Receiver FEC Decoding Assessment for BER and FER Control

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Solution Overview

Problem

Communication networks face challenges in accurately measuring and managing the Bit Error Rate (BER) and Frame Error Rate (FER) due to signal degradation, especially with correlated noise, which can lead to increased FEC iterations and frame errors, making it difficult to maintain low error rates without compromising system performance.

Innovation Solution

The solution involves using measured FEC iteration data to assess operating conditions by comparing it to reference distributions, allowing for the estimation of noise distribution and prediction of FEC performance, thereby optimizing network parameters to maintain low error rates and system performance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If FEC encoding is used to reduce BER, then error rate is reduced, but overhead increases

Engineering Contradiction:
ImproveBit Error RateVSAvoidOverhead
Core Design Contradiction:
ReliabilityVSQuantity of substance

Solution Approach 1:

The patent changes the parameter of FEC coding rate dynamically based on measured FEC iteration data and noise distribution characteristics. By adjusting the coding rate parameter according to actual channel conditions, the system achieves optimal balance between error correction capability and overhead, rather than using a fixed coding rate

Inventive Principle:
Principle #35Parameter changes

2Reliability

If more FEC iterations are performed to correct errors, then error correction capability improves, but processing time and complexity increase

Engineering Contradiction:
ImproveError Correction CapabilityVSAvoidDecoding Time
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary measurement of FEC iteration data and noise distribution before actual decoding operations. By pre-characterizing the channel conditions and error patterns, the system can determine optimal decoding parameters in advance, avoiding excessive iterations during actual data transmission and reducing processing time

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent dynamically adjusts FEC decoding parameters based on measured iteration data and noise characteristics. The system transitions from static FEC parameters to dynamic adaptation, where decoding complexity and iteration count are optimized in real-time based on actual channel conditions, balancing error correction capability with processing efficiency

Inventive Principle:
Principle #15Dynamics

3Reliability

If FEC parameters are optimized for low BER, then error rate decreases, but adaptability to varying noise conditions deteriorates

Engineering Contradiction:
ImproveBit Error RateVSAvoidAdaptability to noise conditions
Core Design Contradiction:
ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent implements a feedback mechanism where FEC iteration data is measured and used to characterize the noise distribution. This feedback loop enables the system to continuously adapt FEC parameters based on actual channel conditions, maintaining low BER while achieving adaptability to varying noise environments through iterative optimization

Inventive Principle:
Principle #23Feedback

Data Source

PatentEP3925112B1Assessing operating conditions of a receiver in a communication network based on forward error correction decoding properties
Publication Date: 2024.11.06 CIENA CORP
  • EP3925112B1 patent drawingFigure 1
  • EP3925112B1 patent drawingFigure 2~3
  • EP3925112B1 patent drawingFigure 4~5

AI summary

A system is configured to measure (602) a forward error correction (FEC) decoding property (216) associated with applying FEC decoding (214) to FEC-encoded bits or symbols at a receiver device (202) deployed in a communication network (100). The system is configured to provide (606) an assessment of operating conditions of the receiver device based on the FEC decoding property. The FEC decoding property comprises, for example, a distribution of a number of iterations of a FEC decoding operation applied to a plurality of FEC blocks processed within a period of time. In some examples, the FEC decoding property comprises any one of heat, temperature, current, voltage, active clock cycles, idle clock cycles, activity of parallel engines, activity of pipeline stages, and input or output buffer fill level of the FEC decoding. The assessment is based, for example, on a comparison between the FEC decoding property and reference FEC data (218).