360 research outputs found
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 Prediction of Nonbinary Forward Error Correction in Optical Transmission Experiments
In this paper, we compare different metrics to predict the error rate of
optical systems based on nonbinary forward error correction (FEC). It is shown
that the correct metric to predict the performance of coded modulation based on
nonbinary FEC is the mutual information. The accuracy of the prediction is
verified in a detailed example with multiple constellation formats, FEC
overheads in both simulations and optical transmission experiments over a
recirculating loop. It is shown that the employed FEC codes must be universal
if performance prediction based on thresholds is used. A tutorial introduction
into the computation of the threshold from optical transmission measurements is
also given.Comment: submitted to IEEE/OSA Journal of Lightwave Technolog
Information Rates and post-FEC BER Prediction in Optical Fiber Communications
Information-theoretic metrics to analyze optical fiber communications systems
with binary and nonbinary soft-decision FEC are reviewed. The numerical
evaluation of these metrics in both simulations and experiments is also
discussed. Ready-to-use closed-form approximations are presented.Comment: Invited paper, OFC 201
Performance Prediction Recipes for Optical Links
Although forward error-correction (FEC) coding is an essential part of modern
fiber-optic communication systems, it is impractical to implement and evaluate
FEC in transmission experiments and simulations. Therefore, it is desirable to
accurately predict the end-to-end link performance including FEC from
transmission data recorded without FEC. In this tutorial, we provide
ready-to-implement "recipes" for such prediction techniques, which apply to
arbitrary channels and require no knowledge of information or coding theory.
The appropriate choice of recipe depends on properties of the FEC encoder and
decoder. The covered metrics include bit error rate, symbol error rate,
achievable information rate, and asymptotic information, in all cases computed
using a mismatched receiver. Supplementary software implementations are
available
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
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
Hierarchical Distribution Matching for Probabilistically Shaped Coded Modulation
The implementation difficulties of combining distribution matching (DM) and
dematching (invDM) for probabilistic shaping (PS) with soft-decision forward
error correction (FEC) coding can be relaxed by reverse concatenation, for
which the FEC coding and decoding lies inside the shaping algorithms. PS can
seemingly achieve performance close to the Shannon limit, although there are
practical implementation challenges that need to be carefully addressed. We
propose a hierarchical DM (HiDM) scheme, having fully parallelized input/output
interfaces and a pipelined architecture that can efficiently perform the
DM/invDM without the complex operations of previously proposed methods such as
constant composition DM (CCDM). Furthermore, HiDM can operate at a
significantly larger post-FEC bit error rate (BER) for the same post-invDM BER
performance, which facilitates simulations. These benefits come at the cost of
a slightly larger rate loss and required signal-to-noise ratio at a given
post-FEC BER.Comment: 11 pages, 7 figure
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