51 research outputs found
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
We propose a low-complexity sub-banded DSP architecture for digital
backpropagation where the walk-off effect is compensated using simple delay
elements. For a simulated 96-Gbaud signal and 2500 km optical link, our method
achieves a 2.8 dB SNR improvement over linear equalization.Comment: 3 pages, 3 figur
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
We consider the weighted belief-propagation (WBP) decoder recently proposed
by Nachmani et al. where different weights are introduced for each Tanner graph
edge and optimized using machine learning techniques. Our focus is on
simple-scaling models that use the same weights across certain edges to reduce
the storage and computational burden. The main contribution is to show that
simple scaling with few parameters often achieves the same gain as the full
parameterization. Moreover, several training improvements for WBP are proposed.
For example, it is shown that minimizing average binary cross-entropy is
suboptimal in general in terms of bit error rate (BER) and a new "soft-BER"
loss is proposed which can lead to better performance. We also investigate
parameter adapter networks (PANs) that learn the relation between the
signal-to-noise ratio and the WBP parameters. As an example, for the (32,16)
Reed-Muller code with a highly redundant parity-check matrix, training a PAN
with soft-BER loss gives near-maximum-likelihood performance assuming simple
scaling with only three parameters.Comment: 5 pages, 5 figures, submitted to ISIT 201
Design of APSK Constellations for Coherent Optical Channels with Nonlinear Phase Noise
We study the design of amplitude phase-shift keying (APSK) constellations for
a coherent fiber-optical communication system where nonlinear phase noise
(NLPN) is the main system impairment. APSK constellations can be regarded as a
union of phase-shift keying (PSK) signal sets with different amplitude levels.
A practical two-stage (TS) detection scheme is analyzed, which performs close
to optimal detection for high enough input power. We optimize APSK
constellations with 4, 8, and 16 points in terms of symbol error probability
(SEP) under TS detection for several combinations of input power and fiber
length. Our results show that APSK is a promising modulation format in order to
cope with NLPN. As an example, for 16 points, performance gains of 3.2 dB can
be achieved at a SEP of 10^-2 compared to 16-QAM by choosing an optimized APSK
constellation. We also demonstrate that in the presence of severe nonlinear
distortions, it may become beneficial to sacrifice a constellation point or an
entire constellation ring to reduce the average SEP. Finally, we discuss the
problem of selecting a good binary labeling for the found constellations. For
the class of rectangular APSK a labeling design method is proposed, resulting
in near-optimal bit error probability.Comment: Submitted to IEEE Transactions on Communication
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
End-to-end data-driven machine learning (ML) of multiple-input
multiple-output (MIMO) systems has been shown to have the potential of
exceeding the performance of engineered MIMO transceivers, without any a priori
knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and
multi-user MIMO and show that the gains of ML-based communication in the former
two cases can be to a large extent ascribed to implicitly learned geometric
shaping and bit and power allocation, not to learning new spatial encoders. For
MU-MIMO, we demonstrate the feasibility of a novel method with centralized
learning and decentralized executing, outperforming conventional zero-forcing.
For each scenario, we provide explicit descriptions as well as open-source
implementations of the selected neural-network architectures.Comment: 6 pages, 8 figures, conference pape
Density Evolution for Deterministic Generalized Product Codes with Higher-Order Modulation
Generalized product codes (GPCs) are extensions of product codes (PCs) where
coded bits are protected by two component codes but not necessarily arranged in
a rectangular array. It has recently been shown that there exists a large class
of deterministic GPCs (including, e.g., irregular PCs, half-product codes,
staircase codes, and certain braided codes) for which the asymptotic
performance under iterative bounded-distance decoding over the binary erasure
channel (BEC) can be rigorously characterized in terms of a density evolution
analysis. In this paper, the analysis is extended to the case where
transmission takes place over parallel BECs with different erasure
probabilities. We use this model to predict the code performance in a coded
modulation setup with higher-order signal constellations. We also discuss the
design of the bit mapper that determines the allocation of the coded bits to
the modulation bits of the signal constellation.Comment: invited and accepted paper for the special session "Recent Advances
in Coding for Higher Order Modulation" at the International Symposium on
Turbo Codes & Iterative Information Processing, Brest, France, 201
Deterministic and Ensemble-Based Spatially-Coupled Product Codes
Several authors have proposed spatially-coupled (or convolutional-like)
variants of product codes (PCs). In this paper, we focus on a parametrized
family of generalized PCs that recovers some of these codes (e.g., staircase
and block-wise braided codes) as special cases and study the iterative decoding
performance over the binary erasure channel. Even though our code construction
is deterministic (and not based on a randomized ensemble), we show that it is
still possible to rigorously derive the density evolution (DE) equations that
govern the asymptotic performance. The obtained DE equations are then compared
to those for a related spatially-coupled PC ensemble. In particular, we show
that there exists a family of (deterministic) braided codes that follows the
same DE equation as the ensemble, for any spatial length and coupling width.Comment: accepted at ISIT 2016, Barcelona, Spai
Improving soft FEC performance for higher-order modulations via optimized bit channel mappings
Soft forward error correction with higher-order modulations is often
implemented in practice via the pragmatic bit-interleaved coded modulation
paradigm, where a single binary code is mapped to a nonbinary modulation. In
this paper, we study the optimization of the mapping of the coded bits to the
modulation bits for a polarization-multiplexed fiber-optical system without
optical inline dispersion compensation. Our focus is on protograph-based
low-density parity-check (LDPC) codes which allow for an efficient hardware
implementation, suitable for high-speed optical communications. The
optimization is applied to the AR4JA protograph family, and further extended to
protograph-based spatially coupled LDPC codes assuming a windowed decoder. Full
field simulations via the split-step Fourier method are used to verify the
analysis. The results show performance gains of up to 0.25 dB, which translate
into a possible extension of the transmission reach by roughly up to 8%,
without significantly increasing the system complexity.Comment: This paper was published in Optics Express and is made available as
an electronic reprint with the permission of OSA. The paper can be found at
the following URL on the OSA website:
http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-22-12-1454
Revisiting Multi-Step Nonlinearity Compensation with Machine Learning
For the efficient compensation of fiber nonlinearity, one of the guiding
principles appears to be: fewer steps are better and more efficient. We
challenge this assumption and show that carefully designed multi-step
approaches can lead to better performance-complexity trade-offs than their
few-step counterparts.Comment: 4 pages, 3 figures, This is a preprint of a paper submitted to the
2019 European Conference on Optical Communicatio
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