Reinforcement learning for communication load balancing: approaches and challenges
The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service fo...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2023.1156064/full |
| _version_ | 1797815147628068864 |
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| author | Di Wu Jimmy Li Amal Ferini Yi Tian Xu Michael Jenkin Seowoo Jang Xue Liu Gregory Dudek |
| author_facet | Di Wu Jimmy Li Amal Ferini Yi Tian Xu Michael Jenkin Seowoo Jang Xue Liu Gregory Dudek |
| author_sort | Di Wu |
| collection | DOAJ |
| description | The amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing. |
| first_indexed | 2024-03-13T08:18:23Z |
| format | Article |
| id | doaj.art-1e6bb9bf9ad94a6892a43defabd7946b |
| institution | Directory Open Access Journal |
| issn | 2624-9898 |
| language | English |
| last_indexed | 2024-03-13T08:18:23Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj.art-1e6bb9bf9ad94a6892a43defabd7946b2023-05-31T12:05:38ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982023-05-01510.3389/fcomp.2023.11560641156064Reinforcement learning for communication load balancing: approaches and challengesDi Wu0Jimmy Li1Amal Ferini2Yi Tian Xu3Michael Jenkin4Seowoo Jang5Xue Liu6Gregory Dudek7Samsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaSamsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaSamsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaSamsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaSamsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaSamsung Electronics, Seoul, Republic of KoreaSamsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaSamsung Artificial Intelligence (AI) Center, Montreal, QC, CanadaThe amount of cellular communication network traffic has increased dramatically in recent years, and this increase has led to a demand for enhanced network performance. Communication load balancing aims to balance the load across available network resources and thus improve the quality of service for network users. Most existing load balancing algorithms are manually designed and tuned rule-based methods where near-optimality is almost impossible to achieve. Furthermore, rule-based methods are difficult to adapt to quickly changing traffic patterns in real-world environments. Reinforcement learning (RL) algorithms, especially deep reinforcement learning algorithms, have achieved impressive successes in many application domains and offer the potential of good adaptabiity to dynamic changes in network load patterns. This survey presents a systematic overview of RL-based communication load-balancing methods and discusses related challenges and opportunities. We first provide an introduction to the load balancing problem and to RL from fundamental concepts to advanced models. Then, we review RL approaches that address emerging communication load balancing issues important to next generation networks, including 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions for applying RL for communication load balancing.https://www.frontiersin.org/articles/10.3389/fcomp.2023.1156064/fullreinforcement learningwireless communication load balancing5G networks and beyondWiFi network load balancingreal-world challenges |
| spellingShingle | Di Wu Jimmy Li Amal Ferini Yi Tian Xu Michael Jenkin Seowoo Jang Xue Liu Gregory Dudek Reinforcement learning for communication load balancing: approaches and challenges Frontiers in Computer Science reinforcement learning wireless communication load balancing 5G networks and beyond WiFi network load balancing real-world challenges |
| title | Reinforcement learning for communication load balancing: approaches and challenges |
| title_full | Reinforcement learning for communication load balancing: approaches and challenges |
| title_fullStr | Reinforcement learning for communication load balancing: approaches and challenges |
| title_full_unstemmed | Reinforcement learning for communication load balancing: approaches and challenges |
| title_short | Reinforcement learning for communication load balancing: approaches and challenges |
| title_sort | reinforcement learning for communication load balancing approaches and challenges |
| topic | reinforcement learning wireless communication load balancing 5G networks and beyond WiFi network load balancing real-world challenges |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2023.1156064/full |
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