ArticleCommunications engineering2026
Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission.
Article in Communications engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Fiber nonlinearity limits reach and capacity in long-haul coherent systems. Here we present a single-step, physics-informed neural approach to nonlinear compensation that preserves the parallel structure of the Volterra-series transfer function while learning data-driven corrections, unlike widely used digital back-propagation, which reconstructs propagation with numerous cascaded steps and therefore incurs high computational complexity and latency at comparable performance. The method integrates a trainable linear kernel with a compact nonlinear mapping and provides a hardware-suitable architecture. In a 1125-km WDM link, NN-VS matches the maximum performance achieved by leading digital back-propagation variants at 19% computational complexity and 1/25 latency; in a 12,057-km WDM system, the reductions increase to ~6.6% and ~1/176, yielding an efficiency improvement exceeding 2,600×. These results demonstrate that single-step, parallel compensation-augmented by learned corrections-helps mitigate performance-complexity-latency trade-offs, suggesting a pathway for the efficient deployment of nonlinear compensation in next-generation coherent optical networks.
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