A 3-param network secletion algorithm in heterogeneous wireless networks

Li, Mo; Du, Yanliang; Ma, Xiaofeng; Huang, Shengbo

doi:10.1109/iccsn.2017.8230142

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2019

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(2 citation statements)

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“….) (1) Let ( ) be the traffic load function varying with time t, and let ( ) be the frequency counterpart of ( ) with the upper bound of max . Nyquist theorem regulates the idea that sampling rate must be at least twice of max to prevent aliasing [19].…”

Section: Iot Trafficmentioning

confidence: 99%

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Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Mao

Liu

et al. 2019

Wireless Communications and Mobile Computing

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Due to the proliferation of global monitoring sensors, the Internet of Things (IoT) is widely used to build smart cities and smart homes. 5G HetNets play an important role in the IoT video stream. This paper proposes an improved Call Session Control Function (CSCF) scheme. The improved CSCF server contains additional modules to facilitate IoT traffic prediction and resource reservation. We highlight traffic prediction in this work and develop a compressed sensing based linear predictor to catch the traffic patterns. Experimental results justify that our proposed scheme can forecast the traffic load with high accuracy but low sampling overhead.

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Section: Iot Trafficmentioning

confidence: 99%

“…In order to solve the problem of explosive data volume of the IoT, a heterogeneous networks (HetNets) technology is proposed. The problem of 5G communication is solved by deploying a large number of small cells [1][2][3]. There are several technical challenges hindering the IoT video streaming over 5G HetNets.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Mao

Liu

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

A 3-param network secletion algorithm in heterogeneous wireless networks

Cited by 1 publication

References 9 publications

Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

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