Deep-Reinforcement-Learning-based RMSCA for Space Division Multiplexing Networks with Multi-Core Fibers

Yiran Teng, Carlos Natalino, Ocean Haiyuan Li, Mark Yang, Jassim Majeed, Sen Shen, Paolo Monti, Reza Nejabati, Shuangyi Yan*, Dimitra Simeonidou

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review


The escalating demands for network capacities catalyze the adoption of space division multiplexing (SDM) technologies. With the 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 load and fibers with different number 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.
Original languageEnglish
Number of pages12
JournalJournal of Optical Communications and Networking
Publication statusAccepted/In press - 3 Apr 2024


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