WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques
The ability to predict the traffic of a particular WiMAX network is crucial in analyzing its performance. It bears various applications in reality, such as enabling better network management and admission. Furthermore, traffic forecasting plays a vital role in ensuring that the quality of service...
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my-usim-ddms-13331 |
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uketd_dc |
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Universiti Sains Islam Malaysia |
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USIM Institutional Repository |
| language |
English |
| topic |
LibyaMax (LibyaInteroperability for Microwave Access) WiMAX (Worldwide Interoperability for Microwave Access) Traffic forecasting system Artificial Intelligence |
| spellingShingle |
LibyaMax (LibyaInteroperability for Microwave Access) WiMAX (Worldwide Interoperability for Microwave Access) Traffic forecasting system Artificial Intelligence Daw Abdulsalam Ali Daw WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques |
| description |
The ability to predict the traffic of a particular WiMAX network is crucial in analyzing its
performance. It bears various applications in reality, such as enabling better network
management and admission. Furthermore, traffic forecasting plays a vital role in ensuring that
the quality of service is maintained at the necessary level. Therefore, in this research, a new
model for WiMAX traffic forecasting system for predicting traffic time series based on the
traffic data recorded (TRD) using Artificial Neural Network (ANN), K-Nearest Neighbor
(KNN) and Fuzzy Time Series (FTS) was proposed. The data used in this work are available
from LibyaMax network (WiMAX technology) automated by Libya Telecom and Technology
(LTT) over a period of 180 days which consist of maximum online user, minimum online
user, traffic of MIMO-A and traffic of MIMO-B. The quality of forecasting WiMAX traffic
was obtained by focusing on the Artificial Intelligence (AI) design through comparison of
different configurations and models that consist of different topologies and learning
algorithms. The decision of changing the Artificial Intelligence (Al) architecture is essentially
based on the objective to obtain the best Al model for a flow traffic prediction model.
Different configurations were tested using real traffic data recorded at base stations (A, B and
AB) that belong to a Libyan WiMAX network. Statistical measurement was used to evaluate
different AI configurations to select the best model based on higher performance result. The
outcome of the study indicates that KNN model using maximum and minimum online user as
inputs give good and accurate mean square error results (MSE) in predicting traffic as a
whole. |
| format |
Thesis |
| author |
Daw Abdulsalam Ali Daw |
| author_facet |
Daw Abdulsalam Ali Daw |
| author_sort |
Daw Abdulsalam Ali Daw |
| title |
WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques |
| title_short |
WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques |
| title_full |
WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques |
| title_fullStr |
WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques |
| title_full_unstemmed |
WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques |
| title_sort |
wimax traffic forecasting based on artificial intelligence techniques |
| granting_institution |
Universiti Sains Islam Malaysia |
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my-usim-ddms-133312024-05-29T19:20:37Z WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques Daw Abdulsalam Ali Daw The ability to predict the traffic of a particular WiMAX network is crucial in analyzing its performance. It bears various applications in reality, such as enabling better network management and admission. Furthermore, traffic forecasting plays a vital role in ensuring that the quality of service is maintained at the necessary level. Therefore, in this research, a new model for WiMAX traffic forecasting system for predicting traffic time series based on the traffic data recorded (TRD) using Artificial Neural Network (ANN), K-Nearest Neighbor (KNN) and Fuzzy Time Series (FTS) was proposed. The data used in this work are available from LibyaMax network (WiMAX technology) automated by Libya Telecom and Technology (LTT) over a period of 180 days which consist of maximum online user, minimum online user, traffic of MIMO-A and traffic of MIMO-B. The quality of forecasting WiMAX traffic was obtained by focusing on the Artificial Intelligence (AI) design through comparison of different configurations and models that consist of different topologies and learning algorithms. The decision of changing the Artificial Intelligence (Al) architecture is essentially based on the objective to obtain the best Al model for a flow traffic prediction model. Different configurations were tested using real traffic data recorded at base stations (A, B and AB) that belong to a Libyan WiMAX network. Statistical measurement was used to evaluate different AI configurations to select the best model based on higher performance result. The outcome of the study indicates that KNN model using maximum and minimum online user as inputs give good and accurate mean square error results (MSE) in predicting traffic as a whole. Universiti Sains Islam Malaysia 2016-03 Thesis en https://oarep.usim.edu.my/handle/123456789/13331 https://oarep.usim.edu.my/bitstreams/d5d69584-2530-4359-a702-ac89f9580590/download 8a4605be74aa9ea9d79846c1fba20a33 https://oarep.usim.edu.my/bitstreams/f08d973b-beb0-4265-9623-c9d4b6e5246f/download 04ee85e63dd680079ddb9c5ddd3a54ca https://oarep.usim.edu.my/bitstreams/b38c682f-dc48-4100-999e-b6d823696d56/download 8495c56ac92d9d51e856a82883008d05 https://oarep.usim.edu.my/bitstreams/4bed2f27-fd4d-42dd-a181-e151416cb622/download 656b72c2a9d3c85d32d8b755bb829116 https://oarep.usim.edu.my/bitstreams/9727cdf7-847c-4b5f-b89e-8bc3e0d176f2/download 38f1655e1dafc0dd4ae20b2f27bc3a63 https://oarep.usim.edu.my/bitstreams/cfe1acfd-7bec-498f-801b-3ebd2215a087/download d823b6a80777a787e3bd391ddef3a1f7 https://oarep.usim.edu.my/bitstreams/40c67a67-2d7b-4e84-8682-0ed51d865cc5/download be2b943710edc7e5154b89bf4c77549a https://oarep.usim.edu.my/bitstreams/92a353ae-dcd4-4322-aa40-44269b131020/download 5237c409947953c80929af294b14eb84 https://oarep.usim.edu.my/bitstreams/250fb469-7713-4319-8194-6d796127b869/download dd32b00fa55c67aef53717740e0d5de4 https://oarep.usim.edu.my/bitstreams/d485fda4-d1dc-41bf-aca1-3ed8506df987/download 75c1da074d2daa920fc0b30d1742624c https://oarep.usim.edu.my/bitstreams/4a1682e8-4c2d-4268-83fa-541f7e2afade/download a43196d3d127b8e7bb8f78733b310b13 https://oarep.usim.edu.my/bitstreams/6e020432-da2a-440c-9e82-9cca5b8dc8ae/download b9532a688275422ffe91b1dc03025265 https://oarep.usim.edu.my/bitstreams/9b6d0df7-527b-4c73-90c5-f0a4c89e7d96/download 032ada3933174e2edd773030f5e89364 https://oarep.usim.edu.my/bitstreams/1b626900-b266-4365-8742-eeaeb01bd5ff/download 1eef9e34d9bd8558efb08831539e232a https://oarep.usim.edu.my/bitstreams/9eb9e29d-d82e-4c94-b155-f647270112ae/download 8c00599884aa21dca6a3d9450413d9ab https://oarep.usim.edu.my/bitstreams/eecabd9c-9d65-4211-8763-a7ee309c3d42/download 4817292d8b1968e370533da698280c5b https://oarep.usim.edu.my/bitstreams/da4407ab-6736-4315-9f24-6ff54d0dae3f/download 58fed332910cde2dfab70217e5b192a4 https://oarep.usim.edu.my/bitstreams/a91b5596-d70d-4cdb-8508-4247e05afe72/download 1d8be94904a80208321a45bbcb985e54 https://oarep.usim.edu.my/bitstreams/e1e1893c-3cc8-4309-947c-f2d0b10c1f5f/download 6dcf4afc6c2e9a25421b35a154df3963 LibyaMax (LibyaInteroperability for Microwave Access) WiMAX (Worldwide Interoperability for Microwave Access) Traffic forecasting system Artificial Intelligence |