  Performance monitoring is an essential function for margin measurements in
live systems. Historically, system budgets have been described by the Q-factor
converted from the bit error rate (BER) under binary modulation and direct
detection. The introduction of hard-decision forward error correction (FEC) did
not change this. In recent years technologies have changed significantly to
comprise coherent detection, multilevel modulation and soft FEC. In such
advanced systems, different metrics such as (nomalized) generalized mutual
information (GMI/NGMI) and asymmetric information (ASI) are regarded as being
more reliable. On the other hand, Q budgets are still useful because pre-FEC
BER monitoring is established in industry for live system monitoring.
  The pre-FEC BER is easily estimated from available information of the number
of flipped bits in the FEC decoding, which does not require knowledge of the
transmitted bits that are unknown in live systems. Therefore, the use of
metrics like GMI/NGMI/ASI for performance monitoring has not been possible in
live systems. However, in this work we propose a blind soft-performance
estimation method. Based on a histogram of log-likelihood-values without the
knowledge of the transmitted bits, we show how the ASI can be estimated.
  We examined the proposed method experimentally for 16 and 64-ary quadrature
amplitude modulation (QAM) and probabilistically shaped 16, 64, and 256-QAM in
recirculating loop experiments. We see a relative error of 3.6%, which
corresponds to around 0.5 dB signal-to-noise ratio difference for binary
modulation, in the regime where the ASI is larger than the assumed FEC
threshold. For this proposed method, the digital signal processing circuitry
requires only a minimal additional function of storing the L-value histograms
before the soft-decision FEC decoder.
