AI-Optimized High-Capacity Tri-Concentric-Core Fiber with Tailored Index Gradients for 5G and Beyond
Year: 2025
Type: article
Abstract: The global expansion of 5G and the approaching 6G era are pushing conventional single-mode fibers toward their fundamental capacity limits, necessitating a paradigm shift in optical network infrastructure. This study introduces a novel, AI-optimized tri-concentric-core fiber with an optimized grading profile (TCC-OGP) to overcome this capacity crunch through spatial-division multiplexing (SDM). The fiber design was realized through an integrated artificial intelligence framework, combining a neural network surrogate model with particle swarm optimization to efficiently navigate a complex multi-objective design space. The resultant TCC-OGP fiber supports six spatial-division-multiplexed LP modes, achieving a breakthrough in the traditional capacity–nonlinearity trade-off. A comprehensive numerical analysis demonstrates that the proposed structure achieves 92% of the theoretical Shannon capacity while simultaneously suppressing nonlinear impairments by 65% compared to the standard single-core fiber. Furthermore, the fiber exhibits low differential mode delay, a flattened dispersion of approximately 16 ps/(nm·km) at 1550 nm, strong bend tolerance (<0.01 dB/m at a 30 mm radius), and excellent inter-modal crosstalk below −25 dB over 20 km. These performance metrics confirm the TCC-OGP fiber’s suitability for terabit-scale transmission in metro networks, dense 5G back-haul, and future 6G infrastructures, establishing a scalable and intelligent platform for next-generation optical networks.
Source: Photonics
Authors: Rabiu Imam Sabitu, Aliyu Aliyu, Amin Malekmohammadi
Institution: California State University, Bakersfield
Language: English
Topic: Optical Network Technologies
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4416845772