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

arXiv: https://arxiv.org/abs/2602.22794 · HTML: https://mlsanity.com/paper/2602.22794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2207.00427/) (2022)
- [A Survey on Semantic Communication Networks: Architecture, Security, and Privacy](https://mlsanity.com/paper/2405.01221/) (2024)
- [Engineering Semantic Communication: A Survey](https://mlsanity.com/paper/2208.06314/) (2022)
- [Collaborative Semantic Communication for Edge Inference](https://mlsanity.com/paper/2301.03996/) (2023)

## Abstract (arXiv)

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.