Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules

Abstract

Recent research on joint source channel coding (JSCC) for wireless communications has achieved great success owing to the employment of deep learning (DL). However, the existing work on DL based JSCC usually trains the designed network to operate under a specific signal-to-noise ratio (SNR) regime, without taking into account that the SNR level during the deployment stage may differ from that during the training stage. A number of networks are required to cover the scenario with a broad range of SNRs, which is computational inefficiency (in the training stage) and requires large storage. To overcome these drawbacks our paper proposes a novel method called Attention DL based JSCC (ADJSCC) that can successfully operate with different SNR levels during transmission. This design is inspired by the resource assignment strategy in traditional JSCC, which dynamically adjusts the compression ratio in source coding and the channel coding rate according to the channel SNR. This is achieved by resorting to attention mechanisms because these are able to allocate computing resources to more critical tasks. Instead of applying the resource allocation strategy in traditional JSCC, the ADJSCC uses the channel-wise soft attention to scaling features according to SNR conditions. We compare the ADJSCC method with the state-of-the-art DL based JSCC method through extensive experiments to demonstrate its adaptability, robustness and versatility. Compared with the existing methods, the proposed method takes less storage and is more robust in the presence of channel mismatch.

14 pages, 13 figures, journal paper

References in corpus (3)

• Recurrent Models of Visual Attention
• Deep Learning for Audio Signal Processing
• Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels

Cited by in corpus (16)

• Channel-Adaptive Wireless Image Transmission with OFDM
• Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks
• Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity
• Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications
• An Efficient Federated Learning Framework for Training Semantic Communication System
• High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models
• Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel Noises
• Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction
• Semantic Communication Enabled 6G-NTN Framework: A Novel Denoising and Gateway Hop Integration Mechanism
• Optimizing Multi-User Semantic Communication via Transfer Learning and Knowledge Distillation
• Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications
• An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation
• Artificial Intelligence Driven Channel Coding and Resource Optimization for Wireless Networks: A Systematic Survey
• Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels
• Deep Learning Based OFDM Channel Estimation Using Frequency-Time Division and Attention Mechanism
• Adversarial Jamming for Autoencoder Distribution Matching