Study neural communication systems with bandwidth-limited channels.
problem Reliable message transmission despite noisy channels.
method Jointly model compression and error correction with neural networks; introduce prior for missing information; use auxiliary latent variables.
result Joint neural communication systems outperform separate models under expected information loss.
A central machine is interested in estimating the underlying structure of a sparse Gaussian Graphical Model (GGM) from datasets distributed across multiple local machines. The local machines can communicate with the central machine through a wireless multiple access channel. In this paper, we are interested in designin…
Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.
problem Statistical heterogeneity across clients in federated learning.
method Dynamic weighting, OTA aggregation, hierarchical federated learning.
result Training speed improvement and robustness against channel effects.
CKA with Gaussian RBF kernels converges linearly as bandwidth increases.
problem Understanding the behavior of CKA with large bandwidth Gaussian kernels.
method Analyzing the convergence of CKA based on Gaussian RBF kernels in the large-bandwidth limit.
result CKA based on Gaussian RBF kernels converges linearly as bandwidth increases.
Shared workspace improves neural module coordination in deep learning.
problem Pairwise interactions in deep learning models lack global coordination.
method Introduce a shared global workspace with bandwidth limitations among neural modules.
result Capacity limitations encourage specialization and synchronization.
Study investigates FL performance over a noisy downlink, showing analog approach outperforms digital.
problem Impact of bandwidth-limited downlink on federated learning performance.
method Modelled downlink and uplink channels; proposed digital and analog downlink approaches; analyzed convergence behavior.
result Analog downlink approach provides significant improvement over digital approach, especially with biased data distribution.
Scheduling and power allocation improve federated learning efficiency in NOMA networks.
problem Efficiently scheduling and allocating power for federated learning in bandwidth-limited wireless networks.
method Proposed a scheduling policy and power allocation scheme using NOMA to maximize data rate and convergence speed.
result Simulation results show improved federated learning accuracy in NOMA networks.
The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directl…
Self-supervised learning from unlabeled sensor data improves model performance in federated learning.
problem Lack of labeled data in decentralized IoT devices.
method Wavelet transform and contrastive learning for self-supervised feature extraction.
result Self-supervised features achieve strong performance and generalize well in federated learning.
Study bandwidth-limited training and inference of language models.
problem Training and inference of language models on scattered data with limited bandwidth.
method Analyzed two protocols: Federated Probe-Logit Distillation (FPLD) for training and Federated Conformal RAG (FC-RAG) for inference.
result Explicit high-probability KL-consistency rate and distribution-free marginal-coverage bound for Federated Conformal RAG.
For high data rate wireless communication systems, developing an efficient channel estimation approach is extremely vital for channel detection and signal recovery. With the trend of high-mobility wireless communications between vehicles and vehicles-to-infrastructure (V2I), V2I communications pose additional challenge…
Attention mechanism is a hot spot in deep learning field. Using channel attention model is an effective method for improving the performance of the convolutional neural network. Squeeze-and-Excitation block takes advantage of the channel dependence, selectively emphasizing the important channels and compressing the rel…
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.
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.
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.
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.
We present a definition of discrete channel surfaces in Lie sphere geometry, which reflects several properties for smooth channel surfaces. Various sets of data, defined at vertices, on edges or on faces, are associated with a discrete channel surface that may be used to reconstruct the underlying particular discrete L…
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.
Interpretation of electroencephalogram (EEG) signals can be complicated by obfuscating artifacts. Artifact detection plays an important role in the observation and analysis of EEG signals. Spatial information contained in the placement of the electrodes can be exploited to accurately detect artifacts. However, when few…
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.
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.
A group of transition probability functions form a Shannon's channel whereas a group of truth functions form a semantic channel. Label learning is to let semantic channels match Shannon's channels and label selection is to let Shannon's channels match semantic channels. The Channel Matching (CM) algorithm is provided f…
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.
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.
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.
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions b…
We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC w…
We discuss channel surfaces in the context of Lie sphere geometry and characterise them as certain Ω0-surfaces. Since Ω0-surfaces possess a rich transformation theory, we study the behaviour of channel surfaces under these transformations. Furthermore, by using certain Dupin cyclide congruences, we characteri…
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.
Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system. Most prior work in designing or learning new modulation schemes has focused on using highly simplified analytic channel models such as additive white Gaussian noise (AWGN), Rayleigh fad…
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.
A new approach for blind channel equalization and decoding, variational inference, and variational autoencoders (VAEs) in particular, is introduced. We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, with…
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…
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.
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.
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transiti…
The idea of end-to-end learning of communications systems through neural network -based autoencoders has the shortcoming that it requires a differentiable channel model. We present in this paper a novel learning algorithm which alleviates this problem. The algorithm iterates between supervised training of the receiver …
Edge computing tackles dynamic data in IIoT with incremental learning.
problem Latency and bandwidth limitations in IoT devices.
method Incremental learning applied to edge-computing systems for continual learning.
result Reduces catastrophic forgetting and provides efficient real-time quality control.
Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI). CC relies on dimensionality reduction of high-dimensional CSI features in order to construct a channel chart that captu…