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[2602.22794] Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices
Summary
This paper presents a novel approach to semantic image communication in IoT networks using a doubly adaptive channel and spatial attention mechanism, enhancing performance while minimizing complexity.
Why It Matters
As IoT devices proliferate, efficient communication methods are critical. This research addresses bandwidth and resource constraints, offering a solution that improves image transmission quality without overburdening device capabilities, which is essential for the future of IoT applications.
Key Takeaways
- Introduces a doubly adaptive DJSCC model for improved semantic image communication.
- Utilizes channel-wise and spatial attention to enhance feature extraction and information recovery.
- Demonstrates significant performance improvements over existing methods with manageable complexity.
- Addresses the challenges of limited bandwidth and resources in IoT environments.
- Provides a scalable solution suitable for performance-demanding IoT applications.
Computer Science > Machine Learning arXiv:2602.22794 (cs) [Submitted on 26 Feb 2026] Title:Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices Authors:Soroosh Miri, Sepehr Abolhasani, Shahrokh Farahmand, S. Mohammad Razavizadeh View a PDF of the paper titled Doubly Adaptive Channel and Spatial Attention for Semantic Image Communication by IoT Devices, by Soroosh Miri and 3 other authors View PDF HTML (experimental) 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 do...