Page 49 - Market Analysis Report of Optical Communications Field in China & Global market 2025

The Architectural Shift: How Intelligent OTN Is

Redefining Optical Networks for the AI Era

Gavin Gu
President of Optical Transport Network Domain of Huawei

T he transformation underway in optical transport millisecond-level latency alongside intelligent bandwidth
networks is not merely incremental—it is allocation that can adapt to applications ranging from
industrial AI and autonomous systems to immersive virtual
foundational. As artificial intelligence becomes the central environments.

driver of global digital infrastructure, the demands placed Intelligent OTN: Powering Networks, Em-
bedding Intelligence
on underlying networks have fundamentally changed. An
Huawei's Intelligent OTN solution embraces the opportunities
industry once characterized by decade-long innovation of the intelligent era through an integrated architecture
that synergistically enhances network performance(OTN
cycles now must adapt to the exponential growth curve of for AI) and embeds native intelligence(AI for OTN). This
forward-looking design moves beyond mere bandwidth
AI. In this new landscape, Huawei’s Intelligent OTN solution expansion, uniting powerful connectivity with adaptive, built-
in intelligence to create networks that are both supremely
emerges not as a simple product iteration, but as a systematic capable and continuously evolving.

response to one of the era’s most pressing technological

challenges: building networks capable of keeping pace with

AI’s insatiable demand for data movement and computational

coordination.

The AI Catalyst: Reshaping Optical Net-
work Economics and Requirements

The explosive growth of AI data centers represents one
of the most significant disruptions the optical transport
industry has ever faced. While previous technological shifts
unfolded within predictable parameters, AI is simultaneously
accelerating innovation cycles and redefining performance
standards. The traditional “ten years per generation” timeline
for optical technology has collapsed into just two to three
years, presenting both unprecedented challenges and
opportunities for network operators worldwide.

This acceleration is driving substantial gains in cost The “OTN for AI” dimension focuses on raw performance
efficiency. As wavelength technologies advance from 400G and efficiency. For DCI scenarios, this entails building ultra-
to 800G and toward future 3.2T implementations, the cost- wide backbone networks through three critical technological
per-bit of network construction declines correspondingly. advances. First, single-wave transmission rates are progressing
More fundamentally, however, AI workloads are forcing from 400G toward Beyond 1T implementations, dramatically
a reconceptualization of what networks must deliver. The reducing the cost-per-bit for network construction. Second,
emerging “Device–Pipe–Edge–Cloud” collaborative model network architectures are evolving from C-band ROADM
represents a paradigm shift—from the one-way data flows to C+L-band OXC, effectively doubling switching capacity
that characterized earlier cloud architectures to dynamic, within the same physical footprint. Third, and most critically
interactive exchanges that require deterministic performance. for AI workloads, the DC-OTN solution ensures zero
packet loss transmission between computing nodes—a non-
The performance requirements for AI-driven networks negotiable requirement in distributed training operations,
represent an order-of-magnitude improvement over traditional where even minimal packet loss can invalidate days of
standards. Data Center Interconnection (DCI) now demands computational work.
what industry insiders refer to 99.9999% availability, coupled
with terabit-level capacity to handle the massive parameter For DCA scenarios, the “OTN for AI” approach enables the
synchronization requirements of distributed AI training. construction of ultra-low latency metro networks through
Meanwhile, Data Center Access (DCA) scenarios require
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