Telecom News: ADTRAN, Keysight Technologies, TeraSignal, OFC 2026

Why It Matters

Power‑efficient optics reduce operating costs while high‑fidelity testing and adaptive link training ensure AI‑driven data centers meet performance and uptime expectations.

Key Takeaways

• ADTRAN's LiteWave800 consumes 0.8 W, 1 pJ/bit.
• Enables 800 Gbps links within tight power budgets.
• Keysight's AresONE 1600GE emulates 1.6 Tbps AI traffic.
• Supports 112 G and 224 G SerDes validation.
• TeraSignal's CMIS link training auto‑optimizes SNR and margin.

Pulse Analysis

The data‑center landscape is increasingly constrained by power and cooling limits, especially as AI workloads demand ever‑higher bandwidth. ADTRAN’s LiteWave800 tackles this head‑on, delivering 800 Gb/s connectivity while drawing less than a watt of power. By slashing energy per bit, operators can pack more GPU clusters into existing racks without overhauling cooling infrastructure, translating into lower total‑cost‑of‑ownership and a smaller carbon footprint.

Accurate performance validation is equally critical for AI‑centric networks. Keysight’s AresONE 1600GE offers a 1.6‑terabit emulation environment that reproduces real‑world AI traffic patterns, enabling engineers to stress‑test both 112 G and 224 G SerDes links at line rate. This capability shortens development cycles, uncovers scalability bottlenecks early, and assures that hyperscale fabrics can sustain the massive data flows characteristic of modern machine‑learning models.

Reliability across heterogeneous interconnects remains a lingering concern. TeraSignal’s CMIS‑based link‑training solution embeds its TSLink algorithm into the Common Management Interface Specification, allowing dynamic adjustment of link parameters to maximize signal‑to‑noise ratio and margin. Demonstrated at OFC 2026, the technology promises plug‑and‑play optimization for both optical and copper modules, reducing manual tuning and improving uptime. Collectively, these innovations signal a shift toward smarter, greener, and more resilient AI data‑center ecosystems.