Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices

2026-02-26

AI Summary

This work addresses the challenges of bandwidth, computational, and energy constraints alongside dynamic channel variations in semantic image communication for Internet of Things (IoT) applications by proposing a dual-adaptive deep joint source-channel coding (DJSCC) architecture. The proposed method integrates channel adaptation and spatial attention mechanisms jointly at both transmitter and receiver, leveraging signal-to-noise ratio (SNR) embedded training to enable a single trained model to dynamically respond to varying channel conditions and image semantic importance. Compared to existing adaptive DJSCC approaches, the proposed scheme achieves significant improvements across multiple performance metrics while incurring only marginal increases in computational complexity, making it well-suited for resource-constrained yet demanding IoT scenarios.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Internet of Things (IoT) networks face significant challenges such as limited communication bandwidth, constrained computational and energy resources, and highly dynamic wireless channel conditions. Utilization of deep neural networks (DNNs) combined with semantic communication has emerged as a promising paradigm to address these limitations. Deep joint source-channel coding (DJSCC) has recently been proposed to enable semantic communication of images. Building upon the original DJSCC formulation, low-complexity attention-style architectures has been added to the DNNs for further performance enhancement. As a main hurdle, training these DNNs separately for various signal-to-noise ratios (SNRs) will amount to excessive storage or communication overhead, which can not be maintained by small IoT devices. SNR Adaptive DJSCC (ADJSCC), has been proposed to train the DNNs once but feed the current SNR as part of the data to the channel-wise attention mechanism. We improve upon ADJSCC by a simultaneous utilization of doubly adaptive channel-wise and spatial attention modules at both transmitter and receiver. These modules dynamically adjust to varying channel conditions and spatial feature importance, enabling robust and efficient feature extraction and semantic information recovery. Simulation results corroborate that our proposed doubly adaptive DJSCC (DA-DJSCC) significantly improves upon ADJSCC in several performance criteria, while incurring a mild increase in complexity. These facts render DA-DJSCC a desirable choice for semantic communication in performance demanding but low-complexity IoT networks.

Problem

• Semantic Image Communication
• IoT Devices
• Dynamic Channel Conditions
• Limited Resources
• SNR Adaptation

Innovation

• Doubly Adaptive Attention
• Semantic Image Communication
• Deep Joint Source-Channel Coding
• IoT Networks
• Channel and Spatial Attention