This note improves code quality for equientropic channels by maximizing marginal entropy.
problem Improving code quality for equientropic channels with finite bit usage.
method Characterizes code quality by an upper bound on expected minimum error probability for equientropic channels, showing random coding maximizes marginal entropy.
result For equientropic channels, random coding maximizes marginal entropy and achieves minimal upper bound on expected minimum error probability.
A new channel locality block improves CNN performance.
problem Improving the performance of convolutional neural networks.
method Proposed a variant of Squeeze-and-Excitation block using convolutional layers to learn nearby channel correlation.
result Our C-Local block achieved higher accuracy than the standard SE block on the cifar-10 dataset.
Deep learning predicts V2I channel responses for high-mobility wireless communications.
problem Efficient channel estimation for high-mobility V2I communications.
method Developed a deep neural network-based channel prediction method.
result Deep neural networks can learn and predict V2I channel properties in real-time.
Reduced-channel EEG systems struggle with artifact detection, highlighting the importance of referential channels.
problem Artifact detection in EEG signals with fewer channels.
method Investigated a deep learning algorithm, CNN-LSTM, on various channel configurations.
result False alarms increase dramatically when fewer channels are used, emphasizing the importance of referential channels.
Defines discrete channel surfaces in Lie sphere geometry.
problem Defining discrete channel surfaces in Lie sphere geometry.
method Definition and associated data sets for reconstruction.
result Proof of a discrete version of Vessiot's Theorem for isothermic discrete channel surfaces.
The study characterizes channel surfaces in Lie sphere geometry and their transformations.
problem Characterizing channel surfaces in Lie sphere geometry.
method Using Ω0-surfaces and Dupin cyclide congruences to study transformations and Ribaucour pairs. result Characterization of channel surfaces and their transformations.
CM algorithm matches Shannon's and semantic channels for multi-label classification.
problem Tackles label learning and selection for multi-label classification.
method Adheres to maximum semantic information criterion, uses Bayes' theorem, and trains truth functions.
result Shows improved performance and adaptability to changing source distributions.
CN normalizes channels for better time series model performance.
problem Improper channel identification in time series models.
method Channel Normalization (CN) and its variants (ACN, PCN) using distinct affine transformations.
result Significant performance gains across various time series models.
Proposes dynamic channel pruning during neural network training.
problem Pruning neural networks during training to reduce computational cost and improve efficiency.
method Dynamic channel propagation to update channel utility values and selectively prune channels.
result Our scheme trains and prunes neural networks simultaneously, achieving superior performance.
A new DVAE architecture improves channel estimation by incorporating temporal correlations.
problem Improving the estimation of time-varying channels.
method Introducing k-MemoryMarkovVAE (k-MMVAE) architecture to learn temporal correlations.
result The k-MMVAE aided channel estimator outperforms other ML aided estimators.
Generative diffusion models improve channel sampling from limited data.
problem Challenges in channel modelling and data collection for wireless systems.
method Diffusion model with U-Net architecture for frequency domain synthesis.
result Stable training and diverse high-fidelity samples generated from true channel distribution.
New method uses VAEs for blind channel equalization and decoding.
problem Blind channel equalization and decoding without pilot symbols.
method Variational autoencoders (VAEs) for blind channel equalization and decoding.
result Significant improvement in error rate compared to existing methods.
BestChanID identifies the channel with maximal capacity using training sequences.
problem Identifying the channel with maximal capacity among several discrete memoryless channels.
method Formulated as a multi-armed bandit problem, proposed a capacity estimator, and developed gap-elimination algorithms.
result Guaranteed to output the DMC with the largest capacity with a desired confidence.
Quantum models simulate classical channels more efficiently, improving transmission rates.
problem Optimizing quantum models to simulate classical channels more efficiently.
method Examining when and how well quantum simulations of classical channels improve upon classical simulations.
result Quantum simulations can improve upon classical simulation rates.
A privacy-preserving method for transmitting data over a wiretap channel using generative networks.
problem Privacy protection in communication over a wiretap channel.
method Data-driven approach using variational autoencoder (VAE)-based joint source channel coding (JSCC).
result The approach provides high reconstruction quality at the receiver while confusing the eavesdropper about the latent sensitive attribute.
Deep JSCC maps images directly to channel symbols for wireless transmission.
problem Efficient image transmission in noisy wireless channels.
method Joint source and channel coding using CNNs trained jointly.
result Deep JSCC outperforms traditional JPEG/2000 + channel codes at low SNR.
New adaptive SGD algorithms for federated learning over physical channels.
problem Reducing communication cost in federated learning over physical channels.
method Proposed adaptive federated SGD algorithms considering channel noise and hardware constraints.
result Demonstrated convergence rates adaptive to stochastic gradient noise level.
Paper tackles accurate channel modeling using variational GANs.
problem Accurately learning channel probability distribution functions (PDFs).
method Variational GAN architecture and loss function for stochastic channel modeling.
result Accurately captures stochastic channel behavior with improved PDFs.
New algorithm separates maternal and fetal ECG from two channels in pregnant women.
problem Affordable ECG monitors for maternal-fetal health monitoring.
method Diffusion-based channel selection for accurate separation.
result Algorithm accurately separates maternal and fetal ECG from two channels.
New ML models improve VVLC channel characterization for vehicular OWC.
problem Inaccurate channel models for VVLC due to mobility effects.
method Machine learning (ML) models incorporating ambient light, turbulence, and reflection effects.
result ML models predict VVLC channel loss and CFR more accurately than existing methods.
VAE leverages MMSE channel estimation with data-driven modeling.
problem Data-driven channel estimation for wireless communications.
method Variational autoencoder (VAE) modeling of channel distribution and LMMSE approximation.
result VAE-based channel estimators approximate MMSE performance with practical training methods.
Paper improves channel charting using autoencoders with spatial constraints.
problem Improving logical positioning of UEs using channel-state information.
method Representation-constrained autoencoders to enhance channel charts.
result Improved quality of learned channel charts for UE positioning.
New algorithm learns communications systems without channel models.
problem Need for a differentiable channel model in end-to-end learning.
method Iterative training between receiver and transmitter using reinforcement learning.
result Works as well as fully supervised methods on AWGN and RBF channels.
Channel normalization prevents vanishing gradients in convolutional neural networks.
problem Vanishing gradients in convolutional neural networks during optimization.
method Channel normalization, which centers and normalizes each channel individually.
result Channel normalization avoids vanishing gradients, enabling efficient optimization.
New DL algorithm estimates OFDM channels without pilots.
problem Estimating OFDM channels in deep fading conditions.
method Deep learning (DL) for blind channel estimation.
result First theory on MSE performance of DL-based estimator.
Paper proposes online learning for MIMO channel estimation using neural networks.
problem Challenges in MIMO channel estimation due to physical model simplifications and system configuration requirements.
method Unfold a channel estimation algorithm as a neural network, allowing online learning and correction of imperfect models.
result Achieves channel estimation error close to that of a perfectly calibrated system.
ICE algorithm removes noisy channel assumption for universal discrete denoising.
problem Universal discrete denoising under channel uncertainty.
method Iterative channel estimation based on Neural DUDE, alternating channel estimation and neural network parameter estimation.
result ICE-N-DUDE achieves superior performance regardless of channel and clean source uncertainties.
Novel channel pruning method accelerates deep CNNs by removing redundant, similar features.
problem Limitations of existing magnitude-based pruning algorithms in similar magnitude cases.
method Hierarchical clustering of channels based on probabilistic similarity metrics, without sparsity training.
result 30% reduction in FLOPs for pruned ResNet-50 on ImageNet, outperforming baseline.
Deep learning improves channel estimation in communication systems.
problem Improving channel estimation accuracy in communication systems.
method Proposed a deep learning pipeline using image processing techniques to estimate channel response.
result The proposed algorithm shows comparable performance to MMSE with full channel knowledge and better than ALMMSE.
MGLM models all possible language channel factorizations for improved multilingual generation.
problem Generating multilingual text with flexibility and quality.
method Generative joint distribution model over language channels, marginalizing all possible factorizations.
result MGLM outperforms traditional models in multilingual generation tasks.
CC charts radio geometry for user localization.
problem Locating users in radio environments.
method Unsupervised learning from passive CSI.
result Extracts channel features for spatial comparison.
Proposes GPCA module for channel attention in CNNs using Gaussian processes.
problem Improving performance in visual tasks through effective channel selection.
method Integrates Gaussian processes into channel attention mechanisms for probabilistic modeling of channel correlations.
result Demonstrates improved performance of GPCA module in end-to-end CNN training.
ReQuestNet simplifies 5G channel estimation with a unified model.
problem Complex channel estimation in 5G systems with varying conditions.
method Unified neural architecture that handles dynamic resource blocks and transmit layers.
result Significantly outperforms legacy methods, achieving up to 10dB gain at high SNRs.
SOR-Mamba improves Mamba for robust time series forecasting by minimizing channel order bias.
problem Robust time series forecasting with Mamba's sequential order bias.
method SOR-Mamba incorporates regularization to minimize channel order discrepancy and introduces CCM for channel correlation preservation.
result SOR-Mamba enhances robustness to channel order and improves forecasting accuracy.
DSF improves EEG model robustness to missing channels and noise.
problem Robust learning from corrupted EEG data with missing channels.
method Dynamic Spatial Filtering (DSF) as a multi-head attention module.
result DSF achieves up to 29.4% accuracy improvement over baseline models in noisy conditions.
New framework improves multivariate time series forecasting by minimizing redundant information.
problem Improving multivariate time series forecasting with deep learning techniques.
method Cross-variable Decorrelation Aware feature Modeling (CDAM) and Temporal correlation Aware Modeling (TAM) to refine Channel-mixing and exploit temporal correlations.
result Significantly surpasses existing models in comprehensive tests.
Novel NDL framework improves spike detection accuracy and channel localization in EEG/MEG data.
problem Manual spike identification is time-consuming and requires specialized training.
method Nested Deep Learning (NDL) framework that combines signals across all channels.
result Improves prediction accuracy and achieves better channel localization.
New Bayes' theorem optimizes semantic channels for machine learning.
problem Class imbalance and semantic meaning evolution in natural language.
method Convert Shannon's channel to semantic channel using third kind of Bayes' theorem.
result CM algorithm explains natural language evolution and improves predictive models.
Dual U-net models improve multi-channel MRI image reconstruction.
problem Improving MRI image reconstruction from multi-channel data.
method Two-element U-nets (W-nets) in k-space and image domains, evaluated for four configurations.
result Dual domain methods are more advantageous for simultaneous reconstruction of all channels.
Paper proposes a new pipeline for few-shot classification using forget-update module and channel vector sequence.
problem Few-shot classification with limited support samples.
method Channel vector sequence construction module and forget-update module.
result Pipeline achieves state-of-the-art results on various datasets.
In agreement with the recent research findings in the econophysics, we propose that the nonlinear dynamic chaos can be generated by the turbulent capital flows in both the quantitative easing transmission channels and the transaction networks channels, when there are the laminar turbulent capital flows transitions in t…
CNNs improve positioning accuracy in massive MIMO systems.
problem Improving positioning accuracy in massive MIMO systems.
method Applying CNNs to learn sparse massive MIMO channel fingerprints.
result Moderately deep CNNs achieve fractional-wavelength positioning accuracy.
A new method prunes neural network channels based on operation characteristics.
problem Compressing deep neural networks efficiently and maintaining accuracy.
method Differentiable masks for channel pruning considering BN and ReLU.
result Outstanding performance in accuracy with less resources compared to state-of-the-art methods.
Proposes a channel pruning method using attention statistics for deep networks.
problem Manual setting of compression ratios in each layer for deep neural networks.
method Channel pruning based on attention statistics with automatic selection of compression ratio.
result Improved performance in terms of accuracy and computational costs compared to conventional methods.
DUET enhances multivariate time series forecasting by clustering time and channels.
problem Heterogeneous temporal patterns and complex channel correlations in multivariate time series.
method DUET uses dual clustering on temporal and channel dimensions to handle these challenges.
result DUET achieves state-of-the-art performance on 25 real-world datasets.
Adversaries can fool deep learning modulator classifiers over wireless channels.
problem Vulnerability of deep learning modulator classifiers to adversarial attacks over wireless channels.
method Presented various adversarial attacks considering channel effects, including targeted and non-targeted attacks, and a universal adversarial perturbation attack.
result Modulation classification is vulnerable to adversarial attacks over wireless channels with realistic channel effects.
DNAMTA model improves media channel attribution accuracy.
problem Measuring the impact of each advertising channel in multi-channel marketing.
method Deep Neural Net with Attention (DNAMTA) model for multi-touch attribution.
result DNAMTA model outperforms existing methods in conversion prediction and media influence evaluation.
Channel gating reduces CNN computation cost by skipping ineffective feature regions.
problem Reducing computation cost in CNNs while maintaining accuracy.
method Dynamic, fine-grained pruning scheme that identifies and skips computation on ineffective feature regions.
result 2.7-8.0x reduction in FLOPs and 2.0-4.4x reduction in memory accesses with minimal accuracy loss.