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Noise Concatenation in Optical Amplifier Chains

Standards 4 Mins Read

Optical networks are the backbone of the internet, carrying vast amounts of data over great distances at the speed of light. However, maintaining signal quality over long fiber runs is a challenge due to a phenomenon known as noise concatenation. Let’s delve into how amplified spontaneous emission (ASE) noise affects Optical Signal-to-Noise Ratio (OSNR) and the performance of optical amplifier chains.

The Challenge of ASE Noise

ASE noise is an inherent byproduct of optical amplification, generated by the spontaneous emission of photons within an optical amplifier. As an optical signal traverses through a chain of amplifiers, ASE noise accumulates, degrading the OSNR with each subsequent amplifier in the chain. This degradation is a crucial consideration in designing long-haul optical transmission systems.

Understanding OSNR

OSNR measures the ratio of signal power to ASE noise power and is a critical parameter for assessing the performance of optical amplifiers. A high OSNR indicates a clean signal with low noise levels, which is vital for ensuring data integrity.

Reference System for OSNR Estimation

As depicted in Figure below), a typical multichannel N span system includes a booster amplifier, N−1 line amplifiers, and a preamplifier. To simplify the estimation of OSNR at the receiver’s input, we make a few assumptions:

Representation of optical line system interfaces (a multichannel N-span system)
  • All optical amplifiers, including the booster and preamplifier, have the same noise figure.
  • The losses of all spans are equal, and thus, the gain of the line amplifiers compensates exactly for the loss.
  • The output powers of the booster and line amplifiers are identical.

Estimating OSNR in a Cascaded System

E1: Master Equation For OSNR

E1: Master Equation For OSNR

Pout is the output power (per channel) of the booster and line amplifiers in dBm, L is the span loss in dB (which is assumed to be equal to the gain of the line amplifiers), GBA is the gain of the optical booster amplifier in dB, NFis the signal-spontaneous noise figure of the optical amplifier in dB, h is Planck’s constant (in mJ·s to be consistent with Pout in dBm), ν is the optical frequency in Hz, νr is the reference bandwidth in Hz (corresponding to c/Br ), N–1 is the total number of line amplifiers.

The OSNR at the receivers can be approximated by considering the output power of the amplifiers, the span loss, the gain of the optical booster amplifier, and the noise figure of the amplifiers. Using constants such as Planck’s constant and the optical frequency, we can derive an equation that sums the ASE noise contributions from all N+1 amplifiers in the chain.

Simplifying the Equation

Under certain conditions, the OSNR equation can be simplified. If the booster amplifier’s gain is similar to that of the line amplifiers, or if the span loss greatly exceeds the booster gain, the equation can be modified to reflect these scenarios. These simplifications help network designers estimate OSNR without complex calculations.

1)          If the gain of the booster amplifier is approximately the same as that of the line amplifiers, i.e., GBA » L, above Equation E1 can be simplified to:

osnr_2

E1-1

2)          The ASE noise from the booster amplifier can be ignored only if the span loss L (resp. the gain of the line amplifier) is much greater than the booster gain GBA. In this case Equation E1-1 can be simplified to:

E1-2

3)          Equation E1-1 is also valid in the case of a single span with only a booster amplifier, e.g., short‑haul multichannel IrDI in Figure 5-5 of [ITU-T G.959.1], in which case it can be modified to:

E1-3

4)          In case of a single span with only a preamplifier, Equation E1 can be modified to:

Practical Implications for Network Design

Understanding the accumulation of ASE noise and its impact on OSNR is crucial for designing reliable optical networks. It informs decisions on amplifier placement, the necessity of signal regeneration, and the overall system architecture. For instance, in a system where the span loss is significantly high, the impact of the booster amplifier on ASE noise may be negligible, allowing for a different design approach.

Conclusion

Noise concatenation is a critical factor in the design and operation of optical networks. By accurately estimating and managing OSNR, network operators can ensure signal quality, minimize error rates, and extend the reach of their optical networks.

In a landscape where data demands are ever-increasing, mastering the intricacies of noise concatenation and OSNR is essential for anyone involved in the design and deployment of optical communication systems.

References

https://www.itu.int/rec/T-REC-G/e

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BER and FEC in Optical Network Performance

Standards 3 Mins Read

Forward Error Correction (FEC) has become an indispensable tool in modern optical communication, enhancing signal integrity and extending transmission distances. ITU-T recommendations, such as G.693, G.959.1, and G.698.1, define application codes for optical interfaces that incorporate FEC as specified in ITU-T G.709. In this blog, we discuss the significance of Bit Error Ratio (BER) in FEC-enabled applications and how it influences optical transmitter and receiver performance.

The Basics of FEC in Optical Communications

FEC is a method of error control for data transmission, where the sender adds redundant data to its messages. This allows the receiver to detect and correct errors without the need for retransmission. In the context of optical networks, FEC is particularly valuable because it can significantly lower the BER after decoding, thus ensuring the accuracy and reliability of data across vast distances.

BER Requirements in FEC-Enabled Applications

For certain optical transport unit rates (OTUk), the system BER is mandated to meet specific standards only after FEC correction has been applied. The optical parameters, in these scenarios, are designed to achieve a BER no worse than 10−12 at the FEC decoder’s output. This benchmark ensures that the data, once processed by the FEC decoder, maintains an extremely high level of accuracy, which is crucial for high-performance networks.

Practical Implications for Network Hardware

When it comes to testing and verifying the performance of optical hardware components intended for FEC-enabled applications, achieving a BER of 10−12 at the decoder’s output is often sufficient. Attempting to test components at 10−12 at the receiver output, prior to FEC decoding, can lead to unnecessarily stringent criteria that may not reflect the operational requirements of the application.

Adopting Appropriate BER Values for Testing

The selection of an appropriate BER for testing components depends on the specific application. Theoretical calculations suggest a BER of 1.8×10−4at the receiver output (Point A) to achieve a BER of 10−12 at the FEC decoder output (Point B). However, due to variations in error statistics, the average BER at Point A may need to be lower than the theoretical value to ensure the desired BER at Point B. In practice, a BER range of 10−5 to 10−6 is considered suitable for most applications.

Conservative Estimation for Receiver Sensitivity

By using a BER of 10−6 for component verification, the measurements of receiver sensitivity and optical path penalty at Point A will be conservative estimates of the values after FEC correction. This approach provides a practical and cost-effective method for ensuring component performance aligns with the rigorous demands of FEC-enabled systems.

Conclusion

FEC is a powerful mechanism that significantly improves the error tolerance of optical communication systems. By understanding and implementing appropriate BER testing methodologies, network operators can ensure their components are up to the task, ultimately leading to more reliable and efficient networks.

As the demands for data grow, the reliance on sophisticated FEC techniques will only increase, cementing BER as a fundamental metric in the design and evaluation of optical communication systems.

References

https://www.itu.int/rec/T-REC-G/e

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ber_t_q_4

BER and Q Factor

Standards 3 Mins Read

Signal integrity is the cornerstone of effective fiber optic communication. In this sphere, two metrics stand paramount: Bit Error Ratio (BER) and Q factor. These indicators help engineers assess the performance of optical networks and ensure the fidelity of data transmission. But what do these terms mean, and how are they calculated?

What is BER?

BER represents the fraction of bits that have errors relative to the total number of bits sent in a transmission. It’s a direct indicator of the health of a communication link. The lower the BER, the more accurate and reliable the system.

ITU-T Standards Define BER Objectives

The ITU-T has set forth recommendations such as G.691, G.692, and G.959.1, which outline design objectives for optical systems, aiming for a BER no worse than 10−12 at the end of a system’s life. This is a rigorous standard that guarantees high reliability, crucial for SDH and OTN applications.

Measuring BER

Measuring BER, especially as low as 10−12, can be daunting due to the sheer volume of bits required to be tested. For instance, to confirm with 95% confidence that a system meets a BER of 10−12, one would need to test 3×1012 bits without encountering an error — a process that could take a prohibitively long time at lower transmission rates.

The Q Factor

The Q factor measures the signal-to-noise ratio at the decision point in a receiver’s circuitry. A higher Q factor translates to better signal quality. For a BER of 10−12, a Q factor of approximately 7.03 is needed. The relationship between Q factor and BER, when the threshold is optimally set, is given by the following equations:

The general formula relating Q to BER is:

bertoq

A common approximation for high Q values is:

ber_t_q_2

For a more accurate calculation across the entire range of Q, the formula is:

ber_t_q_3

Practical Example: Calculating BER from Q Factor

Let’s consider a practical example. If a system’s Q factor is measured at 7, what would be the approximate BER?

Using the approximation formula, we plug in the Q factor:

This would give us an approximate BER that’s indicative of a highly reliable system. For exact calculations, one would integrate the Gaussian error function as described in the more detailed equations.

Graphical Representation

ber_t_q_4

The graph typically illustrates these relationships, providing a visual representation of how the BER changes as the Q factor increases. This allows engineers to quickly assess the signal quality without long, drawn-out error measurements.

Concluding Thoughts

Understanding and applying BER and Q factor calculations is crucial for designing and maintaining robust optical communication systems. These concepts are not just academic; they directly impact the efficiency and reliability of the networks that underpin our modern digital world.

References

https://www.itu.int/rec/T-REC-G/e

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