Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning
In this paper, we present the Learning Greedy Link Scheduling (LGLS) algorithm, a novel approach for optimizing link scheduling in wireless networks. By integrating deep learning and fuzzy logic, LGLS predicts link quality probabilities, which provide critical topological information to dynamically...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10729871/ |
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| author | Maryam Abbasalizadeh Krishnaa Vellamchety Pranathi Rayavaram Sashank Narain |
| author_facet | Maryam Abbasalizadeh Krishnaa Vellamchety Pranathi Rayavaram Sashank Narain |
| author_sort | Maryam Abbasalizadeh |
| collection | DOAJ |
| description | In this paper, we present the Learning Greedy Link Scheduling (LGLS) algorithm, a novel approach for optimizing link scheduling in wireless networks. By integrating deep learning and fuzzy logic, LGLS predicts link quality probabilities, which provide critical topological information to dynamically manage wireless network interference. This approach enhances resource allocation efficiency, leading to better bandwidth and spectrum usage. Our comprehensive evaluation shows that LGLS outperforms traditional algorithms such as Local Greedy Scheduling (LGS), achieving link scheduling performance improvements ranging from 9.60% to 24.79% and activating up to 24.10% more links. These results demonstrate LGLS’s robustness and efficiency in diverse network conditions, making it a promising solution for future wireless network optimization. |
| format | Article |
| id | doaj-art-9ba6bc2abdc84fb1b8a41c95f4281799 |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-9ba6bc2abdc84fb1b8a41c95f42817992024-11-12T00:02:16ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-0156832684810.1109/OJCOMS.2024.348494810729871Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep LearningMaryam Abbasalizadeh0https://orcid.org/0009-0004-6590-1683Krishnaa Vellamchety1https://orcid.org/0009-0007-7419-8426Pranathi Rayavaram2https://orcid.org/0000-0001-8375-7823Sashank Narain3https://orcid.org/0000-0001-5377-3750University of Massachusetts Lowell, Lowell, MA, USAUniversity of Massachusetts Lowell, Lowell, MA, USAUniversity of Massachusetts Lowell, Lowell, MA, USAUniversity of Massachusetts Lowell, Lowell, MA, USAIn this paper, we present the Learning Greedy Link Scheduling (LGLS) algorithm, a novel approach for optimizing link scheduling in wireless networks. By integrating deep learning and fuzzy logic, LGLS predicts link quality probabilities, which provide critical topological information to dynamically manage wireless network interference. This approach enhances resource allocation efficiency, leading to better bandwidth and spectrum usage. Our comprehensive evaluation shows that LGLS outperforms traditional algorithms such as Local Greedy Scheduling (LGS), achieving link scheduling performance improvements ranging from 9.60% to 24.79% and activating up to 24.10% more links. These results demonstrate LGLS’s robustness and efficiency in diverse network conditions, making it a promising solution for future wireless network optimization.https://ieeexplore.ieee.org/document/10729871/Link schedulingfuzzy logicdeep learning optimizationlink quality probability |
| spellingShingle | Maryam Abbasalizadeh Krishnaa Vellamchety Pranathi Rayavaram Sashank Narain Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning IEEE Open Journal of the Communications Society Link scheduling fuzzy logic deep learning optimization link quality probability |
| title | Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning |
| title_full | Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning |
| title_fullStr | Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning |
| title_full_unstemmed | Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning |
| title_short | Dynamic Link Scheduling in Wireless Networks Through Fuzzy-Enhanced Deep Learning |
| title_sort | dynamic link scheduling in wireless networks through fuzzy enhanced deep learning |
| topic | Link scheduling fuzzy logic deep learning optimization link quality probability |
| url | https://ieeexplore.ieee.org/document/10729871/ |
| work_keys_str_mv | AT maryamabbasalizadeh dynamiclinkschedulinginwirelessnetworksthroughfuzzyenhanceddeeplearning AT krishnaavellamchety dynamiclinkschedulinginwirelessnetworksthroughfuzzyenhanceddeeplearning AT pranathirayavaram dynamiclinkschedulinginwirelessnetworksthroughfuzzyenhanceddeeplearning AT sashanknarain dynamiclinkschedulinginwirelessnetworksthroughfuzzyenhanceddeeplearning |