Robust available bandwidth estimation against dynamic behavior of packet scheduler in operational LTE networks

Oshiba, Takashi; Nogami, Kousuke; Nihei, Keiji; Satoda, Kozo

doi:10.1109/iscc.2016.7543912

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…In [103], the authors developed a new scheduling technique to increase the probability of assigning the available resource blocks (RBs) to the cell-edge users so that their achieved throughput would increase. NOKIA in [104] developed a smart scheduler to mitigate cell-edge interference by employing the method of Frequency Selective Scheduling (FSS). FSS uses Channel Aware Scheduling (CAS) and Interference Aware Scheduling (IAS) to select non-faded RBs for each user.…”

Section: Cell Edge Interference Challenges and Solutions In 5g And Lp...mentioning

confidence: 99%

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security

Ogbodo

Abu‐Mahfouz

Kurien

2022

Sensors

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Addressing the recent trend of the massive demand for resources and ubiquitous use for all citizens has led to the conceptualization of technologies such as the Internet of Things (IoT) and smart cities. Ubiquitous IoT connectivity can be achieved to serve both urban and underserved remote areas such as rural communities by deploying 5G mobile networks with Low Power Wide Area Network (LPWAN). The current architectures will not offer flexible connectivity to many IoT applications due to high service demand, data exchange, emerging technologies, and security challenges. Hence, this paper explores various architectures that consider a hybrid 5G-LPWAN-IoT and Smart Cities. This includes security challenges as well as endogenous security and solutions in 5G and LPWAN-IoT. The slicing of virtual networks using software-defined network (SDN)/network function virtualization (NFV) based on the different quality of service (QoS) to satisfy different services and quality of experience (QoE) is presented. Also, a strategy that considers the implementation of 5G jointly with Weightless-N (TVWS) technologies to reduce the cell edge interference is considered. Discussions on the need for ubiquity connectivity leveraging 5G and LPWAN-IoT are presented. In addition, future research directions are presented, including a unified 5G network and LPWAN-IoT architecture that will holistically support integration with emerging technologies and endogenous security for improved/secured smart cities and remote areas IoT applications. Finally, the use of LPWAN jointly with low earth orbit (LEO) satellites for ubiquitous IoT connectivity is advocated in this paper.

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Section: Cell Edge Interference Challenges and Solutions In 5g And Lp...mentioning

confidence: 99%

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security

Ogbodo

Abu‐Mahfouz

Kurien

2022

Sensors

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“…Interface selection for data transmission traditionally uses data rate as metric and is usually estimated by sending a packet sequence through every interface [1]- [4]. Although this approach works universally, it has two major disadvantages: Firstly, it introduces further congestion to the mobile network with redundant packets.…”

Section: Introductionmentioning

confidence: 99%

ASR - Adaptive Similarity-Based Regressor for Uplink Data Rate Estimation in Mobile Networks

Nikolov

Kühn

McGibney

et al. 2020

IEEE J. Select. Areas Commun.

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This paper presents a passive data rate estimation method that leverages commonly available parameters of commercial modems with application in Intelligent Transportation Systems. The estimation is performed by utilizing an Adaptive Similarity-based Regression (ASR) approach. This constitutes the use of Support-Vector Regression (SVR) in conjunction with a similarity-based unlearning algorithm. It is demonstrated that this approach can adapt to the various properties of different mobile networks, while maintaining a fixed training set size. This is particularly useful in cases where training data is not available in large quantities, or the uplink rate is limited by the users subscription. ASR is developed as a set of modular components and as such can be used as an enhancement to protocols such as multipath Transmission Control Protocol (TCP), Software-Defined Networking (SDN), or as an aid to Quality-of-Service (QoS) routing. It is shown that the algorithm can achieve satisfactory performance with as little as 24 training samples, and can be deployed across different mobile networks without the need of pre-training. The solution is validated using a custom test-bed to perform mobile network measurements, gathering over 15, 000 measurement samples. In addition, the algorithm is tested using measurements collected under real life conditions both in a moving car and train. As there is a shortage of open source data in the field of rate estimation in mobile networks, we publish all of the data sets used in this paper to encourage further research on the subject.

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AI-Enhanced Connection Management for Cellular Networks

Wenning

2024

Studies in Computational Intelligence

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Cellular networks such as 4G, 5G and beyond are essential components of current and future communication infrastructure. Current research topics such as smart mobility, intelligent transportation systems and autonomous driving heavily depend on the availability of ubiquitous connectivity. In addition, consumer demand for cellular network resources is ever growing. In the context of the InSecTT project, cellular connectivity contributes, e.g., to use cases in the rail domain, where remote monitoring, maintenance and diagnostics rely on the presence of cellular uplinks from the rolling stock to cloud services. As the rolling stock moves, these uplinks are subject to continuously changing conditions that affect the availability and quality of the connection. To counteract this and to increase resilience, onboard systems may be equipped with a number of uplink options that can be used. This chapter discusses the use of AI to assess and manage the available connections and to move data traffic between the available links according to their expected data rates.

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Robust available bandwidth estimation against dynamic behavior of packet scheduler in operational LTE networks

Cited by 11 publications

References 20 publications

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security

A Survey on 5G and LPWAN-IoT for Improved Smart Cities and Remote Area Applications: From the Aspect of Architecture and Security

ASR - Adaptive Similarity-Based Regressor for Uplink Data Rate Estimation in Mobile Networks

AI-Enhanced Connection Management for Cellular Networks

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