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Performance monitoring is an essential function for margin measurements inlive systems. Historically, system budgets have been described by the Q-factorconverted from the bit error rate (BER) under binary modulation and directdetection. The introduction of hard-decision forward error correction (FEC) didnot change this. In recent years technologies have changed significantly tocomprise coherent detection, multilevel modulation and soft FEC. In suchadvanced systems, different metrics such as (nomalized) generalized mutualinformation (GMI/NGMI) and asymmetric information (ASI) are regarded as beingmore reliable. On the other hand, Q budgets are still useful because pre-FECBER monitoring is established in industry for live system monitoring.  The pre-FEC BER is easily estimated from available information of the numberof flipped bits in the FEC decoding, which does not require knowledge of thetransmitted bits that are unknown in live systems. Therefore, the use ofmetrics like GMI/NGMI/ASI for performance monitoring has not been possible inlive systems. However, in this work we propose a blind soft-performanceestimation method. Based on a histogram of log-likelihood-values without theknowledge of the transmitted bits, we show how the ASI can be estimated.  We examined the proposed method experimentally for 16 and 64-ary quadratureamplitude modulation (QAM) and probabilistically shaped 16, 64, and 256-QAM inrecirculating loop experiments. We see a relative error of 3.6%, whichcorresponds to around 0.5 dB signal-to-noise ratio difference for binarymodulation, in the regime where the ASI is larger than the assumed FECthreshold. For this proposed method, the digital signal processing circuitryrequires only a minimal additional function of storing the L-value histogramsbefore the soft-decision FEC decoder.

Performance Monitoring for Live Systems with Soft FEC and Multilevel Modulation