Adaptive Semantic Communication for Wireless Image Transmission Leveraging Mixture-of-Experts Mechanism
Authors: Haowen Wan, Qianqian Yang
Organizations: Polytechnic Institute, Zhejiang University, Hangzhou, China · College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
Deep learning based semantic communication has achieved significant progress in wireless image transmission, but most existing schemes rely on fixed models and thus lack robustness to diverse image contents and dynamic channel conditions. To improve adaptability, recent studies have developed adaptive semantic communication strategies that adjust transmission or model behavior according to either source content or channel state. More recently, MoE-based semantic communication has emerged as a sparse and efficient adaptive architecture, although existing designs still mainly rely on single-driven routing. To address this limitation, we propose a novel multi-stage end-to-end image semantic communication system for multi-input multi-output (MIMO) channels, built upon an adaptive MoE Swin Transformer block. Specifically, we introduce a dynamic expert gating mechanism that jointly evaluates both real-time CSI and the semantic content of input image patches to compute adaptive routing probabilities. By selectively activating only a specialized subset of experts based on this joint condition, our approach breaks the rigid coupling of traditional adaptive methods and overcomes the bottlenecks of single-driven routing. Simulation results indicate a significant improvement in reconstruction quality over existing methods while maintaining the transmission efficiency.
Figures & tables
Fig. 1: The overall structure of the proposed scheme for wireless image transmission.
Fig. 2: (a) The specific architecture of two successive AD-MoE STBlock. (b) The architecture of AD-MoE MLP Block. Es , Er denotes the number of shared experts and the number of routed experts in AD-MoE MLP blocks respectively.
Fig. 3: (a) ∼ (b) PSNR and LPIPS performance of different models versus SNR under MIMO fading channels for the Kodak dataset, with R of 0.0833; results are shown for 2×2 and 8×8 transmit-receive antenna configurations. (c) ∼ (d) PSNR and LPIPS performance of different models versus R under MIMO fading channel of Kodak Dataset, with SNR of 10 dB; results are shown for 2×2 and 8×8 transmit-receive antenna configurations.
Fig. 4: Examples of visual comparison under MIMO fading channel at SNR = 10dB, R = 0.0833, Nt = 8, Nr = 8. The first column, second column, and third to fifth column shows the original image, original patch, and reconstructions of different transmission schemes, respectively.
Fig. 5: Expert activation frequency in the last AD-MoE STBlock of the final encoder layer, evaluated on the CLIC2021 dataset.
Image semantic communication is a critical component in next-generation wireless communication systems. However, such systems typically suffer from large memory footprints and high computational complexity, making them difficult to deploy on resource-constrained devices. To address these challenges, we propose a vision transformer (ViT)-enabled image semantic communication system. In this system, a recursive structure is introduced to iteratively refine semantic features and reduce the parameter count. In addition, three dynamic adjustment strategies are designed to adaptively reduce computational complexity: dynamic depth adjustment, dynamic width adjustment, and joint width-depth optimization. Dynamic depth adjustment adaptively determines the number of recursive modules according to image content and channel conditions, while dynamic width adjustment selectively preserves important neurons and attention heads. The joint width-depth optimization further enables flexible computation configurations. Simulation results verify that the proposed recursive ViT-based system, combined with the three dynamic adjustment strategies, reduces the parameter count by 48.7% and achieves higher reconstruction quality than existing baselines under comparable computational complexity.
Zhilong Zhang, Xinhui Zhang, Gongyu Jin +3
Beijing Laboratory of Advanced Information Network, and the Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China
The growing demand for efficient semantic communication systems capable of managing diverse tasks and adapting to fluctuating channel conditions has driven the development of robust, resource-efficient frameworks. This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture. Our framework optimizes the transmission of meaningful information by incorporating a multi-task-aware scoring mechanism that identifies and prioritizes semantically significant data across multiple concurrent tasks. A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions. By jointly optimizing semantic relevance and transmission efficiency, the framework ensures minimal performance degradation under resource constraints. Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection. These results underscore the framework's adaptability to heterogeneous channel environments and its scalability for multi-task applications, positioning it as a promising solution for next-generation semantic communication networks.
Xiang Chen, Shuying Gan, Chenyuan Feng +2
School of Electronics and Information Technology, Sun Yat-sen University, China · EURECOM, Sophia Antipolis, France · Singapore University of Technology and Design, Singapore
Semantic communication (SemCom) with learned encoder-decoder architectures enables end-to-end learning of compact task-oriented representations optimized for the wireless channel, reducing channel resources needed to convey task-relevant information and improving spectrum efficiency. This paper studies semantic image transmission over block Rayleigh fading with AWGN using a multi-task semantic autoencoder that jointly reconstructs images and predicts labels from the received waveform. The latent dimension (complex channel uses per source sample) serves as a cross-layer control variable governing semantic fidelity and channel resource usage. We characterize the resulting latency-task fidelity tradeoff: larger latent representations improve inference accuracy but increase service time, channel uses, and queueing delay. Building on this insight, we develop online semantic-rate controllers that adapt the latent dimension per update under a long-term semantic error constraint. A queue-aware drift-plus-penalty policy minimizes delay subject to an average semantic error cap, while a complementary age-aware policy minimizes time-average Age of Information (AoI). By adapting the semantic rate to congestion and fidelity requirements, the proposed framework improves spectrum utilization and enables timely semantic updates with significantly lower delay and AoI than fixed-rate baselines.