15 research outputs found
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
End-to-end Learning for GMI Optimized Geometric Constellation Shape
Autoencoder-based geometric shaping is proposed that includes optimizing bit
mappings. Up to 0.2 bits/QAM symbol gain in GMI is achieved for a variety of
data rates and in the presence of transceiver impairments. The gains can be
harvested with standard binary FEC at no cost w.r.t. conventional BICM.Comment: submitted to ECOC 201
Required and received SNRs in coded modulation
Coded modulation techniques aim at reducing the required signal-to-noise ratio (SNR) over the Gaussian channel with an average energy constraint; however, such techniques tend to degrade the received SNR. We studied the balance of required and received SNRs for a realistic system design
Sparse-dense MLC for peak power constrained channels
Probabilistic amplitude shaping with Maxwell-Boltzmann distributions can degrade system budgets due to a large peak-to-average power ratio at a low spectral efficiency and at a short reach transmission with few optical amplifiers. We propose a novel coded modulation technique, which is useful in such scenarios
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