Transceivers in the Age of Artificial Intelligence

Introduction

The rise of artificial intelligence (AI) is poised to revolutionize optical networks, making them more scalable, affordable, and sustainable. By gathering data from devices across the network and identifying patterns without human input, AI can become a centralized management and orchestration layer that fully automates network provisioning, diagnostics, and management.

As shown in Figure 1, AI can synergize with network function virtualization (NFV) to enable automated control over diverse applications like mobile, home, and office networks.

However, AI and machine learning algorithms are data-hungry, requiring rapid information from all network layers and high-speed data centers to process it quickly. This necessitates smarter, faster pluggable optical transceivers to relay more telemetry data back to the AI controller.

Figure 1: Example of a carrier network with different applications (mobile, home, office) automated via NFV control and AI management.

Faster Transceivers for AI

Optical transceivers play a crucial role in developing better AI systems by facilitating the rapid, reliable data transmission they require. High-speed, high-bandwidth connections are essential for interconnecting data centers and supercomputers that host AI systems, allowing them to analyze massive volumes of data.

Transceivers are also vital for facilitating AI-based edge computing by relocating compute resources to the network's edge. This minimizes latency and increases reaction times for processing data from Internet of Things (IoT) devices like sensors and cameras.

While 400 Gbps links are becoming standard for data center interconnects, providers are already considering faster solutions. As shown in Figure 2, LightCounting forecasts significant growth in shipments of dense wavelength division multiplexing (DWDM) ports with data rates of 600G, 800G, and beyond over the next five years.

Figure 2: Shipments of high-speed DWDM ports by data rate (historical data and forecast). Source: LightCounting Optical Communications Market Forecast, April 2022.

Coherent Modules Need Telemetry Data

To realize full network automation, future mobile networks comprising numerous devices, software, and technologies will require:

1. AI and machine learning algorithms for comprehensive network management and orchestration (MANO).

2. Sensor and control data flow across all network layers, including the physical layer.

As networks grow more complex, MANO software needs more telemetry data and programmable parameters ("dials to turn") to optimize provisioning and diagnostics.

This requires smart optical equipment like transceivers that provide comprehensive telemetry data about their status and connected fibers. The AI controller can then use this data for remote management and diagnostics.

For example, a smart transceiver could relay fiber condition data to the AI controller, including not just major faults but also smaller degradations from age, increased traffic stress, or nonlinear effects. This allows smarter traffic routing decisions.

A Smart Reconfigurable Transceiver

After relaying telemetry to the AI system, a smart pluggable transceiver must also adapt parameters based on the controller's instructions for different use cases.

One adaptable parameter is forward error correction (FEC), which makes coherent links more noise-tolerant for longer reach and higher capacity, as illustrated in Figure 3.

A smart transceiver with a digital signal processor (DSP) could switch between different FEC algorithms. For instance, upgrading a 100 Gbps metro link to 400 Gbps may require switching from an open FEC standard to a proprietary one for sufficient performance.

Reconfigurable transceivers can also auto-configure links based on specific network conditions detected via telemetry data:

• For high fiber quality, reduce modulation complexity or lower semiconductor optical amplifier (SOA) power.

• For poor fiber quality, use more limited modulation or increase power to reduce bit errors.

• For short fiber lengths, scale down laser transmit power or DSP consumption to save energy.

By adapting parameters like these, smart transceivers optimize performance and efficiency for each unique link scenario.

Figure 3: Simplified diagram of a network link with forward error correction (FEC). The FEC encoder adds redundant bits (overhead) to the transmitted data stream. The receiver can use this overhead to check for errors without asking the transmitter to resend the data.

Meeting AI's Need for Speed

To facilitate the growth of data-hungry AI systems, optical transceivers must evolve to be smarter and faster than ever before. Speeds beyond 400G will be required, with major growth expected for DWDM ports at 600G, 800G, and higher data rates.

But raw speed is not enough. Transceivers must also provide comprehensive telemetry data about link conditions to centralized AI management systems. This allows the AI to analyze real-time network status and intelligently reprogram transceiver parameters like modulation schemes, FEC algorithms, and amplifier power.

Through this symbiotic relationship, smart transceivers and AI controllers can fully automate and continuously optimize optical networks. AI unlocks new economies of scale, while transceivers supply the vital telemetry data that feeds AI systems.

The future of optical networking lies in this convergence of intelligent transceivers and artificial intelligence management – providing the scalability, efficiency, and automation required for next-generation networks supporting AI, 5G, cloud, and endless new bandwidth-intensive applications.

Reference

[1] "Transceivers in the Age of AI," March 22, 2023. [Online]. [Accessed: 20-March-2024].