Post-FEC BER Benchmarking for Bit-Interleaved Coded Modulation with Probabilistic Shaping
Tsuyoshi Yoshida, Alex Alvarado, Magnus Karlsson, and Erik Agrell

TL;DR
This paper investigates benchmarking post-FEC BER in optical fiber systems with probabilistic shaping, clarifying the relationships between various information metrics and their effectiveness in predicting system performance.
Contribution
It introduces generalized L-values under mismatched decoding and establishes theoretical links between NGMI, ASI, and achievable FEC rate, enhancing performance benchmarking methods.
Findings
ASI correlates better with post-FEC BER than pre-FEC BER.
NGMI, ASI, and FEC rate are equivalent under matched decoding.
Post-FEC BER varies with bit mapping at a given ASI.
Abstract
Accurate performance benchmarking after forward error correction (FEC) decoding is essential for system design in optical fiber communications. Generalized mutual information (GMI) has been shown to be successful at benchmarking the bit-error rate (BER) after FEC decoding (post-FEC BER) for systems with soft-decision (SD) FEC without probabilistic shaping (PS). However, GMI is not relevant to benchmark post-FEC BER for systems with SD-FEC and PS. For such systems, normalized GMI (NGMI), asymmetric information (ASI), and achievable FEC rate have been proposed instead. They are good at benchmarking post-FEC BER or to give an FEC limit in bit-interleaved coded modulation (BICM) with PS, but their relation has not been clearly explained so far. In this paper, we define generalized L-values under mismatched decoding, which are connected to the GMI and ASI. We then show that NGMI, ASI, and…
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