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Found 32 papers, 8 papers with code
An Information-Theoretic Framework for Out-of-Distribution Generalization
We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds.
Feature Allocation for Semantic Communication with Space-Time Importance Awareness
To tackle this challenge, we introduce a framework called Feature Allocation for Semantic Transmission (FAST), which offers adaptability to channel fluctuations across both spatial and temporal domains.
Deep Refinement-Based Joint Source Channel Coding over Time-Varying Channels
This specialization poses a limitation, as their performance tends to wane in practical scenarios marked by highly dynamic channels, given that a fixed SNR inadequately represents the dynamic nature of such channels.
Alleviating Distortion Accumulation in Multi-Hop Semantic Communication
To address the problem of distortion accumulation, we introduce a novel recursive training method for the encoder and decoder of semantic communication systems.
SCAN: Semantic Communication with Adaptive Channel Feedback
Then, since the images with lower reconstruction quality are generally less robust and need to be allocated with more communication resources, we propose a novel framework of Semantic Communication with Adaptive chaNnel feedback (SCAN).
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments
The proposed framework, consisting of an inner network and an outer network, aims to adapt to the dynamic wireless environment by achieving three important goals, i. e., seamlessness, quickness and continuity.
Rate-Adaptive Coding Mechanism for Semantic Communications With Multi-Modal Data
Based on the proposed framework, we further establish a general rate-adaptive coding mechanism for various types of multi-modal semantic tasks.
Deep-Unfolding for Next-Generation Transceivers
Then, some endeavors in applying deep-unfolding approaches in next-generation advanced transceiver design are presented.
FAST: Feature Arrangement for Semantic Transmission
In this paper, we propose a scheme of Feature Arrangement for Semantic Transmission (FAST).
Adaptive CSI Feedback for Deep Learning-Enabled Image Transmission
In particular, we develop a performance evaluator to predict the reconstruction quality of each image, so that the proposed scheme can adaptively decrease the CSI feedback overhead for the transmitted images with high predicted reconstruction qualities in the JSCC system.
Design and Performance Analysis of Wireless Legitimate Surveillance Systems with Radar Function
We seek to jointly optimize the receive and transmit beamforming vectors to maximize the eavesdropping success probability which is transformed into the difference of signal-to-interference-plus-noise ratios (SINRs) subject to the performance requirements of radar and surveillance.
Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions
As an efficient graph analytical tool, graph neural networks (GNNs) have special properties that are particularly fit for the characteristics and requirements of wireless communications, exhibiting good potential for the advancement of next-generation wireless communications.
A Unified Multi-Task Semantic Communication System for Multimodal Data
Task-oriented semantic communications have achieved significant performance gains.
Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks
As an efficient neural network model for graph data, graph neural networks (GNNs) recently find successful applications for various wireless optimization problems.
Robust Semantic Communications with Masked VQ-VAE Enabled Codebook
Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated.
A Unified Multi-Task Semantic Communication System with Domain Adaptation
Simulation results demonstrate that our proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task with significant transmission overhead reduction and much less number of model parameters.
Robust Semantic Communications Against Semantic Noise
In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise.
DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learning
Furthermore, the proposed framework is extended to realize the adaptive depth of the general deep neural networks (DNNs).
Deep-Unfolding Beamforming for Intelligent Reflecting Surface assisted Full-Duplex Systems
Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue.
Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding
To reduce the signaling overhead and channel state information (CSI) mismatch caused by the transmission delay, a two-timescale DNN composed of a long-term DNN and a short-term DNN is developed.
A New Distributed Method for Training Generative Adversarial Networks
This paper proposes a new framework for training GANs in a distributed fashion: Each device computes a local discriminator using local data; a single server aggregates their results and computes a global GAN.
Decentralized Inference with Graph Neural Networks in Wireless Communication Systems
Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems.
Learning-based WiFi Traffic Load Estimation in NR-U Systems
An unsupervised Neural Network (NN) structure is applied to filter the detected transmission collision probability on the unlicensed spectrum, which enables the NR users to precisely rectify the measurement error and estimate the number of active WiFi users.
Information Theory Information Theory
Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays
In this work, we investigate the joint design of beam selection and digital precoding matrices for mmWave MU-MIMO systems with DLA to maximize the sum-rate subject to the transmit power constraint and the constraints of the selection matrix structure.
A Fast Graph Neural Network-Based Method for Winner Determination in Multi-Unit Combinatorial Auctions
However, the problem of allocating items among the bidders to maximize the auctioneers" revenue, i. e., the winner determination problem (WDP), is NP-complete to solve and inapproximable.
Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems
Then, we implement the proposed deepunfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems.
Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness
In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.
Accelerating Generalized Benders Decomposition for Wireless Resource Allocation
Only useful cuts are added to the master problem and thus the complexity of the master problem is reduced.
Deep Learning based Wireless Resource Allocation with Application to Vehicular Networks
The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality.
Graph Embedding based Wireless Link Scheduling with Few Training Samples
In this paper, we propose a novel graph embedding based method for link scheduling in D2D networks.
Accelerating DNN Training in Wireless Federated Edge Learning Systems
The optimal solution in this scenario is manifested to have the similar structure as that of the CPU scenario, recommending that our proposed algorithm is applicable in more general systems.
Learning to Branch: Accelerating Resource Allocation in Wireless Networks
Moreover, we develop a mixed training strategy to further reinforce the generalization ability and a deep neural network (DNN) with a novel loss function to achieve better dynamic control over optimality and computational complexity.
Information Theory Information Theory
