Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning
Open radio access network (O-RAN) is one of the promising candidates for fulfilling flexible and cost-effective goals by considering openness and intelligence in its architecture. In the O-RAN architecture, a central unit (O-CU) and a distributed unit (O-DU) are virtualized and executed on processin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/13/5029 |
| _version_ | 1797408182905077760 |
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| author | Shahram Mollahasani Turgay Pamuklu Rodney Wilson Melike Erol-Kantarci |
| author_facet | Shahram Mollahasani Turgay Pamuklu Rodney Wilson Melike Erol-Kantarci |
| author_sort | Shahram Mollahasani |
| collection | DOAJ |
| description | Open radio access network (O-RAN) is one of the promising candidates for fulfilling flexible and cost-effective goals by considering openness and intelligence in its architecture. In the O-RAN architecture, a central unit (O-CU) and a distributed unit (O-DU) are virtualized and executed on processing pools of general-purpose processors that can be placed at different locations. Therefore, it is challenging to choose a proper location for executing network functions (NFs) over these entities by considering propagation delay and computational capacity. In this paper, we propose a Soft Actor–Critic Energy-Aware Dynamic DU Selection algorithm (SA2C-EADDUS) by integrating two nested actor–critic agents in the O-RAN architecture. In addition, we formulate an optimization model that minimizes delay and energy consumption. Then, we solve that problem with an MILP solver and use that solution as a lower bound comparison for our SA2C-EADDUS algorithm. Moreover, we compare that algorithm with recent works, including RL- and DRL-based resource allocation algorithms and a heuristic method. We show that by collaborating A2C agents in different layers and by dynamic relocation of NFs, based on service requirements, our schemes improve the energy efficiency by 50% with respect to other schemes. Moreover, we reduce the mean delay by a significant amount with our novel SA2C-EADDUS approach. |
| first_indexed | 2024-03-09T03:54:37Z |
| format | Article |
| id | doaj.art-8b7ecfcc75a2476d909da07971aa8cea |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:54:37Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8b7ecfcc75a2476d909da07971aa8cea2023-12-03T14:22:46ZengMDPI AGSensors1424-82202022-07-012213502910.3390/s22135029Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic LearningShahram Mollahasani0Turgay Pamuklu1Rodney Wilson2Melike Erol-Kantarci3School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, CanadaCiena, Ottawa, ON K2K 0L1, CanadaSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, CanadaOpen radio access network (O-RAN) is one of the promising candidates for fulfilling flexible and cost-effective goals by considering openness and intelligence in its architecture. In the O-RAN architecture, a central unit (O-CU) and a distributed unit (O-DU) are virtualized and executed on processing pools of general-purpose processors that can be placed at different locations. Therefore, it is challenging to choose a proper location for executing network functions (NFs) over these entities by considering propagation delay and computational capacity. In this paper, we propose a Soft Actor–Critic Energy-Aware Dynamic DU Selection algorithm (SA2C-EADDUS) by integrating two nested actor–critic agents in the O-RAN architecture. In addition, we formulate an optimization model that minimizes delay and energy consumption. Then, we solve that problem with an MILP solver and use that solution as a lower bound comparison for our SA2C-EADDUS algorithm. Moreover, we compare that algorithm with recent works, including RL- and DRL-based resource allocation algorithms and a heuristic method. We show that by collaborating A2C agents in different layers and by dynamic relocation of NFs, based on service requirements, our schemes improve the energy efficiency by 50% with respect to other schemes. Moreover, we reduce the mean delay by a significant amount with our novel SA2C-EADDUS approach.https://www.mdpi.com/1424-8220/22/13/5029actor–critic learningenergy-efficiencyO-RANRAN optimization |
| spellingShingle | Shahram Mollahasani Turgay Pamuklu Rodney Wilson Melike Erol-Kantarci Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning Sensors actor–critic learning energy-efficiency O-RAN RAN optimization |
| title | Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning |
| title_full | Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning |
| title_fullStr | Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning |
| title_full_unstemmed | Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning |
| title_short | Energy-Aware Dynamic DU Selection and NF Relocation in O-RAN Using Actor–Critic Learning |
| title_sort | energy aware dynamic du selection and nf relocation in o ran using actor critic learning |
| topic | actor–critic learning energy-efficiency O-RAN RAN optimization |
| url | https://www.mdpi.com/1424-8220/22/13/5029 |
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