Eman Ramadan, Arvind Narayanan, Udhaya Kumar Dayalan, Rostand A. K. Fezeu, Feng Qian, Zhi-Li Zhang
Recent measurement studies show that commercial mmWave 5G can indeed offer ultra-high bandwidth (up to 2 Gbps), capable of supporting bandwidth-intensive applications such as ultra-HD (UHD) 4K/8K and volumetric video streaming on mobile devices. However, mmWave 5G also exhibits highly variable throughput performance and incurs frequent handoffs (e.g., between 5G and 4G), due to its directional nature, signal blockage and other environmental factors, especially when the device is mobile. All these issues make it difficult for applications to achieve high Quality of Experience (QoE). In this paper, we advance several new mechanisms to tackle the challenges facing UHD video streaming applications over 5G networks, thereby making them {em 5G-aware}. We argue for the need to employ machine learning (ML) for effective throughput prediction to aid applications in intelligent bitrate adaptation. Furthermore, we advocate {em adaptive content bursting}, and {em dynamic radio (band) switching} to allow the 5G radio network to fully utilize the available radio resources under good channel/beam conditions, whereas dynamically switched radio channels/bands (e.g., from 5G high-band to low-band, or 5G to 4G) to maintain session connectivity and ensure a minimal bitrate. We conduct initial evaluation using real-world 5G throughput measurement traces. Our results show these mechanisms can help minimize, if not completely eliminate, video stalls, despite wildly varying 5G throughput.
{"title":"Case for 5G-aware video streaming applications","authors":"Eman Ramadan, Arvind Narayanan, Udhaya Kumar Dayalan, Rostand A. K. Fezeu, Feng Qian, Zhi-Li Zhang","doi":"10.1145/3472771.3474036","DOIUrl":"https://doi.org/10.1145/3472771.3474036","url":null,"abstract":"Recent measurement studies show that commercial mmWave 5G can indeed offer ultra-high bandwidth (up to 2 Gbps), capable of supporting bandwidth-intensive applications such as ultra-HD (UHD) 4K/8K and volumetric video streaming on mobile devices. However, mmWave 5G also exhibits highly variable throughput performance and incurs frequent handoffs (e.g., between 5G and 4G), due to its directional nature, signal blockage and other environmental factors, especially when the device is mobile. All these issues make it difficult for applications to achieve high Quality of Experience (QoE). In this paper, we advance several new mechanisms to tackle the challenges facing UHD video streaming applications over 5G networks, thereby making them {em 5G-aware}. We argue for the need to employ machine learning (ML) for effective throughput prediction to aid applications in intelligent bitrate adaptation. Furthermore, we advocate {em adaptive content bursting}, and {em dynamic radio (band) switching} to allow the 5G radio network to fully utilize the available radio resources under good channel/beam conditions, whereas dynamically switched radio channels/bands (e.g., from 5G high-band to low-band, or 5G to 4G) to maintain session connectivity and ensure a minimal bitrate. We conduct initial evaluation using real-world 5G throughput measurement traces. Our results show these mechanisms can help minimize, if not completely eliminate, video stalls, despite wildly varying 5G throughput.","PeriodicalId":270618,"journal":{"name":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126870673","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}
Machine Learning (ML) algorithms are proposed to replace conventional algorithms in the area of wireless networking. Many of the suggested algorithms are often based on simulators or smallscale test-beds. We provide a study based on a dataset collected over a large commercial network, and highlight some of the real network dynamics that learning agents need to cope with. Our dataset includes not only measurements from the User Equipment (UE) but also integrates information from the network. Based on the collected data, we highlight some of the aspects that are important for the design of learning agents and discuss potential dataset characteristics that might hinder the learning process. Then we discuss what dataset characteristics can facilitate the deployment of ML algorithms in the real networks. Finally, we showcase how throughput prediction can be implemented by using ML techniques and provide some examples and insights on feature engineering and the training process.
{"title":"Learning from large-scale commercial networks: challenges and knowledge extraction towards machine learning use cases","authors":"Roman Zhohov, Alexandros Palaios, Philipp Geuer","doi":"10.1145/3472771.3472773","DOIUrl":"https://doi.org/10.1145/3472771.3472773","url":null,"abstract":"Machine Learning (ML) algorithms are proposed to replace conventional algorithms in the area of wireless networking. Many of the suggested algorithms are often based on simulators or smallscale test-beds. We provide a study based on a dataset collected over a large commercial network, and highlight some of the real network dynamics that learning agents need to cope with. Our dataset includes not only measurements from the User Equipment (UE) but also integrates information from the network. Based on the collected data, we highlight some of the aspects that are important for the design of learning agents and discuss potential dataset characteristics that might hinder the learning process. Then we discuss what dataset characteristics can facilitate the deployment of ML algorithms in the real networks. Finally, we showcase how throughput prediction can be implemented by using ML techniques and provide some examples and insights on feature engineering and the training process.","PeriodicalId":270618,"journal":{"name":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127649875","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}
Bringing cellular capacity into modern trains is challenging because they act as Faraday cages. Building a radio frequency (RF) corridor along the railway tracks ensures a high signal-to-noise ratio and limits handovers. However, building such RF corridors is difficult because of the administrative burden of excessive formalities to obtain construction permissions and costly because of the sheer number of base stations. Our contribution in this paper is an unconventional solution of mmWave fronthauled low-power out-of-band repeater nodes deployed in short intervals on existing masts between high-power macro cell sites. The paper demonstrates the feasibility of the concept with an extensive measurement campaign on a commercial railway line. The benefit of using many low-power nodes with low-gain antennas compared to a baseline with only high-gain macro antennas is discussed, and the coverage improvement is evaluated. Based on the measurement results, a simple path loss model is calibrated. This model allows evaluation of the potential of the mmWave repeater architecture to increase the macro cell inter-site distance and reduce deployment costs.
{"title":"Improving railway track coverage with mmWave bridges: A Measurement Campaign","authors":"Adrian Schumacher, N. Jamaly, R. Merz, A. Burg","doi":"10.1145/3472771.3472774","DOIUrl":"https://doi.org/10.1145/3472771.3472774","url":null,"abstract":"Bringing cellular capacity into modern trains is challenging because they act as Faraday cages. Building a radio frequency (RF) corridor along the railway tracks ensures a high signal-to-noise ratio and limits handovers. However, building such RF corridors is difficult because of the administrative burden of excessive formalities to obtain construction permissions and costly because of the sheer number of base stations. Our contribution in this paper is an unconventional solution of mmWave fronthauled low-power out-of-band repeater nodes deployed in short intervals on existing masts between high-power macro cell sites. The paper demonstrates the feasibility of the concept with an extensive measurement campaign on a commercial railway line. The benefit of using many low-power nodes with low-gain antennas compared to a baseline with only high-gain macro antennas is discussed, and the coverage improvement is evaluated. Based on the measurement results, a simple path loss model is calibrated. This model allows evaluation of the potential of the mmWave repeater architecture to increase the macro cell inter-site distance and reduce deployment costs.","PeriodicalId":270618,"journal":{"name":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024665","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}
{"title":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","authors":"","doi":"10.1145/3472771","DOIUrl":"https://doi.org/10.1145/3472771","url":null,"abstract":"","PeriodicalId":270618,"journal":{"name":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115312462","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}
Alexander Rabitsch, Georgios Xilouris, Themis Anagnostopoulos, Karl-Johan Grinnemo, Thanos Sarlas, A. Brunström, Özgü Alay, G. Caso
The network slicing concept of 5G aims to provide the flexibility and scalability required to support a wide array of vertical services. To coordinate the coexistence of network slices, and to guarantee that the required resources are available for each one of them, the 5G core employs a slicing management entity, a slice manager. In this paper, we propose an architecture where the network slicing concept is extended beyond the core and access networks to also include the configuration of the UE's network stack. We exploit the slice manager's global view on the network to feed fine-grained information on slice configuration, health, and status to the UE. This information, together with local policies on the UE, is then used to dynamically create services tailored to the requirements of individual applications. We implement this architecture in a 5G testbed, and show how it can be leveraged in order to enable optimized services through dynamic network protocol configuration, application-to-slice mapping, and network protocol selection.
{"title":"Extending network slice management to the end-host","authors":"Alexander Rabitsch, Georgios Xilouris, Themis Anagnostopoulos, Karl-Johan Grinnemo, Thanos Sarlas, A. Brunström, Özgü Alay, G. Caso","doi":"10.1145/3472771.3472775","DOIUrl":"https://doi.org/10.1145/3472771.3472775","url":null,"abstract":"The network slicing concept of 5G aims to provide the flexibility and scalability required to support a wide array of vertical services. To coordinate the coexistence of network slices, and to guarantee that the required resources are available for each one of them, the 5G core employs a slicing management entity, a slice manager. In this paper, we propose an architecture where the network slicing concept is extended beyond the core and access networks to also include the configuration of the UE's network stack. We exploit the slice manager's global view on the network to feed fine-grained information on slice configuration, health, and status to the UE. This information, together with local policies on the UE, is then used to dynamically create services tailored to the requirements of individual applications. We implement this architecture in a 5G testbed, and show how it can be leveraged in order to enable optimized services through dynamic network protocol configuration, application-to-slice mapping, and network protocol selection.","PeriodicalId":270618,"journal":{"name":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130600248","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}
G. Xilouris, M. Christopoulou, H. Koumaras, M. Kourtis, M. Emmelmann, D. Triantafyllopoulou, Yogaratnam Rahulan, Iván González Muriel, A. Díaz, E. Atxutegi, G. Gardikis, D. Lioprasitis, D. Tsolkas, Panagiotis Kostakis, Erik Aumayr, A. Bosneag, Özgü Alay, V. Frascolla, A. Brunström
The adoption of 5G has recently picked up pace and across the globe commercial deployments are more and more numerous. In addition to better performance, the 5G technology brings, among others, management and operation flexibility, thus allowing industry verticals to exploit features of the telecommunication networks that go far beyond the new mobile access capabilities. As with any new technology, the integration, testing and validation of new vertical applications pose great challenges at both management and operational levels. In this context, there is an evident need for 5G infrastructure facilities that offer testing and validation capabilities through a flexible experimentation framework. This paper presents the 5GENESIS EU-funded research project Experimentation Facility and the results of the validation campaigns conducted in its five experimentation platforms. The tests and obtained results were facilitated by the 5GENESIS Suite, an open-source framework providing test automation and result analytics.
{"title":"Experimentation and 5G KPI measurements in the 5GENESIS platforms","authors":"G. Xilouris, M. Christopoulou, H. Koumaras, M. Kourtis, M. Emmelmann, D. Triantafyllopoulou, Yogaratnam Rahulan, Iván González Muriel, A. Díaz, E. Atxutegi, G. Gardikis, D. Lioprasitis, D. Tsolkas, Panagiotis Kostakis, Erik Aumayr, A. Bosneag, Özgü Alay, V. Frascolla, A. Brunström","doi":"10.1145/3472771.3472776","DOIUrl":"https://doi.org/10.1145/3472771.3472776","url":null,"abstract":"The adoption of 5G has recently picked up pace and across the globe commercial deployments are more and more numerous. In addition to better performance, the 5G technology brings, among others, management and operation flexibility, thus allowing industry verticals to exploit features of the telecommunication networks that go far beyond the new mobile access capabilities. As with any new technology, the integration, testing and validation of new vertical applications pose great challenges at both management and operational levels. In this context, there is an evident need for 5G infrastructure facilities that offer testing and validation capabilities through a flexible experimentation framework. This paper presents the 5GENESIS EU-funded research project Experimentation Facility and the results of the validation campaigns conducted in its five experimentation platforms. The tests and obtained results were facilitated by the 5GENESIS Suite, an open-source framework providing test automation and result analytics.","PeriodicalId":270618,"journal":{"name":"Proceedings of the 1st Workshop on 5G Measurements, Modeling, and Use Cases","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115335962","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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