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Found 21 papers, 6 papers with code
Learned Image Transmission with Hierarchical Variational Autoencoder
In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE).
From Analog to Digital: Multi-Order Digital Joint Coding-Modulation for Semantic Communication
Herein, we treat modulation/demodulation as a constrained quantization process and introduce scaling operations alongside manually crafted noise to approximate this process.
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.
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).
One-shot Learning for Channel Estimation in Massive MIMO Systems
In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems.
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.
A Unified Multi-Task Semantic Communication System for Multimodal Data
Task-oriented semantic communications have achieved significant performance gains.
Multiband Delay Estimation for Localization Using a Two-Stage Global Estimation Scheme
In the coarse stage, we exploit the group sparsity structure of the multiband channel and propose a Turbo Bayesian inference (Turbo-BI) algorithm to achieve a good initial delay estimation based on a coarse signal model, which is transformed from the original multiband signal model by absorbing the carrier frequency terms.
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.
Blending Advertising with Organic Content in E-Commerce: A Virtual Bids Optimization Approach
In e-commerce platforms, sponsored and non-sponsored content are jointly displayed to users and both may interactively influence their engagement behavior.
Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems
In this letter, we investigate an intelligent reflecting surface (IRS) aided device-to-device (D2D) offloading system, where an IRS is employed to assist in computation offloading from a group of users with intensive tasks to another group of idle users.
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.
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.
