su, Ruijun Chai, Shaoyong Guo, Siya Xu, Juntao Zheng
The rapid development of 5G has made mobile edge computing technology the first choice for smart highways, but the exponentially growing number of vehicles has also brought great challenges, that is, how to provide vehicle users with low-latency and reliable services has become a key issue. To this end, a multi-level intelligent collaborative computing mechanism based on Double Deep Q-learning(DDQN) for smart highways was proposed. First, a multi-level intelligent collaborative computing model based on RSUs, mobile edge computing (MEC) servers and clouds was established. Further, aiming at the goals of task delay minimization and network load balancing, a multi-level intelligent collaborative computing algorithm based on deep reinforcement learning was proposed to perform collaborative computing on massive tasks generated by high-speed moving vehicles. Simulation results show that the proposed mechanism can reduce service delay and maintain network load balancing while completing service requirements.
{"title":"Multi-level Intelligent Collaborative Computing Mechanism Based on DDQN for Smart Highways","authors":"Sujie Shao, Lili Su, Ruijun Chai, Shaoyong Guo, Siya Xu, Juntao Zheng","doi":"10.1109/BMSB58369.2023.10211110","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211110","url":null,"abstract":"The rapid development of 5G has made mobile edge computing technology the first choice for smart highways, but the exponentially growing number of vehicles has also brought great challenges, that is, how to provide vehicle users with low-latency and reliable services has become a key issue. To this end, a multi-level intelligent collaborative computing mechanism based on Double Deep Q-learning(DDQN) for smart highways was proposed. First, a multi-level intelligent collaborative computing model based on RSUs, mobile edge computing (MEC) servers and clouds was established. Further, aiming at the goals of task delay minimization and network load balancing, a multi-level intelligent collaborative computing algorithm based on deep reinforcement learning was proposed to perform collaborative computing on massive tasks generated by high-speed moving vehicles. Simulation results show that the proposed mechanism can reduce service delay and maintain network load balancing while completing service requirements.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"60 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82673037","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211242
Jiaying Zong, Xuan Huang, Haitao Liu, Yang Liu, Qingtian Wang, Yanxia Xing
With the widespread use of smart devices and the emergence of novel scenarios and services, the future wireless network architecture is urged to innovate to fulfill various application requirements. However, the current architecture of the Radio Access Network (RAN) divides and evolves functions in units of the Centralized Unit Control Plane (CU-CP), Central Unit User Plane (CU-UP), and Distributed Unit (DU), which lacks adequate flexibility and openness. Besides, it proves difficult for the network to adapt to new features such as intelligence, security, and computing capabilities. This paper presents a service-based RAN architecture, and reconstructs and defines the corresponding functions. Combined with the evolution trend of the future distributed Core Network (CN), the service-oriented RAN functions and the sinking CN functions are integrated at the edge node. Then the handover procedure is taken as an example, and the process design under the converged architecture is considered to reduce the overhead of redundant functions and promote transmission efficiency.
{"title":"Service-Oriented Wireless Network Architecture and Edge Network Convergence Design","authors":"Jiaying Zong, Xuan Huang, Haitao Liu, Yang Liu, Qingtian Wang, Yanxia Xing","doi":"10.1109/BMSB58369.2023.10211242","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211242","url":null,"abstract":"With the widespread use of smart devices and the emergence of novel scenarios and services, the future wireless network architecture is urged to innovate to fulfill various application requirements. However, the current architecture of the Radio Access Network (RAN) divides and evolves functions in units of the Centralized Unit Control Plane (CU-CP), Central Unit User Plane (CU-UP), and Distributed Unit (DU), which lacks adequate flexibility and openness. Besides, it proves difficult for the network to adapt to new features such as intelligence, security, and computing capabilities. This paper presents a service-based RAN architecture, and reconstructs and defines the corresponding functions. Combined with the evolution trend of the future distributed Core Network (CN), the service-oriented RAN functions and the sinking CN functions are integrated at the edge node. Then the handover procedure is taken as an example, and the process design under the converged architecture is considered to reduce the overhead of redundant functions and promote transmission efficiency.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"12 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83857937","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}
The Metaverse has been gaining more attention from researchers worldwide. As the avatar of human in the Metaverse, three dimensional (3D) digital human has also attracted extensive attention. At present, the large-scale application of 3D digital human cannot be realized. The reason is that the construction cost of 3D avatar is too high, and it is mostly completed manually, especially for building a drivable 3D digital human. At present, the research on intelligent modeling of 3D digital human is modularized, lacking system integration to generate a drivable 3D digital human. This paper proposes a 3D drivable digital human generation method, and constructs an end-to-end testing system. The test proves that it can realize the generation of 3D drivable digital human based on single-view RGB image, and realize the driven of it by video from ordinary camera.
{"title":"Research on 3D Driveable Digital Human Generation System","authors":"Qiuhong Zheng, Peng Ding, Yun Shen, Jinghan Wang, Zhichao Zhang","doi":"10.1109/BMSB58369.2023.10211615","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211615","url":null,"abstract":"The Metaverse has been gaining more attention from researchers worldwide. As the avatar of human in the Metaverse, three dimensional (3D) digital human has also attracted extensive attention. At present, the large-scale application of 3D digital human cannot be realized. The reason is that the construction cost of 3D avatar is too high, and it is mostly completed manually, especially for building a drivable 3D digital human. At present, the research on intelligent modeling of 3D digital human is modularized, lacking system integration to generate a drivable 3D digital human. This paper proposes a 3D drivable digital human generation method, and constructs an end-to-end testing system. The test proves that it can realize the generation of 3D drivable digital human based on single-view RGB image, and realize the driven of it by video from ordinary camera.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"122 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87705629","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211106
Ning Yang, Haoyang Li, Dazhi He, Yin Xu, Yunfeng Guan, Zongze Li
The 3rd Generation Partnership Project (3GPP) has already introduced LTE-based 5G Release 16 (Rel-16) to integrate terrestrial broadcasting and mobile broadband network. However, the test results for Rel-16 broadcast transmissions in practical scenarios are deficient by far, and this issue may lead to a lack of targeted objectives for subsequent evolution. In this paper, a 5G R16 receiver is implemented based on FPGA. Moreover, a 5G Rel-16 transmission field test environment is deployed in Shenzhen, China. In combination with a local TV tower carrying the transmitter, a 270W Rel-16 video stream signal is transmitted. Meanwhile, the Rel-16 receiver is deployed in a vehicle to test its performance in practical urban mobile scenarios. The performance is mainly evaluated by the self-estimated Signal-to-Noise Ratio (SNR) and Block Error Ratio (BLER) under different parameters of the received signal. From the field test results, this FPGA based 5G R16 receiver could be applied in practice.
{"title":"An Enhanced LTE-based 5G Terrestrial Broadcast Receiver: FPGA Implement and Field Test","authors":"Ning Yang, Haoyang Li, Dazhi He, Yin Xu, Yunfeng Guan, Zongze Li","doi":"10.1109/BMSB58369.2023.10211106","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211106","url":null,"abstract":"The 3rd Generation Partnership Project (3GPP) has already introduced LTE-based 5G Release 16 (Rel-16) to integrate terrestrial broadcasting and mobile broadband network. However, the test results for Rel-16 broadcast transmissions in practical scenarios are deficient by far, and this issue may lead to a lack of targeted objectives for subsequent evolution. In this paper, a 5G R16 receiver is implemented based on FPGA. Moreover, a 5G Rel-16 transmission field test environment is deployed in Shenzhen, China. In combination with a local TV tower carrying the transmitter, a 270W Rel-16 video stream signal is transmitted. Meanwhile, the Rel-16 receiver is deployed in a vehicle to test its performance in practical urban mobile scenarios. The performance is mainly evaluated by the self-estimated Signal-to-Noise Ratio (SNR) and Block Error Ratio (BLER) under different parameters of the received signal. From the field test results, this FPGA based 5G R16 receiver could be applied in practice.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"106 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79265484","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211198
Di Zhai, Yang Lu, Cheng Zhong, Jiajia Tang, Z. Wang, Sujie Shao
With the rapid development of various wireless access technologies, various types of wireless communication networks integrate with each other and jointly provide services for users. However, different types of terminals in the power local wireless communication network have different service requests and QoS requirements. At the same time, due to the limited resources of a single network node, the collaboration of multiple nodes is required to meet the requirements of multi-dimensional resources such as communication, storage and computing. To solve the above problems, this paper proposes an integrated allocation method of storage and computing resources in local converged network. Firstly, an integrated allocation model of storage and computing resources in local converged network is established, and the three-dimensional cooperative allocation problem of storage and computing resources among multiple nodes in the network is transformed into a collaborative allocation problem of two-dimensional resources of storage and computing with delay attribute. An integrated allocation method of storage and computing resources is proposed to minimize delay and maximize network throughput, solve the problem of cooperative allocation of node resources in local converged networks, and improve the utilization rate of network resources.
{"title":"An Delay minimum Allocation Method for Integrated Communication, Storage and Computing Resources in power Local intergated Network","authors":"Di Zhai, Yang Lu, Cheng Zhong, Jiajia Tang, Z. Wang, Sujie Shao","doi":"10.1109/BMSB58369.2023.10211198","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211198","url":null,"abstract":"With the rapid development of various wireless access technologies, various types of wireless communication networks integrate with each other and jointly provide services for users. However, different types of terminals in the power local wireless communication network have different service requests and QoS requirements. At the same time, due to the limited resources of a single network node, the collaboration of multiple nodes is required to meet the requirements of multi-dimensional resources such as communication, storage and computing. To solve the above problems, this paper proposes an integrated allocation method of storage and computing resources in local converged network. Firstly, an integrated allocation model of storage and computing resources in local converged network is established, and the three-dimensional cooperative allocation problem of storage and computing resources among multiple nodes in the network is transformed into a collaborative allocation problem of two-dimensional resources of storage and computing with delay attribute. An integrated allocation method of storage and computing resources is proposed to minimize delay and maximize network throughput, solve the problem of cooperative allocation of node resources in local converged networks, and improve the utilization rate of network resources.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"128 1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79593527","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211175
Min Wei, Bo Lei, Xiaoyao Huang
Under the trend of multiple big data technologies, the development of multi-node collaborative parallel computing has made great advances. Limited by data security and data permissions, federated learning has become the current mainstream method to solve the difficult situation. However, for the huge number of computing nodes based on federated learning, there are many redundancies in client nodes. Therefore, the computing resource utilization and model training efficiency can be negatively affected. In this paper, in order to improve the computing efficiency in the parallel computing scenarios such as federated learning, we study a node selection algorithm based on machine learning. In the proposed algorithm, each node implements independent training based on the current global model. According to the contribution to model training accuracy, we select the computing nodes which will be aggregated in central sever afterwards by means of LASSO(Least Absolute Shrinkage and Selection Operator). Additionally, in the experimental simulation part, we add the standard federated averaging (FedAvg) method with random selection as comparison result. Experimental simulation shows that the combination of nodes selected by our proposed algorithm can achieve higher calculation efficiency, and improve the accuracy of federated learning and the mean square error of the accuracy.
{"title":"A Machine Learning Based Computing Node Selection Algorithm","authors":"Min Wei, Bo Lei, Xiaoyao Huang","doi":"10.1109/BMSB58369.2023.10211175","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211175","url":null,"abstract":"Under the trend of multiple big data technologies, the development of multi-node collaborative parallel computing has made great advances. Limited by data security and data permissions, federated learning has become the current mainstream method to solve the difficult situation. However, for the huge number of computing nodes based on federated learning, there are many redundancies in client nodes. Therefore, the computing resource utilization and model training efficiency can be negatively affected. In this paper, in order to improve the computing efficiency in the parallel computing scenarios such as federated learning, we study a node selection algorithm based on machine learning. In the proposed algorithm, each node implements independent training based on the current global model. According to the contribution to model training accuracy, we select the computing nodes which will be aggregated in central sever afterwards by means of LASSO(Least Absolute Shrinkage and Selection Operator). Additionally, in the experimental simulation part, we add the standard federated averaging (FedAvg) method with random selection as comparison result. Experimental simulation shows that the combination of nodes selected by our proposed algorithm can achieve higher calculation efficiency, and improve the accuracy of federated learning and the mean square error of the accuracy.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"28 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75767388","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211257
Jingyi Xue
In the context of the deep integration of radio & TV and network audiovisual media, this paper conducts a more comprehensive study on the content label system of audiovisual programmes in China through a number of steps such as question collection, questionnaire design, audience survey, institutional interviews, and data analysis. This paper innovatively proposed three labeling classes and content identifier that constitute the future label system of radio & TV and network audiovisual, effectively solved the current problem of inconsistent label discourse system and completing the whole set of work of "problem-investigation-analysis-solution".
{"title":"Research on the Content Label System of Radio & TV and Network Audiovisual Programmes","authors":"Jingyi Xue","doi":"10.1109/BMSB58369.2023.10211257","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211257","url":null,"abstract":"In the context of the deep integration of radio & TV and network audiovisual media, this paper conducts a more comprehensive study on the content label system of audiovisual programmes in China through a number of steps such as question collection, questionnaire design, audience survey, institutional interviews, and data analysis. This paper innovatively proposed three labeling classes and content identifier that constitute the future label system of radio & TV and network audiovisual, effectively solved the current problem of inconsistent label discourse system and completing the whole set of work of \"problem-investigation-analysis-solution\".","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"96 1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74951297","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211246
Yuhan Liu, Chaowei Wang, Danhao Deng, Yuan Yao, Ji Wang, Weidong Wang
With the development of communication technology and the increasing demand, there are more expectations for the research of multi-beam satellite communication system, and the efficient management and allocation of resources on the planet and the improvement of system performance are becoming more and more important. In this paper, the downlink Non-Orthogonal Multiple Access (NOMA) multi-beam satellite is used as the system model, and a greedy principle-based joint user grouping and subchannel joint access algorithm is proposed to solve the problems of user grouping and bandwidth allocation. On this basis, a power resource allocation algorithm based on deep reinforcement learning is proposed. By optimizing the reachability and rate of the system and user fairness under the constraints of the total transmission power and the user's minimum transmission rate, the optimization problem is modeled as Markov decision process, using the Proximal Policy Optimization (PPO) algorithm to achieve optimal allocation. The validity and superiority of the algorithm are verified by simulation, which is of great significance to the research of multi-beam satellite resource allocation.
{"title":"A DRL Resource Allocation for Downlink NOMA Multi-beam Satellite Communications","authors":"Yuhan Liu, Chaowei Wang, Danhao Deng, Yuan Yao, Ji Wang, Weidong Wang","doi":"10.1109/BMSB58369.2023.10211246","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211246","url":null,"abstract":"With the development of communication technology and the increasing demand, there are more expectations for the research of multi-beam satellite communication system, and the efficient management and allocation of resources on the planet and the improvement of system performance are becoming more and more important. In this paper, the downlink Non-Orthogonal Multiple Access (NOMA) multi-beam satellite is used as the system model, and a greedy principle-based joint user grouping and subchannel joint access algorithm is proposed to solve the problems of user grouping and bandwidth allocation. On this basis, a power resource allocation algorithm based on deep reinforcement learning is proposed. By optimizing the reachability and rate of the system and user fairness under the constraints of the total transmission power and the user's minimum transmission rate, the optimization problem is modeled as Markov decision process, using the Proximal Policy Optimization (PPO) algorithm to achieve optimal allocation. The validity and superiority of the algorithm are verified by simulation, which is of great significance to the research of multi-beam satellite resource allocation.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"25 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74987275","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 : 2023-06-14DOI: 10.1109/BMSB58369.2023.10211219
Aditi Vyas, Jung-Hyeon Kim, Qiang Wu
This study focuses on improving an existing technique of securing multimedia data transmission in a wireless network using a secret key. The proposed secret key generation algorithm extracts the key using localization information of the nodes participating in the communication in presence of an eavesdropper in which legitimate nodes could agree on a synchronized secret key, while eavesdroppers can only overhear limited information through the channel. Theoretical analysis of maximum secret key generation rate (MSR) for chain and hybrid topology has been done to evaluate the performance of the algorithm. Mathematical simulation has been done for MSR in terms of signal to noise ratio (SNR) and wireless network size which provides better insight to the localization based key extraction process. Results of simulations for small-scale and large-scale wireless models including an eavesdropper under Gaussian noise confirm the feasibility of the proposed algorithm, which can further be applied to multiple node mobile wireless network.
{"title":"Localization Information based Secret Key Generation in Chain and Hybrid Topology based Wireless Mobile Network to secure Multimedia Data","authors":"Aditi Vyas, Jung-Hyeon Kim, Qiang Wu","doi":"10.1109/BMSB58369.2023.10211219","DOIUrl":"https://doi.org/10.1109/BMSB58369.2023.10211219","url":null,"abstract":"This study focuses on improving an existing technique of securing multimedia data transmission in a wireless network using a secret key. The proposed secret key generation algorithm extracts the key using localization information of the nodes participating in the communication in presence of an eavesdropper in which legitimate nodes could agree on a synchronized secret key, while eavesdroppers can only overhear limited information through the channel. Theoretical analysis of maximum secret key generation rate (MSR) for chain and hybrid topology has been done to evaluate the performance of the algorithm. Mathematical simulation has been done for MSR in terms of signal to noise ratio (SNR) and wireless network size which provides better insight to the localization based key extraction process. Results of simulations for small-scale and large-scale wireless models including an eavesdropper under Gaussian noise confirm the feasibility of the proposed algorithm, which can further be applied to multiple node mobile wireless network.","PeriodicalId":13080,"journal":{"name":"IEEE international Symposium on Broadband Multimedia Systems and Broadcasting","volume":"71 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75121286","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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