article · Journal of Optical Communications and Networking · 2026

Transformer-pointer DRL model for static resource allocation problems in SDM-EONs

Cited by 1Abstract and details only

Abstract

The static resource allocation problem in space-division multiplexing elastic optical networks (SDM-EONs) requires joint optimization of routing, modulation, space, and spectrum assignment (RMSSA) for efficient resource use. Because integer-programming models and sophisticated heuristics are computationally expensive, the First-Fit algorithm is often used for fast feasible solutions, yet its quality is usually poor and highly order-dependent. To address this, we propose a deep reinforcement learning method for static resource allocation. We reformulate the combinatorial problem as sequence optimization by pairing with a fixed First-Fit allocator and prove that, for the space-spectrum assignment (SSA) subproblem, First-Fit is order-expressive and can achieve an optimal solution under a suitable ordering. A Transformer encoder extracts features of the request set, and a pointer-network decoder optimizes the output order. The trained network can be used as a black-box heuristic. Compared with hand-crafted orderings, it delivers higher solution quality with rapid inference speed, making it highly suitable for time-sensitive reconfiguration and large-scale planning tasks, while maintaining a runtime comparable to First-Fit. Finally, ablation studies verify the contributions of the encoder and decoder, and we attempt to interpret the trained network.

Cite this paper

APA 7

Chen, S., Wang, J., & Shigeno, M. (2026). Transformer-pointer DRL model for static resource allocation problems in SDM-EONs. Journal of Optical Communications and Networking, 18(3), 315. https://doi.org/10.1364/jocn.580228

MLA 9

Chen, Sibo, et al. “Transformer-pointer DRL model for static resource allocation problems in SDM-EONs.” Journal of Optical Communications and Networking, vol. 18, no. 3, 2026, p. 315, https://doi.org/10.1364/jocn.580228.

Chicago

Chen, Sibo, Jiading Wang, and Maiko Shigeno. 2026. “Transformer-pointer DRL model for static resource allocation problems in SDM-EONs.” Journal of Optical Communications and Networking 18 (3): 315. https://doi.org/10.1364/jocn.580228.

Harvard

Chen, S., Wang, J. and Shigeno, M. (2026) ‘Transformer-pointer DRL model for static resource allocation problems in SDM-EONs’, Journal of Optical Communications and Networking, 18(3), p. 315. Available at: https://doi.org/10.1364/jocn.580228.

BibTeX

@article{chen2026transformer,
  title = {{Transformer-pointer DRL model for static resource allocation problems in SDM-EONs}},
  author = {Chen, Sibo and Wang, Jiading and Shigeno, Maiko},
  journal = {Journal of Optical Communications and Networking},
  volume = {18},
  number = {3},
  pages = {315},
  year = {2026},
  doi = {10.1364/jocn.580228}
}

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