Search Results for author:
Found 33 papers, 10 papers with code
Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices.
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
We propose a novel frequency-domain blind equalization scheme for coherent optical communications.
Semi-Supervised End-to-End Learning for Integrated Sensing and Communications
Integrated sensing and communications (ISAC) is envisioned as one of the key enablers of next-generation wireless systems, offering improved hardware, spectral, and energy efficiencies.
Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication
We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments.
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
Most of today's communication systems are designed to target reliable message recovery after receiving the entire encoded message (codeword).
Model-Driven End-to-End Learning for Integrated Sensing and Communication
Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G.
FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training
We design and implement an adaptive machine learning equalizer that alternates multiple linear and nonlinear computational layers on an FPGA.
Spatial Signal Design for Positioning via End-to-End Learning
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning.
Data-Driven Estimation of Capacity Upper Bounds
We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet.
Polarization Tracking in the Presence of PDL and Fast Temporal Drift
In this paper, we analyze the effectiveness of polarization tracking algorithms in optical transmission systems suffering from fast state of polarization (SOP) rotations and polarization-dependent loss (PDL).
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning.
End-to-End Learning for Integrated Sensing and Communication
The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure.
Over-the-fiber Digital Predistortion Using Reinforcement Learning
We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning.
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment
We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component.
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.
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.
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.
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
GMI-based end-to-end learning is shown to be highly nonconvex.
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.
Learning Physical-Layer Communication with Quantized Feedback
A novel quantization method is proposed, which exploits the specific properties of the feedback signal and is suitable for non-stationary signal distributions.
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.
Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning
Machine learning is used to compute achievable information rates (AIRs) for a simplified fiber channel.
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.
Nonlinear Interference Mitigation via Deep Neural Networks
A neural-network-based approach is presented to efficiently implement digital backpropagation (DBP).
