Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118719
Tianrun Qi, Y. Sun, Jie Huang, Chenghai Wang
Nowadays, Reconfigurable intelligent surface (RIS) is regarded as one of the key technology of the sixth generation (6G) wireless communication systems. However, most of the current researches are based on single RIS. In this paper, we propose a three-dimensional (3D) double-RIS-assisted geometry-based stochastic model (GBSM) for massive multiple-input multiple-output (MIMO) communication systems. The channel model also supports the movements of transmitter, receiver, and clusters. For RIS, a new method is proposed for the joint design of reflection coefficients in MIMO channels based on cascaded RISs. In addition, different channel properties in the spatial domain, time domain, and frequency domain are studied to verify the validity and non-stationary properties of the model and explore the improvement of channel performance by double RISs.
{"title":"A Novel 3D Non-Stationary Double-RIS-Assisted Channel Model for 6G Wireless Communication Systems","authors":"Tianrun Qi, Y. Sun, Jie Huang, Chenghai Wang","doi":"10.1109/WCNC55385.2023.10118719","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118719","url":null,"abstract":"Nowadays, Reconfigurable intelligent surface (RIS) is regarded as one of the key technology of the sixth generation (6G) wireless communication systems. However, most of the current researches are based on single RIS. In this paper, we propose a three-dimensional (3D) double-RIS-assisted geometry-based stochastic model (GBSM) for massive multiple-input multiple-output (MIMO) communication systems. The channel model also supports the movements of transmitter, receiver, and clusters. For RIS, a new method is proposed for the joint design of reflection coefficients in MIMO channels based on cascaded RISs. In addition, different channel properties in the spatial domain, time domain, and frequency domain are studied to verify the validity and non-stationary properties of the model and explore the improvement of channel performance by double RISs.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123026586","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}
Over the past years, Human Activity Recognition (HAR) has shown its great value and has been further developed with the help of deep learning. However, existing HAR systems that use deep learning methods to achieve the ideal accuracy of recognition heavily rely on massive amounts of labeled training samples. Unfortunately, it requires considerable human effort and is unrealistic for real-life applications. In this paper, we propose a novel system, which combines active learning with WiFi-based HAR. The system is capable of building a good activities recognizer in HAR with a limited amount of labeled training samples. We thus call the system ALSensing. To the best of our knowledge, ALSensing is the first system to apply active learning to WiFi-based HAR. We implement ALSensing using commercial WiFi devices and evaluated it with realistic data in several different environments. Our experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% training samples, 58.97% recognition accuracy using 15% training samples and the baseline predicted with the existing method achieves 62.19% recognition accuracy using 100% training samples. When the performance of ALSensing is similar to that of the baseline, the required labeled samples are much less than that of the baseline.
{"title":"ALSensing: Human Activity Recognition using WiFi based on Active Learning","authors":"Guangzhi Zhao, Zhipeng Zhou, Yutao Huang, A. Nayak, Wei Gong, Haoquan Zhou","doi":"10.1109/WCNC55385.2023.10119036","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119036","url":null,"abstract":"Over the past years, Human Activity Recognition (HAR) has shown its great value and has been further developed with the help of deep learning. However, existing HAR systems that use deep learning methods to achieve the ideal accuracy of recognition heavily rely on massive amounts of labeled training samples. Unfortunately, it requires considerable human effort and is unrealistic for real-life applications. In this paper, we propose a novel system, which combines active learning with WiFi-based HAR. The system is capable of building a good activities recognizer in HAR with a limited amount of labeled training samples. We thus call the system ALSensing. To the best of our knowledge, ALSensing is the first system to apply active learning to WiFi-based HAR. We implement ALSensing using commercial WiFi devices and evaluated it with realistic data in several different environments. Our experimental results show that ALSensing achieves 52.83% recognition accuracy using 3.7% training samples, 58.97% recognition accuracy using 15% training samples and the baseline predicted with the existing method achieves 62.19% recognition accuracy using 100% training samples. When the performance of ALSensing is similar to that of the baseline, the required labeled samples are much less than that of the baseline.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129786910","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118653
Takuya Hasegawa, Mitsukuni Konishi, Y. Ohta, A. Nagate
A high-altitude platform station (HAPS) is a new mobile communication platform that directly provides mobile communication services such as fourth-generation long-term evolution (4G LTE) or fifth-generation new radio (5G NR) from the stratosphere to terrestrial user equipment (UE) by utilizing aircraft such as solar planes flying in the stratosphere. In this paper, we study on an automatic gain control (AGC) system for HAPS wireless repeater systems. We assume a repeater system, which uses different frequencies for the feeder and service links to avoid loop-back interference between the links. Measuring the receive power in the feeder link according to a known reference signal instead of the total receive power avoids fluctuations in the transmit power depending on the existence of traffic channels. We propose an AGC system for a HAPS system compatible with 5G NR, which enables the repeater to transmit at maximum power to maximize the coverage area regardless of changes in the propagation loss or polarization in the feeder link due to the flight of the HAPS aircraft. We conducted experiments to demonstrate the performance of the proposed AGC system.
{"title":"An Experimental Study on Automatic Gain Control in HAPS Wireless Repeater System","authors":"Takuya Hasegawa, Mitsukuni Konishi, Y. Ohta, A. Nagate","doi":"10.1109/WCNC55385.2023.10118653","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118653","url":null,"abstract":"A high-altitude platform station (HAPS) is a new mobile communication platform that directly provides mobile communication services such as fourth-generation long-term evolution (4G LTE) or fifth-generation new radio (5G NR) from the stratosphere to terrestrial user equipment (UE) by utilizing aircraft such as solar planes flying in the stratosphere. In this paper, we study on an automatic gain control (AGC) system for HAPS wireless repeater systems. We assume a repeater system, which uses different frequencies for the feeder and service links to avoid loop-back interference between the links. Measuring the receive power in the feeder link according to a known reference signal instead of the total receive power avoids fluctuations in the transmit power depending on the existence of traffic channels. We propose an AGC system for a HAPS system compatible with 5G NR, which enables the repeater to transmit at maximum power to maximize the coverage area regardless of changes in the propagation loss or polarization in the feeder link due to the flight of the HAPS aircraft. We conducted experiments to demonstrate the performance of the proposed AGC system.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128625323","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118940
Jingjing Zheng, Kai Li, N. Mhaisen, Wei Ni, E. Tovar, M. Guizani
Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device’s energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.
{"title":"Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach","authors":"Jingjing Zheng, Kai Li, N. Mhaisen, Wei Ni, E. Tovar, M. Guizani","doi":"10.1109/WCNC55385.2023.10118940","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118940","url":null,"abstract":"Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption. A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work. The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain. This framework provides the maximum accuracy ratio of FL divided by the device’s energy consumption. A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124620322","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118595
Dan Feng, B. Bai, Fei Wan
Recently, index modulated orthogonal time frequency space modulation with multi-input and multi-output (MIMO-OTFS) has been introduced to achieve better bit error rate (BER) performance than conventional MIMO-OTFS. To further utilize the multi-domain resources, in this paper, we present a transmission scheme called generalized space-delay-Doppler index modulated OTFS (GSDDIM-OTFS) to explore the potential advantages of the high dimensional index modulation, in which additional information bits are carried through the combined space-delay-Doppler resource units. Furthermore, the analytical expressions of average bit error probability (ABEP) are derived to evaluate the performance of the proposed scheme. Simulation results demonstrate the enhanced performance of the proposed scheme over doubly-selective fading channels.
{"title":"Generalised Space-Delay-Doppler Index Modulated OTFS Transmission","authors":"Dan Feng, B. Bai, Fei Wan","doi":"10.1109/WCNC55385.2023.10118595","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118595","url":null,"abstract":"Recently, index modulated orthogonal time frequency space modulation with multi-input and multi-output (MIMO-OTFS) has been introduced to achieve better bit error rate (BER) performance than conventional MIMO-OTFS. To further utilize the multi-domain resources, in this paper, we present a transmission scheme called generalized space-delay-Doppler index modulated OTFS (GSDDIM-OTFS) to explore the potential advantages of the high dimensional index modulation, in which additional information bits are carried through the combined space-delay-Doppler resource units. Furthermore, the analytical expressions of average bit error probability (ABEP) are derived to evaluate the performance of the proposed scheme. Simulation results demonstrate the enhanced performance of the proposed scheme over doubly-selective fading channels.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126873154","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118849
Yitu Wang, Runqi Dong, T. Nakachi, Wei Wang
Network traffic monitoring plays a crucial role in maintaining the security and reliability of the communication networks. Although Machine Learning (ML) assisted abnormal traffic detection has been emerged as a promising paradigm, the existing data-driven learning-based approaches are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. To this end, we establish an online learning framework for abnormality traffic detection by embracing Gaussian Process (GP) and Sparse Representation (SR). The contributions of this paper are two-fold: 1). We utilize a special kernel, i.e., mixture of Gaussian, to better explore and exploit the evolving traffic characteristics, so as to more accurately model network traffic. 2). To combat noise and modeling error, we formulate a feature vector based on Kullback-Leibler (KL) divergence to measure the difference between normal and abnormal traffic, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.
{"title":"A Light-weight Online Learning Framework for Network Traffic Abnormality Detection","authors":"Yitu Wang, Runqi Dong, T. Nakachi, Wei Wang","doi":"10.1109/WCNC55385.2023.10118849","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118849","url":null,"abstract":"Network traffic monitoring plays a crucial role in maintaining the security and reliability of the communication networks. Although Machine Learning (ML) assisted abnormal traffic detection has been emerged as a promising paradigm, the existing data-driven learning-based approaches are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. To this end, we establish an online learning framework for abnormality traffic detection by embracing Gaussian Process (GP) and Sparse Representation (SR). The contributions of this paper are two-fold: 1). We utilize a special kernel, i.e., mixture of Gaussian, to better explore and exploit the evolving traffic characteristics, so as to more accurately model network traffic. 2). To combat noise and modeling error, we formulate a feature vector based on Kullback-Leibler (KL) divergence to measure the difference between normal and abnormal traffic, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126883624","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10119011
Qiaojia Lu, Hanchen Lu, Xinyu Yang, Feihong Chen
With the widely deployed video sensors, Internet of Video Things (IoVT) has emerged as a new paradigm of Internet of Things (IoT). Due to limited computing capacity of video sensors and multi-device wireless environments in IoVT, uplink video transmission faces challenges brought by complex coding and heterogeneous channel conditions. To combat these challenges, we propose a multi-device uplink uncoded video transmission scheme (MDUcast). Different from traditional encoded video transmission systems, MDUcast performs efficient linear operations instead of complex coding to reduce computing requirements on video sensors as well as guarantee the reconstructed video quality proportional to channel conditions in heterogenous environments. Furthermore, in MDUcast, an optimal power allocation strategy and a subcarrier scheduling algorithm based on matching theory are proposed to approach the near-optimal performance for multi-device uplink transmission, where both channel diversity and video content diversity are exploited. Simulation results demonstrate that MDUcast outperforms conventional Softcast and Parcast in terms of peak signal-to-noise ratio under various scenarios.
{"title":"MDUcast: Multi-Device Uplink Uncoded Video Transmission in Internet of Video Things","authors":"Qiaojia Lu, Hanchen Lu, Xinyu Yang, Feihong Chen","doi":"10.1109/WCNC55385.2023.10119011","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119011","url":null,"abstract":"With the widely deployed video sensors, Internet of Video Things (IoVT) has emerged as a new paradigm of Internet of Things (IoT). Due to limited computing capacity of video sensors and multi-device wireless environments in IoVT, uplink video transmission faces challenges brought by complex coding and heterogeneous channel conditions. To combat these challenges, we propose a multi-device uplink uncoded video transmission scheme (MDUcast). Different from traditional encoded video transmission systems, MDUcast performs efficient linear operations instead of complex coding to reduce computing requirements on video sensors as well as guarantee the reconstructed video quality proportional to channel conditions in heterogenous environments. Furthermore, in MDUcast, an optimal power allocation strategy and a subcarrier scheduling algorithm based on matching theory are proposed to approach the near-optimal performance for multi-device uplink transmission, where both channel diversity and video content diversity are exploited. Simulation results demonstrate that MDUcast outperforms conventional Softcast and Parcast in terms of peak signal-to-noise ratio under various scenarios.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130590025","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10119030
Cezar Anicai, Muhammad Zeeshan Shakir
Physiological health indicators can provide valuable insights into the general health and well-being of a person. However, acquiring these indicators implies being physically connected to a medical device or using wearable sensors. Moreover, the aforementioned devices only measure the indicators but provide no information on what influences them. This study proposes an approach for estimating such indicators from ambient data, enabling simultaneously non-invasive monitoring and providing details on how the environment affects one’s health. A system based on Internet of Things (IoT) sensors is used for data collection and Machine Learning (ML) algorithms are employed for data analysis. The study focused on two health signals, Heart Rate (HR) and Skin Resistance (SR). Out of the three tested algorithms, Random Forest (RF) yielded the best results in terms of Mean Absolute Error (MAE) for both indicators. The results obtained proved that physiological signals estimation exclusively from ambient data is possible and identified which environmental factors are most important.
{"title":"IoT and Machine Learning Enabled Estimation of Health Indicators from Ambient Data","authors":"Cezar Anicai, Muhammad Zeeshan Shakir","doi":"10.1109/WCNC55385.2023.10119030","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10119030","url":null,"abstract":"Physiological health indicators can provide valuable insights into the general health and well-being of a person. However, acquiring these indicators implies being physically connected to a medical device or using wearable sensors. Moreover, the aforementioned devices only measure the indicators but provide no information on what influences them. This study proposes an approach for estimating such indicators from ambient data, enabling simultaneously non-invasive monitoring and providing details on how the environment affects one’s health. A system based on Internet of Things (IoT) sensors is used for data collection and Machine Learning (ML) algorithms are employed for data analysis. The study focused on two health signals, Heart Rate (HR) and Skin Resistance (SR). Out of the three tested algorithms, Random Forest (RF) yielded the best results in terms of Mean Absolute Error (MAE) for both indicators. The results obtained proved that physiological signals estimation exclusively from ambient data is possible and identified which environmental factors are most important.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130645363","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118793
K. Du, Luhan Wang, Zishen Zhu, Yunan Yan, X. Wen
Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies have driven mobile communication networks to evolve toward a Service-Based Architecture (SBA) with great flexibility to meet the highly-dynamic requirements of vertical applications. The SBA has been applied into 5th Generation (5G) core network and is evolving toward service-based end-to-end networks including virtualized Radio Access Networks (vRANs). This paper proposes a converged SBA (cSBA) to decouple access, control, and data planes. Specifically, the access plane with Distributed Units (DUs) functionalities is responsible for accessing User Equipments (UEs); the control plane consists of newly-defined converged services by integrating functionalities of Central Unit Control Planes (CU-CPs) and 5G core network for signaling processing; the data plane comprises Central Unit User Planes (CP-UPs) and User Plane Functions (UPFs), which are responsible for service data transmission. Optional converged services definition, service-based interface implementation, and cognitive service framework are proposed to enable an efficient cSBA. Finally, we evaluate the performance of the cSBA against the monolithic architecture.
软件定义网络(SDN)和网络功能虚拟化(NFV)技术推动移动通信网络向基于服务的架构(SBA)发展，具有极大的灵活性，以满足垂直应用的高动态需求。SBA已应用于第5代(5G)核心网，并正在向虚拟化无线接入网(vRANs)等基于业务的端到端网络发展。本文提出了一种将访问平面、控制平面和数据平面解耦的融合SBA (cSBA)。具体来说，具有分布式单元(du)功能的访问平面负责访问用户设备(ue);控制平面由新定义的融合业务组成，集成了中央单元控制平面(CU-CPs)和5G信令处理核心网的功能;数据平面包括cps (Central Unit User Planes)和upf (User plane Functions)，负责业务数据的传输。提出了可选的融合服务定义、基于服务的接口实现和认知服务框架，以实现高效的cSBA。最后，我们根据单片架构评估了cSBA的性能。
{"title":"Converged Service-based Architecture for Next-Generation Mobile Communication Networks","authors":"K. Du, Luhan Wang, Zishen Zhu, Yunan Yan, X. Wen","doi":"10.1109/WCNC55385.2023.10118793","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118793","url":null,"abstract":"Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies have driven mobile communication networks to evolve toward a Service-Based Architecture (SBA) with great flexibility to meet the highly-dynamic requirements of vertical applications. The SBA has been applied into 5th Generation (5G) core network and is evolving toward service-based end-to-end networks including virtualized Radio Access Networks (vRANs). This paper proposes a converged SBA (cSBA) to decouple access, control, and data planes. Specifically, the access plane with Distributed Units (DUs) functionalities is responsible for accessing User Equipments (UEs); the control plane consists of newly-defined converged services by integrating functionalities of Central Unit Control Planes (CU-CPs) and 5G core network for signaling processing; the data plane comprises Central Unit User Planes (CP-UPs) and User Plane Functions (UPFs), which are responsible for service data transmission. Optional converged services definition, service-based interface implementation, and cognitive service framework are proposed to enable an efficient cSBA. Finally, we evaluate the performance of the cSBA against the monolithic architecture.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371323","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 : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118594
W. Cai, Feng Ke, Yue Zhang
In a wireless device-to-device (D2D) network, mobile edge caching can reduce transmission cost for network traffic, but it may also cause outdated caching information. How to reduce the transmission cost and improve the freshness of information becomes an important issue. A dedicated caching device called helper which has a large cache can be used in a wireless D2D network, which can significantly improve the performance of network. In this paper, to better model the file-centric data transmission, we proposed a concept called age of file (AoF), which is defined as the duration from the latest updating of the file. We analyzed the AoF, energy cost and updating cost of the files in the network level. We comprehensively consider the AoF and energy cost through the maximum and minimum normalization methods, and proposed an AoF-based accessing strategy. In the strategy, users can adjust the access of files according to their demand for file freshness and improve the quality of service. The results indicate that this strategy can reduce the energy cost for file transmission and reduce the total cost of the entire network while satisfying the freshness of files.
在无线设备到设备(D2D)网络中，移动边缘缓存可以降低网络流量的传输成本，但也可能导致缓存信息过时。如何降低传播成本，提高信息的新鲜度成为一个重要的问题。在无线D2D网络中使用一种专用的缓存设备helper，它具有较大的缓存容量，可以显著提高网络的性能。为了更好地模拟以文件为中心的数据传输，本文提出了文件年龄(age of file, AoF)的概念，将其定义为从文件最近一次更新开始的持续时间。分析了网络层文件的AoF、能源成本和更新成本。通过最大和最小归一化方法综合考虑AoF和能源成本，提出了一种基于AoF的接入策略。在该策略中，用户可以根据自己对文件新鲜度的需求来调整对文件的访问，从而提高服务质量。结果表明，该策略在满足文件新鲜度的同时，降低了文件传输的能量成本，降低了整个网络的总成本。
{"title":"Freshness Aware Caching for Wireless D2D Network with Helpers","authors":"W. Cai, Feng Ke, Yue Zhang","doi":"10.1109/WCNC55385.2023.10118594","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118594","url":null,"abstract":"In a wireless device-to-device (D2D) network, mobile edge caching can reduce transmission cost for network traffic, but it may also cause outdated caching information. How to reduce the transmission cost and improve the freshness of information becomes an important issue. A dedicated caching device called helper which has a large cache can be used in a wireless D2D network, which can significantly improve the performance of network. In this paper, to better model the file-centric data transmission, we proposed a concept called age of file (AoF), which is defined as the duration from the latest updating of the file. We analyzed the AoF, energy cost and updating cost of the files in the network level. We comprehensively consider the AoF and energy cost through the maximum and minimum normalization methods, and proposed an AoF-based accessing strategy. In the strategy, users can adjust the access of files according to their demand for file freshness and improve the quality of service. The results indicate that this strategy can reduce the energy cost for file transmission and reduce the total cost of the entire network while satisfying the freshness of files.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121464468","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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