Spectrum access options for vertical network service providers in 5G

Vuojala, Heli; Mustonen, Miia; Chen, Xianfu; Kujanpää, Kaisa; Ruuska, Pekka; Höyhtyä, Marko; Matinmikko-Blue, Marja; Kalliovaara, Juha; Talmola, Pekka; Nyström, Anna-Greta

doi:10.1016/j.telpol.2019.101903

Cited by 24 publications

(12 citation statements)

References 38 publications

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“…e number of nodes in the hidden layer is not only related to the number of nodes in the input and output layers but also to the complexity of the problem to be solved, the type of transfer function, and the characteristics of the sample data. e condition of determining the number of hidden layer nodes is that the number of nodes in and out layer and hidden layer must be less than n-1 (where n is the number of training samples) [31]. e input and output of all kinds of training samples in this study are all 5 and 1, so the number of nodes in the hidden layer is determined to be 3.…”

Section: Information Forward Propagation Processmentioning

confidence: 98%

Research on Sports Performance Prediction Based on BP Neural Network

Yang

Luo

Tan

2021

Mobile Information Systems

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Artificial neural network has the advantages of self-training and fault tolerance, while BP neural network has simple learning algorithms and powerful learning capabilities. The BP neural network algorithm has been widely used in practice. This paper conducts research on sports performance prediction based on 5G and artificial neural network algorithms. This paper uses the BP neural network algorithm as a pretest modelling method to predict the results of the 30th Olympic Men’s 100m Track and Field Championships and is supported by the MATLAB neural network toolbox. According to the experimental results, the scheme proposed in this paper has better performance than the other prediction strategies. In order to explore the feasibility and application of the BP neural network in this kind of prediction, there is a lot of work to be done. The model has a high prediction accuracy and provides a new method for the prediction of sports performance. The results show that the BP neural network algorithm can be used to predict sports performance, with high prediction accuracy and strong generalization ability.

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Section: Information Forward Propagation Processmentioning

confidence: 98%

Research on Sports Performance Prediction Based on BP Neural Network

Yang

Luo

Tan

2021

Mobile Information Systems

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“…Private mobile networks using LTE technology have recently gained interest in many business applications. Main benefits of a private network are guaranteed service bandwidth, speed, security, and reliability [1].…”

Section: Introductionmentioning

confidence: 99%

Measurements to Study the Coexistence of Private LTE TDD Networks in 2.3 GHz Band

Ekman

Talmola

Kalliovaara

et al. 2021

2021 29th Conference of Open Innovations Association (FRUCT)

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Private wireless networks have recently gained interest in many business applications. Use of 4G and 5G technologies for private network deployments has become feasible recently. Finnish Transport and Communications Agency Traficom began to grant licenses for private mobile Time Division Duplex (TDD) networks on frequency band 2300-2320 MHz in 2020. In this study, the coexistence of two 4G TDD networks is investigated through an extensive measurement campaign. The laboratory test setup developed to conduct the interference measurement campaign can be used to study 4G and 5G TDD technologies in frequency bands below 4 GHz. Networks using TDD technology are recommended to use synchronized frame configurations to avoid interference. Private network applications and requirements differ between user organisations and hence the uplink and downlink data rate requirements are different and may need different TDD frame configurations. This paper studies interference between adjacent private networks, which have same or different TDD frame configurations. Measurement results indicate that non-synchronized networks produce interference and significant decrease in data throughput in neighboring networks.

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“…The vertical sectors being targeted include energy [20], transport and logistics [21], [22], healthcare [23], [24], live events [25], manufacturing [26], [27], agriculture [28], construction [29], and tourism [30]. Yet, deployment of these new wireless 5G services require bespoke forms of software, hardware and spectrum to meet the connectivity requirements of every vertical sector business model [31]- [33].…”

Section: The 5g Context Use Cases and Key Technologiesmentioning

confidence: 99%