Normalized Generalized Mutual Information as a Forward Error Correction Threshold for Probabilistically Shaped QAM

We show that the normalized generalized mutual information represents an excellent forward error correction (FEC) threshold for uniform as well as for probabilistically shaped QAM and hence allows to accurately predict post-FEC performance from measured pre-FEC data.