Channel-Adaptive Conditional Diffusion Generation for Task-Oriented Semantic Communication

FU Weiqi; ZHAO Xiaoyu; JIN Lei; WANG Li; FEI Aiguo

doi:10.11999/JEIT260410

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FU Weiqi, ZHAO Xiaoyu, JIN Lei, WANG Li, FEI Aiguo. Channel-Adaptive Conditional Diffusion Generation for Task-Oriented Semantic Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260410

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FU Weiqi, ZHAO Xiaoyu, JIN Lei, WANG Li, FEI Aiguo. Channel-Adaptive Conditional Diffusion Generation for Task-Oriented Semantic Communication[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260410

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Channel-Adaptive Conditional Diffusion Generation for Task-Oriented Semantic Communication

doi: 10.11999/JEIT260410 cstr: 32379.14.JEIT260410

FU Weiqi^1
,,
ZHAO Xiaoyu^2
,,
JIN Lei^3
,,
WANG Li^{1
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FEI Aiguo^4
,

1.
School of Computer Science(National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China
3.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
4.
State Key Laboratory of Multi-domain Data Collaborative Processing and Control, China

Funds: The National Natural Science Foundation of China under Grant (62525106), BUPT-China Unicom Joint Innovation Center under Grant (2025-STHZ-BJYDDX-004)

Accepted Date: 2026-10-28
Rev Recd Date: 2026-10-28

Available Online: 2026-10-30

Abstract

Abstract

Objective In emergency rescue scenarios, on-site unmanned aerial vehicles and other intelligent equipment often lack sufficient computing power to execute complex perception tasks such as disaster identification and personnel detection, while transmitting raw image data back to edge servers suffers from severe bandwidth limitations and channel degradation. Task-oriented semantic communication systems address this challenge by transmitting only task-relevant semantic features, thereby achieving high compression ratios and robust performance under poor channel conditions. However, existing methods require end-to-end training, which becomes computationally prohibitive when integrating large generative models like Stable Diffusion. Moreover, channel noise accumulated during the reverse diffusion process can cause semantic drift, leading to degraded task accuracy. To overcome these limitations, this paper proposes a channel-adaptive conditional diffusion generation method for task-oriented semantic communication, specifically targeting image classification under low-signal-to-noise-ratio (SNR) conditions. The objective is to achieve robust semantic feature transmission and accurate classification by leveraging a pre-trained diffusion model at the receiver while maintaining low training complexity and adapting dynamically to varying channel qualities. Methods The proposed framework comprises three key components: a task-channel joint sensing encoder at the transmitter, a conditional diffusion generator based on Stable Diffusion (SD) with ControlNet at the receiver, and an adaptive condition guidance strategy. At the transmitter, the encoder extracts task-relevant semantic features, taking the input image and the current SNR as inputs. The encoder adaptively adjusts its feature extraction according to the channel condition via an attention-based modulation module. The semantic features are transmitted over an additive white Gaussian noise channel, yielding noisy features. At the receiver, a pre-trained SD model serves as the core generator, and a trainable ControlNet takes the noisy features as input to produce residual corrections that guide the reverse diffusion process. After a fixed number of DDIM sampling steps, the generated features are decoded by a task decoder to output classification results.To avoid the high computational cost of end-to-end backpropagation through the multi-step diffusion chain, a two-stage training framework is proposed. In the first stage, under a noise-free environment, an information bottleneck is used to learn task-relevant features and train the task decoder. The loss function combines cross-entropy classification loss and KL divergence for variational information bottleneck. In the second stage, the SD backbone is frozen, and only the task encoder and ControlNet are trained to adapt to channel noise. The training loss includes a standard diffusion denoising loss and a distillation loss that aligns the transmitted features with the first-stage features. To mitigate semantic drift caused by noise accumulation during reverse diffusion, an adaptive condition guidance strategy is introduced. A learnable weight network takes the current timestep and SNR as inputs and outputs a scaling factor between zero and two. When the scaling factor equals one, the conditional residual is injected as is; when it is less than one, the conditional guidance is weakened; when greater than one, the conditional guidance is enhanced. Results and Discussions Experiments are conducted on the ImageNet-100 dataset, comprising 130,000 training images and 5,000 validation images across 100 classes. All images are resized to 256×256 pixels and compressed to 4×32×32 latent features (48× compression). The Stable Diffusion v1.5 backbone is frozen during the second stage, with only the task encoder and ControlNet trained for 15 epochs at a learning rate of 1e-4. The adaptive weight network is trained during the DDIM sampling process for 5 epochs with a batch size of 96 and a learning rate of 1e-4, using 20 sampling steps.The proposed method is compared with two baselines: T-DeepSC, a single-task semantic communication system without diffusion, and DDSC+VGG16, a diffusion-driven semantic communication method that maps channel noise to forward diffusion steps and cascades VGG16 for classification. Under SNR values from -3 dB to 7 dB, the proposed method achieves the highest classification accuracy across all conditions. Specifically, at -3 dB, the proposed method attains 44.0% accuracy, outperforming T-DeepSC at 39.8% by 4.2 percentage points. At -1 dB, the proposed method reaches 53.2%, surpassing T-DeepSC at 40.0% by 13.2 percentage points. At 7 dB, the proposed method achieves 66.8%, exceeding DDSC+VGG16 at 58.4% by 8.4 percentage points. These results demonstrate that the proposed two-stage training framework with adaptive conditional guidance effectively recovers task-relevant semantic features from noisy transmissions, delivering superior performance even under severe channel degradation.Ablation studies further validate the contribution of each component. Removing the adaptive weight control, denoted as configuration B, reduces accuracy by 3.5 percentage points at -3 dB and 3.6 percentage points at -1 dB compared to the full method, configuration C, which achieves 44.0% and 53.2% respectively. Compared to the DDSC+VGG16 baseline, configuration A, introducing the task encoder and ControlNet, configuration B, yields dramatic improvements, for example, from 58.4% to 65.0% at 7 dB. This confirms that the two-stage framework successfully learns task-relevant semantic features and adapts to channel noise without end-to-end backpropagation through the diffusion chain. Conclusions This paper presents a channel-adaptive conditional diffusion generation method for task-oriented semantic communication, targeting image classification under bandwidth and resource constraints. The proposed framework integrates a task-channel joint sensing encoder, a ControlNet-guided Stable Diffusion generator, and a two-stage training strategy that decouples task feature learning from channel noise adaptation, significantly reducing training complexity compared to end-to-end diffusion-based alternatives. The adaptive condition guidance strategy dynamically adjusts the injection strength of noisy conditional information during the reverse diffusion process, suppressing semantic drift and improving classification robustness. Experimental results on ImageNet-100 demonstrate that the proposed method outperforms both task-oriented semantic communication without diffusion (T-DeepSC) and diffusion-driven reconstruction-then-classification (DDSC+VGG16) across most SNR conditions. In addition, the framework can also generate reconstructed images at the receiver, which may serve as visual auxiliary evidence for human verification in emergency rescue scenarios. Future work will extend the framework to multi-modal semantic fusion, model lightweighting for edge deployment, and more complex tasks such as object detection and visual question answering, facilitating practical adoption in emergency rescue and other mission-critical scenarios.
- task-oriented semantic communication,
- conditional diffusion model,
- channel adaptation,
- Stable Diffusion

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References(19)

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