Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis

Abdullah, Ismail Bin; Daw, Daw Abdulsalam Ali; Seman, Kamaruzzaman

doi:10.4236/jamp.2015.31009

JAMP

2015

DOI: 10.4236/jamp.2015.31009

|View full text |Cite

Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis

Ismail Bin Abdullah¹,

Daw Abdulsalam Ali Daw²,

Kamaruzzaman Seman³

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e.g. to predict stock prices [3] in economics; to predict air quality [14] in environmental science; to predict power generation [11] in industry; to predict traffic flow problem [1] in transport, etc. In this paper, it was applied to predict the packet disordering in network, which opened a new application direction of the fuzzy time series.…”

mentioning

confidence: 99%

An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering

Li¹,

Zhang²

2020

NACO

View full text Add to dashboard Cite

Fuzzy time series shows great advantages in dealing with incomplete or unreasonable data. But most of them are based on fuzzy AR time series model, so it is necessary to add MA variables to the fuzzy time series [10] to make it more accurate. An improved ARMA(1,1) type fuzzy time series based on fuzzy logic group relations including fuzzy MA variables along with fuzzy AR variables was proposed in this paper. To take full account of the errors, the prediction errors were added to the forecast fuzzy sets, and it made the first-order fuzzy logical relationship sets more exact. In order to verify the advantage of the proposed method, it was applied to predict the stock prices of State Bank of India (SBI) and the packet disordering from a common source host in the Northeast University to www. yahoo. com. The experimental results showed that the proposed model was more precise than other models. 2020 Mathematics Subject Classification. 62A86. Key words and phrases. Fuzzy time series; ARMA(1,1) type fuzzy time series; packet disordering.

show abstract

mentioning

confidence: 99%

An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering

Li¹,

Zhang²

2020

NACO

View full text Add to dashboard Cite

show abstract

Wireless backhaul network’s capacity optimization using time series forecasting approach

Mahmood

Kiah

Azzuhri

et al. 2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

et al. 2020

View full text Add to dashboard Cite

Telecom operators are aiming to provide high-grade data, multimedia applications and low latency videos for smart devices. As today's mobile data is experiencing rapid growth and the usage of smart devices are fabricating unparalleled challenges for telecom operators to meet the global bandwidth requirement. From the first generation to fourth generation, the technology evolution is predominantly governed by the hardware side but now it is moving towards the concept of cognitive network management, resource orchestration and machine learning-based solutions. In this paper, we propose the adaptive capacity and frequency optimization (ACFO) method for adaptive optimization based on time series forecasting approach. The daily capacity utilization of microwave (MW) links is analyzed to use forecasted demand. Based on the projected demand, the capacity and frequency optimization will be executed. The two main forecasting models 1) SARIMA and 2) MLP are used and for performance evaluation, we used RMSE and MAPE criterion. The analytic outcomes show that MLP with two layers and six hidden nodes (6/6) are good enough to achieve the desired results. In some cases, we need to exceed the hidden nodes up to fifteen (15/15). By using the forecasting approach, the reactive optimization will successfully shift to the predicted/ proactive optimization, will balance the resource distribution and can condense the wastage of resources. The outcome of the study will be a contribution to the dynamic resource optimization in wireless backhaul network. INDEX TERMS Time series forecasting, capacity planning, dynamic resource optimization, wireless backhaul optimization.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis

Cited by 3 publications

References 14 publications

An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering

An improved ARMA(1, 1) type fuzzy time series applied in predicting disordering

Wireless backhaul network’s capacity optimization using time series forecasting approach

Capacity and Frequency Optimization of Wireless Backhaul Network Using Traffic Forecasting

Contact Info

Product

Resources

About