Pub Date : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454881
Amato Otsuki, D. Kominami, Hideyuki Shimonishi, Masayuki Murata, Tatsuya Otoshi
Network slicing technology is required to dynamically provide virtual networks in response to user requirements with a wide variety of services operating on the network. Generally, optimal allocation of virtual networks to resources on the real network is a combinatorial optimization problem, and it is difficult to find an exact solution in realistic time in the current large-scale and complex networks. In addition, user requirements change dynamically, and therefore, optimization methods that can cope with such temporal variations in the situation are required. In this paper, we propose a method to solve a virtual network embedding problem using quality-diversity (QD) algorithms, especially the MAP-Elites algorithm, and evaluate its effectiveness through computer simulations.
{"title":"Adaptive Network Slicing Control Method for Unpredictable Network Variations Using Quality-Diversity Algorithms","authors":"Amato Otsuki, D. Kominami, Hideyuki Shimonishi, Masayuki Murata, Tatsuya Otoshi","doi":"10.1109/CCNC51664.2024.10454881","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454881","url":null,"abstract":"Network slicing technology is required to dynamically provide virtual networks in response to user requirements with a wide variety of services operating on the network. Generally, optimal allocation of virtual networks to resources on the real network is a combinatorial optimization problem, and it is difficult to find an exact solution in realistic time in the current large-scale and complex networks. In addition, user requirements change dynamically, and therefore, optimization methods that can cope with such temporal variations in the situation are required. In this paper, we propose a method to solve a virtual network embedding problem using quality-diversity (QD) algorithms, especially the MAP-Elites algorithm, and evaluate its effectiveness through computer simulations.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"105 6","pages":"819-822"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531807","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454664
Vinay Kumar Shrivastava, Sriganesh Rajendran, Aby Kanneath Abraham, Rajavelsamy Rajadurai
Extended Reality, aka XR, is a promising technology which is expected to usher in an era of digital twin and meta-universe. 5G Advanced is directed to provide a communication framework to support XR applications and which, in turn, requires significant enhancements on the resource scheduling front to simultaneously fulfil low latency, high reliability and high data-rate requirements for XR. This paper provides a new scheduling strategy which effectively integrates frame-integrity and energy-efficiency awareness as prime factors in the XR scheduler design framework. With simulations we demonstrate the proposed approach outperforms traditional packet based scheduling approaches used in cellular communication networks, and achieves significant gains in terms of capacity enhancement and power saving performance.
{"title":"Enhanced Scheduling Strategy and Energy Efficiency for Extended Reality in 5G Advanced","authors":"Vinay Kumar Shrivastava, Sriganesh Rajendran, Aby Kanneath Abraham, Rajavelsamy Rajadurai","doi":"10.1109/CCNC51664.2024.10454664","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454664","url":null,"abstract":"Extended Reality, aka XR, is a promising technology which is expected to usher in an era of digital twin and meta-universe. 5G Advanced is directed to provide a communication framework to support XR applications and which, in turn, requires significant enhancements on the resource scheduling front to simultaneously fulfil low latency, high reliability and high data-rate requirements for XR. This paper provides a new scheduling strategy which effectively integrates frame-integrity and energy-efficiency awareness as prime factors in the XR scheduler design framework. With simulations we demonstrate the proposed approach outperforms traditional packet based scheduling approaches used in cellular communication networks, and achieves significant gains in terms of capacity enhancement and power saving performance.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"103 4","pages":"546-549"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531812","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454656
Michael Wentz, Jack Capper, Binoy G. Kurien, Keith Forsythe, Kaushik R. Chowdhury
Adaptive receiver beamforming processors typically require expert design and can be limited by their convergence rate in data-starved applications. In this paper, we present a new type of machine learning beamformer using classification-based transfer learning (CBTL) to alleviate these limitations. The architecture consists of a pre-trained signal classifier, in our case a convolutional neural network, prepended by a beamforming layer. Narrowband beamforming weights are optimized by minimizing the classification loss, in turn nulling interference and amplifying a signal of interest (SOI). There are no requirements for calibration of the array, synchronization to the SOI, or training data modulated by the SOI. We describe the CBTL beamformer and demonstrate its effectiveness using several modulated signals. Simulated performance was compared to two well-established methods for blind source separation, and we achieved average signal-to-interference-plus-noise ratio gains of 3 to 9 dB when fewer than 100 samples were available from a 4-element array. The technique shows promise for applications where there is limited prior knowledge and few samples are available for beamformer estimation.
自适应接收器波束成形处理器通常需要专家设计，在数据匮乏的应用中可能会受到收敛速度的限制。在本文中，我们提出了一种新型机器学习波束成形器，利用基于分类的迁移学习（CBTL）来缓解这些限制。该架构由一个预先训练好的信号分类器（在我们的例子中是一个卷积神经网络）和一个波束成形层组成。窄带波束成形权重通过最小化分类损失进行优化，反过来使干扰无效并放大感兴趣的信号（SOI）。对阵列校准、与 SOI 同步或由 SOI 调制的训练数据没有要求。我们介绍了 CBTL 波束形成器，并使用几个调制信号演示了其有效性。我们将其模拟性能与两种成熟的盲源分离方法进行了比较，当 4 元阵列的样本少于 100 个时，我们获得了 3 到 9 dB 的平均信号干扰加噪声比增益。在先验知识有限、可用于波束成形器估计的样本较少的情况下，该技术的应用前景广阔。
{"title":"Classification-Based Transfer Learning for Blind Adaptive Receiver Beamforming","authors":"Michael Wentz, Jack Capper, Binoy G. Kurien, Keith Forsythe, Kaushik R. Chowdhury","doi":"10.1109/CCNC51664.2024.10454656","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454656","url":null,"abstract":"Adaptive receiver beamforming processors typically require expert design and can be limited by their convergence rate in data-starved applications. In this paper, we present a new type of machine learning beamformer using classification-based transfer learning (CBTL) to alleviate these limitations. The architecture consists of a pre-trained signal classifier, in our case a convolutional neural network, prepended by a beamforming layer. Narrowband beamforming weights are optimized by minimizing the classification loss, in turn nulling interference and amplifying a signal of interest (SOI). There are no requirements for calibration of the array, synchronization to the SOI, or training data modulated by the SOI. We describe the CBTL beamformer and demonstrate its effectiveness using several modulated signals. Simulated performance was compared to two well-established methods for blind source separation, and we achieved average signal-to-interference-plus-noise ratio gains of 3 to 9 dB when fewer than 100 samples were available from a 4-element array. The technique shows promise for applications where there is limited prior knowledge and few samples are available for beamformer estimation.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"98 7","pages":"59-64"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531818","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454751
H. S. Rossi, Karan Mitra, C. Åhlund, Irina Cotanis
Subjective Quality of Experience (QoE) studies often require setting up complex lab environments to study users' perceptions of the application or service under controlled test conditions. These lab environments must control applications and devices to generate the required test conditions accurately, reliably, repeatedly, and error-free under study. Further, the data collection should be performed on many devices, such as clients and servers, often in real-time, and correctly labelled according to each test condition. To circumvent the complex task of configuring the lab environment and the laborious and error-prone work of data collection, we demonstrate ALTRUIST, a multi-platform tool to conduct subjective tests efficiently. In particular, we present the use of ALTRUIST in two lab setups involving immersive applications such as mobile cloud gaming and virtual reality gaming.
{"title":"A Demonstration of ALTRUIST for Conducting QoE Subjective Tests in Immersive Systems","authors":"H. S. Rossi, Karan Mitra, C. Åhlund, Irina Cotanis","doi":"10.1109/CCNC51664.2024.10454751","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454751","url":null,"abstract":"Subjective Quality of Experience (QoE) studies often require setting up complex lab environments to study users' perceptions of the application or service under controlled test conditions. These lab environments must control applications and devices to generate the required test conditions accurately, reliably, repeatedly, and error-free under study. Further, the data collection should be performed on many devices, such as clients and servers, often in real-time, and correctly labelled according to each test condition. To circumvent the complex task of configuring the lab environment and the laborious and error-prone work of data collection, we demonstrate ALTRUIST, a multi-platform tool to conduct subjective tests efficiently. In particular, we present the use of ALTRUIST in two lab setups involving immersive applications such as mobile cloud gaming and virtual reality gaming.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"64 6","pages":"1120-1121"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531828","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454832
Gustavo Flores, A. Nahapetian
In this work, we provide a new software input mechanism using solely the smart phone gyroscope to detect taps made near the phone. Multiple experiments were conducted to determine the best conditions for enabling an interface that leverages taps made to a surface on which a device is placed. The experiments considered the impact of the phone type, the presence of a phone case, the surface type, and the sample rate. Taps made in eight different directions and at different distances ranging from on the side of the phone to 10 inches away from the phone were classified. The results motivated a keyboard layout that extends out from the smart phone, tripling the tapping area while still allowing full use of the smart phone touch screen. The successful classification of taps made on the prototype, called the keyboardless keyboard, opens a range of possibilities for an input interface which requires no hardware other than the smartphone's own gyroscope.
{"title":"Keyboardless Keyboard: Smart Phone Gyroscope for Improved User Interface","authors":"Gustavo Flores, A. Nahapetian","doi":"10.1109/CCNC51664.2024.10454832","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454832","url":null,"abstract":"In this work, we provide a new software input mechanism using solely the smart phone gyroscope to detect taps made near the phone. Multiple experiments were conducted to determine the best conditions for enabling an interface that leverages taps made to a surface on which a device is placed. The experiments considered the impact of the phone type, the presence of a phone case, the surface type, and the sample rate. Taps made in eight different directions and at different distances ranging from on the side of the phone to 10 inches away from the phone were classified. The results motivated a keyboard layout that extends out from the smart phone, tripling the tapping area while still allowing full use of the smart phone touch screen. The successful classification of taps made on the prototype, called the keyboardless keyboard, opens a range of possibilities for an input interface which requires no hardware other than the smartphone's own gyroscope.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"73 4","pages":"472-477"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531834","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454897
Thanh Trung Nguyen, Minh Hai Vu, Thi Ha Ly Dinh, Phi Le Nguyen, Kien Nguyen
Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.
{"title":"MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler","authors":"Thanh Trung Nguyen, Minh Hai Vu, Thi Ha Ly Dinh, Phi Le Nguyen, Kien Nguyen","doi":"10.1109/CCNC51664.2024.10454897","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454897","url":null,"abstract":"Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"84 12","pages":"656-661"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531885","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454748
Yerin Lee, Howon Lee
The integration of the integrated access and back-haul (IAB) network and tethered flying platform (TFP) solves the performance degradation problem of airborne base stations (ABS) due to battery constraints and provides flexibility in topology. Therefore, this study proposes a distributed deep Q-Network (DQN)-based resource allocation and tethered unmanned aerial vehicles (TUAVs) placement control (RAPC) joint optimization scheme to maximize the total sum rate of IAB network supported by TUAVs and tethered balloon (TB). Simulations demonstrate that the RAPC achieves a high aggregate total sum rate compared to several benchmarks, and has robust performance maintained in various ground users (GUs) moving speed environments.
{"title":"Resource Allocation and Placement for Tethered Flying Platform-Aided IAB Network: Distributed DQN Approach","authors":"Yerin Lee, Howon Lee","doi":"10.1109/CCNC51664.2024.10454748","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454748","url":null,"abstract":"The integration of the integrated access and back-haul (IAB) network and tethered flying platform (TFP) solves the performance degradation problem of airborne base stations (ABS) due to battery constraints and provides flexibility in topology. Therefore, this study proposes a distributed deep Q-Network (DQN)-based resource allocation and tethered unmanned aerial vehicles (TUAVs) placement control (RAPC) joint optimization scheme to maximize the total sum rate of IAB network supported by TUAVs and tethered balloon (TB). Simulations demonstrate that the RAPC achieves a high aggregate total sum rate compared to several benchmarks, and has robust performance maintained in various ground users (GUs) moving speed environments.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"84 2","pages":"1096-1097"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531886","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 the 5G New Radio (NR) millimeter(mm) wave hybrid beamforming based antenna systems, the number of simultaneous beams limited by the number of Radio Frequency (RF) chains (also limits simultaneous Angle of Arrival (AoA) estimation of signals coming from multiple directions). The Base Station (BS) can identify antenna beams for each user equipment (UE) through appropriate channel measurements. However, this process is very time consuming since BS with limited number of simultaneous antenna beams has to examine a large number of beam directions (or AoA) due to the narrow beams employed to enhance the beam searching capability. Through this manuscript we propose a novel idea to enable simultaneous AoA estimation of signals coming from different directions for a single RF chain. This technique helps to bypass transmit beam sweeping procedure, by which the UE initial access latency to find the transmit beam reduces by approximately 7 times compared to legacy systems, and this improves the Key Performance Indices.
{"title":"A Novel Framework to Reduce the Initial Access Latency by Utilizing Multi Resolution Angle of Arrival Estimation in 6G","authors":"Sridhar Kondabathini, Lalit Pathak, Kiran Bynam, Jyotirmov Karjee","doi":"10.1109/CCNC51664.2024.10454805","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454805","url":null,"abstract":"In the 5G New Radio (NR) millimeter(mm) wave hybrid beamforming based antenna systems, the number of simultaneous beams limited by the number of Radio Frequency (RF) chains (also limits simultaneous Angle of Arrival (AoA) estimation of signals coming from multiple directions). The Base Station (BS) can identify antenna beams for each user equipment (UE) through appropriate channel measurements. However, this process is very time consuming since BS with limited number of simultaneous antenna beams has to examine a large number of beam directions (or AoA) due to the narrow beams employed to enhance the beam searching capability. Through this manuscript we propose a novel idea to enable simultaneous AoA estimation of signals coming from different directions for a single RF chain. This technique helps to bypass transmit beam sweeping procedure, by which the UE initial access latency to find the transmit beam reduces by approximately 7 times compared to legacy systems, and this improves the Key Performance Indices.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"76 8","pages":"630-631"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531903","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454787
Shoki Ohta, Cheng Chen, Takayuki Nishio
This paper demonstrates the feasibility of millimeter-wave (mmWave) link quality prediction based on out-of-band channel state information (CSI) fingerprinting. To overcome the pedestrian blockage problem of mmWave communications, a large number of computer vision-aided mmWave link quality prediction methods have been investigated. However, the use of cameras and LiDAR to acquire computer vision information entails privacy risks. In this paper, we employ 5 GHz band CSI fingerprinting - an aggregation of CSI measured at multiple locations, for mmWave link quality prediction. CSI reflects the propagation environment of the wireless communication channel and thus includes information on pedestrians that block mmWave communications. CSI fingerprinting, aggregated from various measurement locations, enables future mmWave link quality prediction owing to its sufficient spatial information. We conducted a real-world wireless communication experiment with commercial devices compliant with IEEE 802.11ad for the mmWave, and nine IEEE 802.11ac CSI measurement devices for 5 GHz, to experimentally evaluate our method. The experimental result revealed that our proposed method can deterministically and numerically predict the mmWave link quality deterioration caused by pedestrian blockage 500 ms in advance.
{"title":"Proactive Millimeter-Wave Link Quality Prediction Utilizing Out-of-Band CSI Fingerprinting and Supervised Learning: An Experimental Study","authors":"Shoki Ohta, Cheng Chen, Takayuki Nishio","doi":"10.1109/CCNC51664.2024.10454787","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454787","url":null,"abstract":"This paper demonstrates the feasibility of millimeter-wave (mmWave) link quality prediction based on out-of-band channel state information (CSI) fingerprinting. To overcome the pedestrian blockage problem of mmWave communications, a large number of computer vision-aided mmWave link quality prediction methods have been investigated. However, the use of cameras and LiDAR to acquire computer vision information entails privacy risks. In this paper, we employ 5 GHz band CSI fingerprinting - an aggregation of CSI measured at multiple locations, for mmWave link quality prediction. CSI reflects the propagation environment of the wireless communication channel and thus includes information on pedestrians that block mmWave communications. CSI fingerprinting, aggregated from various measurement locations, enables future mmWave link quality prediction owing to its sufficient spatial information. We conducted a real-world wireless communication experiment with commercial devices compliant with IEEE 802.11ad for the mmWave, and nine IEEE 802.11ac CSI measurement devices for 5 GHz, to experimentally evaluate our method. The experimental result revealed that our proposed method can deterministically and numerically predict the mmWave link quality deterioration caused by pedestrian blockage 500 ms in advance.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"71 1","pages":"248-253"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531924","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 : 2024-01-06DOI: 10.1109/CCNC51664.2024.10454712
Haoting Zhang, Hiroshi Yamamoto
In order to prevent the lifestyle diseases, it is clarified that the recording of lifelog data related with the location and motions of people is effective. However, the power consumption of the sensor device measuring the lifelog data should be reduced to observe users in a wide area for a long time. On the other hand, an energy harvesting technology that can obtain electric power from nearby energy sources such as solar light, wind, heat, and vibration is attracting attention as a viable alternative to conventional batteries. In addition, to achieve wide-area communication, the mesh network technology is attracting attention because the wide-area sensor network can be constructed even if the communication distance of each sensor device should be short to reduce the power consumption. However, if a power supply from the energy harvesting is very limited, prolonged operation of the wide-area mesh network is challenging because the sensor node can work as a part of the mesh network only in a very limited time. Therefore, in this study, we propose a new method for constructing a mesh sensor network system by controlling the timing of wake-up of sensor devices based on the states of power generation by energy harvesting.
{"title":"Lifelog Mesh Sensor Network System Supporting Wake-Up Control Function Based on States of Power Generation","authors":"Haoting Zhang, Hiroshi Yamamoto","doi":"10.1109/CCNC51664.2024.10454712","DOIUrl":"https://doi.org/10.1109/CCNC51664.2024.10454712","url":null,"abstract":"In order to prevent the lifestyle diseases, it is clarified that the recording of lifelog data related with the location and motions of people is effective. However, the power consumption of the sensor device measuring the lifelog data should be reduced to observe users in a wide area for a long time. On the other hand, an energy harvesting technology that can obtain electric power from nearby energy sources such as solar light, wind, heat, and vibration is attracting attention as a viable alternative to conventional batteries. In addition, to achieve wide-area communication, the mesh network technology is attracting attention because the wide-area sensor network can be constructed even if the communication distance of each sensor device should be short to reduce the power consumption. However, if a power supply from the energy harvesting is very limited, prolonged operation of the wide-area mesh network is challenging because the sensor node can work as a part of the mesh network only in a very limited time. Therefore, in this study, we propose a new method for constructing a mesh sensor network system by controlling the timing of wake-up of sensor devices based on the states of power generation by energy harvesting.","PeriodicalId":518411,"journal":{"name":"2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)","volume":"113 5","pages":"484-489"},"PeriodicalIF":0.0,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140531638","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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