2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)最新文献_第3页

Adaptive Network Slicing Control Method for Unpredictable Network Variations Using Quality-Diversity Algorithms 使用质量多样性算法的自适应网络切片控制方法，应对不可预测的网络变化

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454881

Amato Otsuki, D. Kominami, Hideyuki Shimonishi, Masayuki Murata, Tatsuya Otoshi

引用次数: 0

Enhanced Scheduling Strategy and Energy Efficiency for Extended Reality in 5G Advanced 增强调度策略和能效，实现 5G 先进网络中的扩展现实

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454664

Vinay Kumar Shrivastava, Sriganesh Rajendran, Aby Kanneath Abraham, Rajavelsamy Rajadurai

引用次数: 0

Classification-Based Transfer Learning for Blind Adaptive Receiver Beamforming 基于分类的盲自适应接收器波束成形转移学习

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454656

Michael Wentz, Jack Capper, Binoy G. Kurien, Keith Forsythe, Kaushik R. Chowdhury

{"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}

引用次数: 0

A Demonstration of ALTRUIST for Conducting QoE Subjective Tests in Immersive Systems ALTRUIST 用于在沉浸式系统中进行 QoE 主观测试的演示

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454751

H. S. Rossi, Karan Mitra, C. Åhlund, Irina Cotanis

引用次数: 0

Keyboardless Keyboard: Smart Phone Gyroscope for Improved User Interface 无键键盘智能手机陀螺仪改进用户界面

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454832

Gustavo Flores, A. Nahapetian

引用次数: 0

MuLeS: A Multi-Client Learning-Based MPQUIC Scheduler MuLeS：基于多客户端学习的 MPQUIC 调度器

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454897

Thanh Trung Nguyen, Minh Hai Vu, Thi Ha Ly Dinh, Phi Le Nguyen, Kien Nguyen

{"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}

引用次数: 0

Resource Allocation and Placement for Tethered Flying Platform-Aided IAB Network: Distributed DQN Approach 系留飞行平台辅助 IAB 网络的资源分配与安置：分布式 DQN 方法

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454748

Yerin Lee, Howon Lee

引用次数: 0

A Novel Framework to Reduce the Initial Access Latency by Utilizing Multi Resolution Angle of Arrival Estimation in 6G 在 6G 中利用多分辨率到达角估计减少初始接入延迟的新框架

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454805

Sridhar Kondabathini, Lalit Pathak, Kiran Bynam, Jyotirmov Karjee

引用次数: 0

Proactive Millimeter-Wave Link Quality Prediction Utilizing Out-of-Band CSI Fingerprinting and Supervised Learning: An Experimental Study 利用带外 CSI 指纹和监督学习进行主动毫米波链路质量预测：实验研究

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454787

Shoki Ohta, Cheng Chen, Takayuki Nishio

{"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}

引用次数: 0

Lifelog Mesh Sensor Network System Supporting Wake-Up Control Function Based on States of Power Generation 支持基于发电状态的唤醒控制功能的 Lifelog 网状传感器网络系统

2024 IEEE 21st Consumer Communications & Networking Conference (CCNC) Pub Date : 2024-01-06 DOI: 10.1109/CCNC51664.2024.10454712

Haoting Zhang, Hiroshi Yamamoto

{"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}

引用次数: 0