2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)最新文献_第8页

Coordinated Dynamic Spectrum Sharing Between Terrestrial and Non-Terrestrial Networks in 5G and Beyond 5G及以后地面与非地面网络的协调动态频谱共享

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2023-04-24 DOI: 10.1109/WoWMoM57956.2023.00074

H. Martikainen, Mikko Majamaa, J. Puttonen

{"title":"Coordinated Dynamic Spectrum Sharing Between Terrestrial and Non-Terrestrial Networks in 5G and Beyond","authors":"H. Martikainen, Mikko Majamaa, J. Puttonen","doi":"10.1109/WoWMoM57956.2023.00074","DOIUrl":"https://doi.org/10.1109/WoWMoM57956.2023.00074","url":null,"abstract":"The emerging Non-Terrestrial Networks (NTNs) can aid to provide 5G and beyond services everywhere and anytime. However, the vast emergence of NTN systems will introduce an unseen interference to both the existing satellite systems and Terrestrial Networks (TNs). For that, there is a need for novel ideas on how to efficiently utilize the co-existing systems with the ever-increasing competition on scarce spectrum resources. Dynamic Spectrum Sharing (DSS) is a promising technique in which different systems can operate on the same spectrum, thus increasing the spectrum efficiency and offering better coverage for the users. In this paper, we present a centralized scheme for achieving coordinated DSS to protect the primary TN while providing NTN with sufficient resources. The scheme is evaluated by system simulations in a scenario with a TN and low earth orbit satellite. The results reveal that in a low traffic demand situation, the primary TN users are not affected negatively while the NTN can provide service to the rural area. In high-demand traffic situations, the peak performance of the TN inevitably suffers but the TN cell edge and NTN users’ performance is improved.","PeriodicalId":132845,"journal":{"name":"2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128237445","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: 1

Satellite-Assisted Multi-Connectivity in Beyond 5G 超5G中的卫星辅助多连接

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2023-04-24 DOI: 10.1109/WoWMoM57956.2023.00073

Mikko Majamaa, H. Martikainen, J. Puttonen, T. Hämäläinen

{"title":"Satellite-Assisted Multi-Connectivity in Beyond 5G","authors":"Mikko Majamaa, H. Martikainen, J. Puttonen, T. Hämäläinen","doi":"10.1109/WoWMoM57956.2023.00073","DOIUrl":"https://doi.org/10.1109/WoWMoM57956.2023.00073","url":null,"abstract":"Due to the ongoing standardization and deployment activities, satellite networks will be supplementing the 5G and beyond Terrestrial Networks (TNs). For the satellite communications involved to be as efficient as possible, techniques to achieve that should be used. Multi-Connectivity (MC), in which a user can be connected to multiple Next Generation Node Bs simultaneously, is one such technique. However, the technique is not well-researched in the satellite environment. In this paper, an algorithm to activate MC for users in the weakest radio conditions is introduced. The algorithm operates dynamically, considering deactivation of MC to prioritize users in weaker conditions when necessary. The algorithm is evaluated with a packet-level 5G non-terrestrial network system simulator in a scenario that consists of a TN and transparent payload low earth orbit satellite. The algorithm outperforms the benchmark algorithms. The usage of MC with the algorithm increases the mean throughput of the users by 20.3% and the 5th percentile throughput by 83.5% compared to when MC is turned off.","PeriodicalId":132845,"journal":{"name":"2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127356230","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: 1

FTMRate: Collision-Immune Distance-based Data Rate Selection for IEEE 802.11 Networks FTMRate: IEEE 802.11网络中基于冲突免疫距离的数据速率选择

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2023-04-20 DOI: 10.1109/WoWMoM57956.2023.00039

Wojciech Ciezobka, Maksymilian Wojnar, Katarzyna Kosek-Szott, S. Szott, Krzysztof Rusek

{"title":"FTMRate: Collision-Immune Distance-based Data Rate Selection for IEEE 802.11 Networks","authors":"Wojciech Ciezobka, Maksymilian Wojnar, Katarzyna Kosek-Szott, S. Szott, Krzysztof Rusek","doi":"10.1109/WoWMoM57956.2023.00039","DOIUrl":"https://doi.org/10.1109/WoWMoM57956.2023.00039","url":null,"abstract":"Data rate selection algorithms for Wi-Fi devices are an important area of research because they directly impact performance. Most of the proposals are based on measuring the transmission success probability for a given data rate. In dense scenarios, however, this probing approach will fail because frame collisions are misinterpreted as erroneous data rate selection. We propose FTMRate which uses the fine timing measurement (FTM) feature, recently introduced in IEEE 802.11. FTM allows stations to measure their distance from the AP. We argue that knowledge of the distance from the receiver can be useful in determining which data rate to use. We apply statistical learning (a form of machine learning) to estimate the distance based on measurements, estimate channel quality from the distance, and select data rates based on channel quality. We evaluate three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. We present a performance evaluation of the three variants of FTMRate and show, in several dense and mobile (though line-of-sight only) scenarios, that it can outperform two benchmarks and provide close to optimal results in IEEE 802.11ax networks.","PeriodicalId":132845,"journal":{"name":"2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127814899","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

Deep Reinforcement Learning with Importance Weighted A3C for QoE enhancement in Video Delivery Services 基于重要性加权A3C的视频交付服务QoE增强深度强化学习

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2023-04-10 DOI: 10.1109/WoWMoM57956.2023.00024

Mandan Naresh, Paresh Saxena, Manik Gupta

{"title":"Deep Reinforcement Learning with Importance Weighted A3C for QoE enhancement in Video Delivery Services","authors":"Mandan Naresh, Paresh Saxena, Manik Gupta","doi":"10.1109/WoWMoM57956.2023.00024","DOIUrl":"https://doi.org/10.1109/WoWMoM57956.2023.00024","url":null,"abstract":"Adaptive bitrate (ABR) algorithms are used to adapt the video bitrate based on the network conditions to improve the overall video quality of experience (QoE). Recently, reinforcement learning (RL) and asynchronous advantage actor-critic (A3C) methods have been used to generate adaptive bit rate algorithms and they have been shown to improve the overall QoE as compared to fixed rule ABR algorithms. However, a common issue in the A3C methods is the lag between behaviour policy and target policy. As a result, the behaviour and the target policies are no longer synchronized which results in suboptimal updates. In this work, we present ALISA: An Actor-Learner Architecture with Importance Sampling for efficient learning in ABR algorithms. ALISA incorporates importance sampling weights to give more weightage to relevant experience to address the lag issues with the existing A3C methods. We present the design and implementation of ALISA, and compare its performance to state-of-the-art video rate adaptation algorithms including vanilla A3C implemented in the Pensieve framework and other fixed-rule schedulers like BB, BOLA, and RB. Our results show that ALISA improves average QoE by up to 25%-48% higher average QoE than Pensieve, and even more when compared to fixed-rule schedulers.","PeriodicalId":132845,"journal":{"name":"2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115542260","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: 1

SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals SiMWiSense:通过Wi-Fi信号同时进行多主体活动分类

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2023-03-31 DOI: 10.1109/WoWMoM57956.2023.00019

Khandaker Foysal Haque, Milin Zhang, Francesco Restuccia

{"title":"SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals","authors":"Khandaker Foysal Haque, Milin Zhang, Francesco Restuccia","doi":"10.1109/WoWMoM57956.2023.00019","DOIUrl":"https://doi.org/10.1109/WoWMoM57956.2023.00019","url":null,"abstract":"Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities. Moreover, it is known that Wi-Fi sensing systems struggle to adapt to new environments and subjects. To address both issues, we propose SiMWiSense, the first framework for simultaneous multi-subject activity classification based on Wi-Fi that generalizes to multiple environments and subjects. We address the scalability issue by using the Channel State Information (CSI) computed from the device positioned closest to the subject. We experimentally prove this intuition by confirming that the best accuracy is experienced when the CSI computed by the transceiver positioned closest to the subject is used for classification. To address the generalization issue, we develop a brand-new few-shot learning algorithm named Feature Reusable Embedding Learning (FREL). Through an extensive data collection campaign in 3 different environments and 3 subjects performing 20 different activities simultaneously, we demonstrate that SiMWiSense achieves classification accuracy of up to 97%, while FREL improves the accuracy by 85% in comparison to a traditional Convolutional Neural Network (CNN) and up to 20% when compared to the state-of-the-art few-shot embedding learning (FSEL), by using only 15 seconds of additional data for each class. For reproducibility purposes, we share our 1 TB dataset and code repository1 [1].1https://github.com/kfoysalhaque/SiMWiSense","PeriodicalId":132845,"journal":{"name":"2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121934552","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: 1

WIP: Federated Learning for Routing in Swarm Based Distributed Multi-Hop Networks 基于群的分布式多跳网络中路由的联邦学习

2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) Pub Date : 2023-03-15 DOI: 10.1109/WoWMoM57956.2023.00049

M. Cash, J. Murphy, A. Wyglinski

引用次数: 1