67 research outputs found
On the Performance under Hard and Soft Bitwise Mismatched-Decoding
We investigated a suitable auxiliary channel setting and the gap between Q-factors with hard and soft demapping. The system margin definition should be reconsidered for systems employing complex coded modulation with soft forward error correction
On the Performance under Hard and Soft Bitwise Mismatched-Decoding
We investigated a suitable auxiliary channel setting and the gap between
Q-factors with hard and soft demapping. The system margin definition should be
reconsidered for systems employing complex coded modulation with soft forward
error correction.Comment: 3 pages, 4 figure
Post-FEC BER Benchmarking for Bit-Interleaved Coded Modulation with Probabilistic Shaping
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
achievable FEC rate are theoretically equal under matched decoding but not
under mismatched decoding. We also examine BER before FEC decoding (pre-FEC
BER) and ASI over Gaussian and nonlinear fiber-optic channels with
approximately matched decoding. ASI always shows better correlation with
post-FEC BER than pre-FEC BER for BICM with PS. On the other hand, post-FEC BER
can differ at a given ASI when we change the bit mapping, which describes how
each bit in a codeword is assigned to a bit tributary.Comment: 14 pages, 8 figure
Performance Metrics for Systems with Soft-Decision FEC and Probabilistic Shaping
High-throughput optical communication systems utilize binary soft-decision
forward error correction (SD-FEC) with bit interleaving over the bit channels.
The generalized mutual information (GMI) is an achievable information rate
(AIR) in such systems and is known to be a good predictor of the bit error rate
after SD-FEC decoding (post-FEC BER) for uniform signaling. However, for
probabilistically shaped (nonuniform) signaling, we find that the normalized
AIR, defined as the AIR divided by the signal entropy, is less correlated with
the post-FEC BER. We show that the information quantity based on the
distribution of the single bit signal, and its asymmetric loglikelihood ratio,
are better predictors of the post-FEC BER. In simulations over the Gaussian
channel, we find that the prediction accuracy, quantified as the peak-to-peak
deviation of the post-FEC BER within a set of different modulation formats and
distributions, can be improved more than 10 times compared with the normalized
AIR.Comment: 4 pages, 3 figure
Performance Monitoring for Live Systems with Soft FEC and Multilevel Modulation
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.Comment: 9 pages, 9 figure
Low-Complexity Near-Optimum Symbol Detection Based on Neural Enhancement of Factor Graphs
We consider the application of the factor graph framework for symbol
detection on linear inter-symbol interference channels. Based on the Ungerboeck
observation model, a detection algorithm with appealing complexity properties
can be derived. However, since the underlying factor graph contains cycles, the
sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we
develop and evaluate efficient strategies to improve the performance of the
factor graph-based symbol detection by means of neural enhancement. In
particular, we consider neural belief propagation and generalizations of the
factor nodes as an effective way to mitigate the effect of cycles within the
factor graph. By applying a generic preprocessor to the channel output, we
propose a simple technique to vary the underlying factor graph in every SPA
iteration. Using this dynamic factor graph transition, we intend to preserve
the extrinsic nature of the SPA messages which is otherwise impaired due to
cycles. Simulation results show that the proposed methods can massively improve
the detection performance, even approaching the maximum a posteriori
performance for various transmission scenarios, while preserving a complexity
which is linear in both the block length and the channel memory.Comment: revised version. arXiv admin note: text overlap with arXiv:2203.0333
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