Academic Journal
A backhaul adaptation scheme for IAB networks using deep reinforcement learning with recursive discrete choice model
| العنوان: | A backhaul adaptation scheme for IAB networks using deep reinforcement learning with recursive discrete choice model |
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| المؤلفون: | Sande, Malcolm Makomborero, Hlophe, Mduduzi Comfort, Maharaj, Bodhaswar T. Sunil |
| بيانات النشر: | Institute of Electrical and Electronics Engineers |
| سنة النشر: | 2023 |
| المجموعة: | University of Pretoria: UPSpace |
| مصطلحات موضوعية: | Backhaul, Choice aversion, Constrained Markov decision process, Deep reinforcement learning, Integrated access and backhaul, Recursive discrete choice model, Routing |
| الوصف: | Challenges such as backhaul availability and backhaul scalability have continued to outweigh the progress of integrated access and backhaul (IAB) networks that enable multi-hop backhauling in 5G networks. These challenges, which are predominant in poor wireless channel conditions such as foliage, may lead to high energy consumption and packet losses. It is essential that the IAB topology enables efficient traffic flow by minimizing congestion and increasing robustness to backhaul failure. This article proposes a backhaul adaptation scheme that is controlled by the load on the access side of the network. The routing problem is formulated as a constrained Markov decision process and solved using a dual decomposition approach due to the existence of explicit and implicit constraints. A deep reinforcement learning (DRL) strategy that takes advantage of a recursive discrete choice model (RDCM) was proposed and implemented in a knowledge-defined networking architecture of an IAB network. The incorporation of the RDCM was shown to improve robustness to backhaul failure in IAB networks. The performance of the proposed algorithm was compared to that of conventional DRL, i.e., without RDCM, and generative model-based learning (GMBL) algorithms. The simulation results of the proposed approach reveal risk perception by introducing certain biases on alternative choices and the results showed that the proposed algorithm provides better throughput and delay performance over the two baselines. ; The Sentech Chair in Broadband Wireless Multimedia Communications (BWMC) and the University of Pretoria. ; https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 ; Electrical, Electronic and Computer Engineering |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | Sande, M.M., Hlophe, M.C. & Maharaj, B.T.S. 2023, 'A backhaul adaptation scheme for IAB networks using deep reinforcement learning with recursive discrete choice model', IEEE Access, vol. 11, pp. 18385-18390, doi : 10.1109/ACCESS.2023.3243519.; 2169-3536 (online); http://hdl.handle.net/2263/92640 |
| DOI: | 10.1109/ACCESS.2023.3243519 |
| الاتاحة: | http://hdl.handle.net/2263/92640 https://doi.org/10.1109/ACCESS.2023.3243519 |
| Rights: | © 2023. The Authors. Licensee IEEE: This work is licensed under a Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0. |
| رقم الانضمام: | edsbas.386A500A |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/ACCESS.2023.3243519 |
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