72 citations · 410 across the 39 of their papers we have counts for
21 papers · 1 filter
Belief Propagation Decoding of Quantum LDPC Codes with Guided Decimation
Hanwen Yao, Waleed Abu Laban, Christian Häger +2
Quantum low-density parity-check (QLDPC) codes have emerged as a promising technique for quantum error correction. A variety of decoders have been proposed for QLDPC codes and many…
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Vukan Ninkovic, Dejan Vukobratovic, Christian Häger +2
Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword). However, in many practical scenarios,…
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski +8
Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-c…
Data-Driven Estimation of Capacity Upper Bounds
Christian Häger, Erik Agrell
We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm…
Autoencoder-Based Unequal Error Protection Codes
Vukan Ninkovic, Dejan Vukobratovic, Christian Häger +2
We present a novel autoencoder-based approach for designing codes that provide unequal error protection (UEP) capabilities. The proposed design is based on a generalization of an a…
Pruning and Quantizing Neural Belief Propagation Decoders
Andreas Buchberger, Christian Häger, Henry D. Pfister +2
We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding…
Learned Decimation for Neural Belief Propagation Decoders
Andreas Buchberger, Christian Häger, Henry D. Pfister +2
We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-chec…
Pruning Neural Belief Propagation Decoders
Andreas Buchberger, Christian Häger, Henry D. Pfister +2
We consider near maximum-likelihood (ML) decoding of short linear block codes based on neural belief propagation (BP) decoding recently introduced by Nachmani et al.. While this me…
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
Fabrizio Carpi, Christian Häger, Marco Martalò +2
In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve…
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
Mengke Lian, Fabrizio Carpi, Christian Häger +1
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…
What Can Machine Learning Teach Us about Communications?
Mengke Lian, Christian Häger, Henry D. Pfister
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects. For communications, engineers with limited domain expertise can now use off-…
Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
Christian Häger, Henry D. Pfister
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 s…
ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters
Christoffer Fougstedt, Christian Häger, Lars Svensson +2
We consider time-domain digital backpropagation with chromatic dispersion filters jointly optimized and quantized using machine-learning techniques. Compared to the baseline implem…
Decoding Reed-Muller Codes Using Minimum-Weight Parity Checks
Elia Santi, Christian Häger, Henry D. Pfister
Reed-Muller (RM) codes exhibit good performance under maximum-likelihood (ML) decoding due to their highly-symmetric structure. In this paper, we explore the question of whether th…
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
Christian Häger, Henry D. Pfister
An important problem in fiber-optic communications is to invert the nonlinear Schrödinger equation in real time to reverse the deterministic effects of the channel. Interestingly,…
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Shen Li, Christian Häger, Nil Garcia +1
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel. The approach jointly optimizes the input distribution (constellation shaping…
Approaching Miscorrection-free Performance of Product and Generalized Product Codes
Christian Häger, Henry D. Pfister
Product codes (PCs) protect a two-dimensional array of bits using short component codes. Assuming transmission over the binary symmetric channel, the decoding is commonly performed…
Nonlinear Interference Mitigation via Deep Neural Networks
Christian Häger, Henry D. Pfister
A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP). For a 32x100 km fiber-optic link, the resulting "learned" DBP significantly red…
Density Evolution for Deterministic Generalized Product Codes on the Binary Erasure Channel at High Rates
Christian Häger, Henry D. Pfister, Alexandre Graell i Amat +1
Generalized product codes (GPCs) are extensions of product codes (PCs) where code symbols are protected by two component codes but not necessarily arranged in a rectangular array.…
Improving soft FEC performance for higher-order modulations via optimized bit channel mappings
Christian Häger, Alexandre Graell i Amat, Fredrik Brännström +2
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…
Design of APSK Constellations for Coherent Optical Channels with Nonlinear Phase Noise
Christian Häger, Alexandre Graell i Amat, Alex Alvarado +1
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 im…