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Found 18 papers, 7 papers with code
Data-Driven Neural Polar Codes for Unknown Channels With and Without Memory
The proposed method leverages the structure of the successive cancellation (SC) decoder to devise a neural SC (NSC) decoder.
Polar Codes for Channels with Insertions, Deletions, and Substitutions
This paper presents a coding scheme for an insertion deletion substitution channel.
Information Theory Information Theory
Physics-Based Deep Learning for Fiber-Optic Communication Systems
Our main observation is that the popular split-step method (SSM) for numerically solving the NLSE has essentially the same functional form as a deep multi-layer neural network; in both cases, one alternates linear steps and pointwise nonlinearities.
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation.
Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration
One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems.
Belief Propagation with Quantum Messages for Quantum-Enhanced Classical Communications
For space-based laser communications, when the mean photon number per received optical pulse is much smaller than one, there is a large gap between communications capacity achievable with a receiver that performs individual pulse-by-pulse detection, and the quantum-optimal "joint-detection receiver" that acts collectively on long codeword-blocks of modulated pulses; an effect often termed "superadditive capacity".
Quantum Physics Information Theory Information Theory
Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation
We propose a model-based machine-learning approach for polarization-multiplexed systems by parameterizing the split-step method for the Manakov-PMD equation.
Pruning Neural Belief Propagation Decoders
In this paper, we introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning.
Logical Clifford Synthesis for Stabilizer Codes
Furthermore, we show that any circuit that normalizes the stabilizer of the code can be transformed into a circuit that centralizes the stabilizer, while realizing the same logical operation.
Quantum Physics Information Theory Information Theory
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding
In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes.
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.
Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation
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.
What Can Machine Learning Teach Us about Communications?
Rapid improvements in machine learning over the past decade are beginning to have far-reaching effects.
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.
ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters
We consider time-domain digital backpropagation with chromatic dispersion filters jointly optimized and quantized using machine-learning techniques.
Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications
For example, Ip and Kahn showed that for a 10 Gbaud signal and 2000 km optical link, a truncated SSFM with 25 steps would require 70-tap filters in each step and 100 times more operations than linear equalization.
Synthesis of Logical Clifford Operators via Symplectic Geometry
We propose a mathematical framework for synthesizing physical circuits that implement logical Clifford operators for stabilizer codes.
Information Theory Information Theory Quantum Physics 15Axx, 15B10, 20D45, 51A50, 68R01, 68W01, 81R05, 94B05
Nonlinear Interference Mitigation via Deep Neural Networks
A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP).
