CULuT: Improving channel utilization with Look-up Table in WLANs
Channel efficiency in wireless local area networks (WLAN) is crucial for performance. One of the most important parameters that increase channel efficiency is channel utilization (CU). Channel Utilization has an important function to increase the Quality of Service (QoS) in the medium access control...
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| Format: | Article |
| Language: | English |
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Gazi University
2020-09-01
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| Series: | Gazi Üniversitesi Fen Bilimleri Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1018058 |
| _version_ | 1797920092077424640 |
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| author | Hacı Bayram KARAKURT Cemal KOÇAK |
| author_facet | Hacı Bayram KARAKURT Cemal KOÇAK |
| author_sort | Hacı Bayram KARAKURT |
| collection | DOAJ |
| description | Channel efficiency in wireless local area networks (WLAN) is crucial for performance. One of the most important parameters that increase channel efficiency is channel utilization (CU). Channel Utilization has an important function to increase the Quality of Service (QoS) in the medium access control (MAC) layer of wireless local area networks (WLAN). There are many input parameters to measure the channel utilization in WLANs. Most popular input parameters, Request to Send Threshold (RTS Threshold-RTSTV), Fragmentation Threshold (FTV) and Buffer Size (BS) controls the usage and movement of RTS and CTS virtual packets used in CSMA/CA (Carrier-sense Multiple Access with Collision Avoidance) protocol to increase Channel Utilization. In previous studies, these parameters were tested on OPNET Modeler and datasets were obtained. Performance is increased in the output parameters by applying reinforcement learning over the obtained datasets. In this study, RTSTV, FTV and BS values were updated using the brute force algorithm in with the Look-up Table on the code block structure of the OPNET Modeler and the Channel Utilization increased at the time of simulation. With this new agent model structure, the network layer, node layer and process layer have been updated. With this simulation study, CU and performance has been increased with reinforcement learning from %10 to 15% -18%. |
| first_indexed | 2024-04-10T13:55:49Z |
| format | Article |
| id | doaj.art-bf8c3dec943f4045b457b45e3702471e |
| institution | Directory Open Access Journal |
| issn | 2147-9526 |
| language | English |
| last_indexed | 2024-04-10T13:55:49Z |
| publishDate | 2020-09-01 |
| publisher | Gazi University |
| record_format | Article |
| series | Gazi Üniversitesi Fen Bilimleri Dergisi |
| spelling | doaj.art-bf8c3dec943f4045b457b45e3702471e2023-02-15T16:10:29ZengGazi UniversityGazi Üniversitesi Fen Bilimleri Dergisi2147-95262020-09-018354256010.29109/gujsc.707916CULuT: Improving channel utilization with Look-up Table in WLANsHacı Bayram KARAKURThttps://orcid.org/0000-0003-1591-4502Cemal KOÇAKhttps://orcid.org/0000-0002-8902-0934Channel efficiency in wireless local area networks (WLAN) is crucial for performance. One of the most important parameters that increase channel efficiency is channel utilization (CU). Channel Utilization has an important function to increase the Quality of Service (QoS) in the medium access control (MAC) layer of wireless local area networks (WLAN). There are many input parameters to measure the channel utilization in WLANs. Most popular input parameters, Request to Send Threshold (RTS Threshold-RTSTV), Fragmentation Threshold (FTV) and Buffer Size (BS) controls the usage and movement of RTS and CTS virtual packets used in CSMA/CA (Carrier-sense Multiple Access with Collision Avoidance) protocol to increase Channel Utilization. In previous studies, these parameters were tested on OPNET Modeler and datasets were obtained. Performance is increased in the output parameters by applying reinforcement learning over the obtained datasets. In this study, RTSTV, FTV and BS values were updated using the brute force algorithm in with the Look-up Table on the code block structure of the OPNET Modeler and the Channel Utilization increased at the time of simulation. With this new agent model structure, the network layer, node layer and process layer have been updated. With this simulation study, CU and performance has been increased with reinforcement learning from %10 to 15% -18%.https://dergipark.org.tr/tr/download/article-file/1018058wlanlook-up tablechannel utilizationfragmentation threshold |
| spellingShingle | Hacı Bayram KARAKURT Cemal KOÇAK CULuT: Improving channel utilization with Look-up Table in WLANs Gazi Üniversitesi Fen Bilimleri Dergisi wlan look-up table channel utilization fragmentation threshold |
| title | CULuT: Improving channel utilization with Look-up Table in WLANs |
| title_full | CULuT: Improving channel utilization with Look-up Table in WLANs |
| title_fullStr | CULuT: Improving channel utilization with Look-up Table in WLANs |
| title_full_unstemmed | CULuT: Improving channel utilization with Look-up Table in WLANs |
| title_short | CULuT: Improving channel utilization with Look-up Table in WLANs |
| title_sort | culut improving channel utilization with look up table in wlans |
| topic | wlan look-up table channel utilization fragmentation threshold |
| url | https://dergipark.org.tr/tr/download/article-file/1018058 |
| work_keys_str_mv | AT hacıbayramkarakurt culutimprovingchannelutilizationwithlookuptableinwlans AT cemalkocak culutimprovingchannelutilizationwithlookuptableinwlans |