Reinforcement Learning-Based Television White Space Database
Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of im...
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University of Baghdad, College of Science for Women
2021-06-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6215 |
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| author | Armie E. Pakzad Raine Mattheus Manuel Jerrick Spencer Uy Xavier Francis Asuncion Joshua Vincent Ligayo Lawrence Materum |
| author_facet | Armie E. Pakzad Raine Mattheus Manuel Jerrick Spencer Uy Xavier Francis Asuncion Joshua Vincent Ligayo Lawrence Materum |
| author_sort | Armie E. Pakzad |
| collection | DOAJ |
| description | Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs. |
| format | Article |
| id | doaj-art-3c042bd8417b4190a7eff08fc68b9461 |
| institution | DOAJ |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-3c042bd8417b4190a7eff08fc68b94612025-08-20T02:52:58ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862021-06-01182(Suppl.)10.21123/bsj.2021.18.2(Suppl.).0947Reinforcement Learning-Based Television White Space DatabaseArmie E. Pakzad0Raine Mattheus Manuel1Jerrick Spencer Uy2Xavier Francis Asuncion3Joshua Vincent Ligayo4Lawrence Materum5De La Selle University, Philippines.De La Selle University, Philippines.De La Selle University, Philippines.1 De La Selle University, Philippines.De La Selle University, Philippines.Tokyo City University, Japan. Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6215radio propagationradio spectrum managementreinforcement learningtelevision white space database |
| spellingShingle | Armie E. Pakzad Raine Mattheus Manuel Jerrick Spencer Uy Xavier Francis Asuncion Joshua Vincent Ligayo Lawrence Materum Reinforcement Learning-Based Television White Space Database مجلة بغداد للعلوم radio propagation radio spectrum management reinforcement learning television white space database |
| title | Reinforcement Learning-Based Television White Space Database |
| title_full | Reinforcement Learning-Based Television White Space Database |
| title_fullStr | Reinforcement Learning-Based Television White Space Database |
| title_full_unstemmed | Reinforcement Learning-Based Television White Space Database |
| title_short | Reinforcement Learning-Based Television White Space Database |
| title_sort | reinforcement learning based television white space database |
| topic | radio propagation radio spectrum management reinforcement learning television white space database |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/6215 |
| work_keys_str_mv | AT armieepakzad reinforcementlearningbasedtelevisionwhitespacedatabase AT rainemattheusmanuel reinforcementlearningbasedtelevisionwhitespacedatabase AT jerrickspenceruy reinforcementlearningbasedtelevisionwhitespacedatabase AT xavierfrancisasuncion reinforcementlearningbasedtelevisionwhitespacedatabase AT joshuavincentligayo reinforcementlearningbasedtelevisionwhitespacedatabase AT lawrencematerum reinforcementlearningbasedtelevisionwhitespacedatabase |