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Deep-reinforcement-...
Deep-reinforcement-learning-based RMSCA for space division multiplexing networks with multi-core fibers [Invited Tutorial]
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- Teng, Yiran (författare)
- University of Bristol
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- Natalino Da Silva, Carlos, 1987 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Li, Haiyuan (författare)
- University of Bristol
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- Yang, Ruizhi (författare)
- University of Bristol
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- Majeed, Jassim (författare)
- University of Bristol
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- Shen, Sen (författare)
- University of Bristol
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- Monti, Paolo, 1973 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Nejabati, Reza (författare)
- University of Bristol
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- Yan, Shuangyi (författare)
- University of Bristol
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- Simeonidou, Dimitra (författare)
- University of Bristol
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Journal of Optical Communications and Networking. - 1943-0620 .- 1943-0639. ; 16:7, s. C76-C87
- Relaterad länk:
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https://research.cha... (primary) (free)
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https://research.cha...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With continuous advances in multi-core fiber (MCF) fabrication, MCF-based SDM networks are positioned as a viable and promising solution to achieve higher transmission capacities in multi-dimensional optical networks. However, with the extensive network resources offered by MCF-based SDM networks comes the challenge of traditional routing, modulation, spectrum, and core allocation (RMSCA) methods to achieve appropriate performance. This paper proposes an RMSCA approach based on deep reinforcement learning (DRL) for MCF-based elastic optical networks (MCF-EONs). Within the solution, a novel state representation with essential network information and a fragmentation-aware reward function were designed to direct the agent in learning effective RMSCA policies. Additionally, we adopted a proximal policy optimization algorithm featuring an action mask to enhance the sampling efficiency of the DRL agent and speed up the training process. The performance of the proposed algorithm was evaluated with two different network topologies with varying traffic loads and fibers with different numbers of cores. The results confirmed that the proposed algorithm outperforms the heuristics and the state-of-the-art DRL-based RMSCA algorithm in reducing the service blocking probability by around 83% and 51%, respectively. Moreover, the proposed algorithm can be applied to networks with and without core switching capability and has an inference complexity compatible with real-world deployment requirements.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
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- art (ämneskategori)
- ref (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Teng, Yiran
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Natalino Da Silv ...
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Li, Haiyuan
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Yang, Ruizhi
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Majeed, Jassim
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Shen, Sen
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visa fler...
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Monti, Paolo, 19 ...
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Nejabati, Reza
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Yan, Shuangyi
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Simeonidou, Dimi ...
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Elektroteknik oc ...
- Artiklar i publikationen
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Journal of Optic ...
- Av lärosätet
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Chalmers tekniska högskola