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Found 60 papers, 17 papers with code
Evolving Semantic Communication with Generative Model
In this paper, we explore an evolving semantic communication system for image transmission, referred to as ESemCom, with the capability to continuously enhance transmission efficiency.
Channel Deduction: A New Learning Framework to Acquire Channel from Outdated Samples and Coarse Estimate
How to reduce the pilot overhead required for channel estimation?
Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding
Compared to Patch Embed, which requires more than one hundred tokens for one image, HOOK requires only 6 and 8 tokens for sparse and dense tasks, respectively, resulting in efficiency improvements of 1. 5 to 2. 8 times.
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
In this paper, we propose TernaryVote, which combines a ternary compressor and the majority vote mechanism to realize differential privacy, gradient compression, and Byzantine resilience simultaneously.
Channel Mapping Based on Interleaved Learning with Complex-Domain MLP-Mixer
For such a channel mapping task, inspired by the intrinsic coupling across the space and frequency domains, this letter proposes to use interleaved learning with partial antenna and subcarrier characteristics to represent the whole MIMO-OFDM channel.
Point Cloud in the Air
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality.
Cached Transformers: Improving Transformers with Differentiable Memory Cache
This work introduces a new Transformer model called Cached Transformer, which uses Gated Recurrent Cached (GRC) attention to extend the self-attention mechanism with a differentiable memory cache of tokens.
Robust Target Detection of Intelligent Integrated Optical Camera and mmWave Radar System
To improve the target detection performance under complex real-world scenarios, this paper proposes an intelligent integrated optical camera and millimeter-wave (mmWave) radar system.
AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusion
To this end, we propose an all-in-one image restoration framework with latent diffusion (AutoDIR), which can automatically detect and address multiple unknown degradations.
Online Resource Allocation for Semantic-Aware Edge Computing Systems
To solve this problem, an online low-complexity algorithm is proposed.
Resource Allocation for Semantic-Aware Mobile Edge Computing Systems
Moreover, the closed-form optimal solution of the semantic extraction factor is derived.
Semantic Information Extraction for Text Data with Probability Graph
In this paper, the problem of semantic information extraction for resource constrained text data transmission is studied.
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG).
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
LWC modulates the extreme values of weights by optimizing the clipping threshold.
GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation
Based on this, we propose contrastive learning with Gradient guided Sampling Strategy (GraSS) for RSI semantic segmentation.
Deep Joint Source-Channel Coding for Wireless Image Transmission with Entropy-Aware Adaptive Rate Control
Adaptive rate control for deep joint source and channel coding (JSCC) is considered as an effective approach to transmit sufficient information in scenarios with limited communication resources.
MIMO Precoding Design with QoS and Per-Antenna Power Constraints
Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem.
Distributed Learning over Networks with Graph-Attention-Based Personalization
In conventional distributed learning over a network, multiple agents collaboratively build a common machine learning model.
Semantic-aware Digital Twin for Metaverse: A Comprehensive Review
To facilitate the deployment of digital twins in Metaverse, the paradigm with semantic awareness has been proposed as a means for enabling accurate and task-oriented information extraction with inherent intelligence.
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer.
Revenue Management without Demand Forecasting: A Data-Driven Approach for Bid Price Generation
We develop a methodology to generate bid prices using historical booking data only.
From Data-driven Learning to Physics-inspired Inferring: A Novel Mobile MIMO Channel Prediction Scheme Based on Neural ODE
Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving.
Real-time Controllable Denoising for Image and Video
Controllable image denoising aims to generate clean samples with human perceptual priors and balance sharpness and smoothness.
Robust mmWave Beamforming by Self-Supervised Hybrid Deep Learning
Beamforming with large-scale antenna arrays has been widely used in recent years, which is acknowledged as an important part in 5G and incoming 6G.
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks.
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability.
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics
In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications.
WAIR-D: Wireless AI Research Dataset
The statistical data hinder the trained AI models from further fine-tuning for a specific scenario, and ray-tracing data with limited environments lower down the generalization capability of the trained AI models.
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems.
Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission
Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years.
False: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image
This indicates that the SSCL model has the ability to self-differentiate FNS and that the FALSE effectively mitigates the SCI in self-supervised contrastive learning.
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Model Compression for DNN-based Speaker Verification Using Weight Quantization
Experimental results on VoxCeleb show that weight quantization is effective for compressing SV models.
Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation
The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit.
Over-the-Air Split Machine Learning in Wireless MIMO Networks
In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.
Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO
Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead, where the downlink pilot transmission, CSI feedback at the user equipments (UEs), and CSI reconstruction at the BS are modeled as an end-to-end neural network based on Transformer.
Mobile MIMO Channel Prediction with ODE-RNN: a Physics-Inspired Adaptive Approach
Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead.
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space
It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive.
Ranked #2 on
Transferability
on classification benchmark
Environment Sensing Considering the Occlusion Effect: A Multi-View Approach
In this paper, we consider the problem of sensing the environment within a wireless cellular framework.
Semantic-preserved Communication System for Highly Efficient Speech Transmission
Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years.
Semantic-aware Speech to Text Transmission with Redundancy Removal
We also propose a two-stage training scheme, which speeds up the training of the proposed DL model.
Sufficient-Statistic Memory AMP
Given an arbitrary MAMP, we can construct an SS-MAMP by damping, which not only ensures the convergence of the state evolution, but also preserves the orthogonality, i. e., its dynamics can be correctly described by state evolution.
Dynamic Token Normalization Improves Vision Transformers
It is difficult for Transformers to capture inductive bias such as the positional context in an image with LN.
C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction
Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths.
Blind Channel Estimation for MIMO Systems via Variational Inference
In this paper, we investigate the blind channel estimation problem for MIMO systems under Rayleigh fading channel.
JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks
In this paper, a joint model split and neural architecture search (JMSNAS) framework is proposed to automatically generate and deploy a DNN model over a mobile edge network.
Communication-Efficient Federated Learning with Binary Neural Networks
{ Numerical results show that the proposed FL framework significantly reduces the communication cost compared to the conventional neural networks with typical real-valued parameters, and the performance loss incurred by the binarization can be further compensated by a hybrid method.
Joint Multi-User Communication and Sensing Exploiting Both Signal and Environment Sparsity
As a potential technology feature for 6G wireless networks, the idea of sensing-communication integration requires the system not only to complete reliable multi-user communication but also to achieve accurate environment sensing.
BWCP: Probabilistic Learning-to-Prune Channels for ConvNets via Batch Whitening
To address this problem, we develop a probability-based pruning algorithm, called batch whitening channel pruning (BWCP), which can stochastically discard unimportant channels by modeling the probability of a channel being activated.
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation
Prior arts often discretize the network weights by carefully tuning hyper-parameters of quantization (e. g. non-uniform stepsize and layer-wise bitwidths), which are complicated and sub-optimal because the full-precision and low-precision models have a large discrepancy.
Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss.
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
Distributed ADMM with Synergetic Communication and Computation
In this paper, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system.
Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method
Wireless communication in the TeraHertz band (0. 1--10 THz) is envisioned as one of the key enabling technologies for the future six generation (6G) wireless communication systems.
Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications
We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station.
AdaX: Adaptive Gradient Descent with Exponential Long Term Memory
Although adaptive optimization algorithms such as Adam show fast convergence in many machine learning tasks, this paper identifies a problem of Adam by analyzing its performance in a simple non-convex synthetic problem, showing that Adam's fast convergence would possibly lead the algorithm to local minimums.
Differentiable Learning-to-Group Channels via Groupable Convolutional Neural Networks
ResNeXt, still suffers from the sub-optimal performance due to manually defining the number of groups as a constant over all of the layers.
Temporal Sequence Distillation: Towards Few-Frame Action Recognition in Videos
Secondly, TSD significantly reduces the computations to run video action recognition with compressed frames on the cloud, while maintaining high recognition accuracies.
Boosting up Scene Text Detectors with Guided CNN
Deep CNNs have achieved great success in text detection.
Performance Evaluation of Channel Decoding With Deep Neural Networks
With the demand of high data rate and low latency in fifth generation (5G), deep neural network decoder (NND) has become a promising candidate due to its capability of one-shot decoding and parallel computing.
