发表机构
Beijing Institute of Technology; The University of Hong Kong（北京理工大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对资源受限无线边缘设备的图像传输问题，提出含TSPA与TRM模块的TokenComSR框架，结合Swin Transformer收发器与接收端SR模块，仿真显示其性能优于基线方法。
AI 中文摘要
针对带宽受限衰落信道上资源受限的无线边缘设备，采用传统分离编码的无线图像传输会出现 cliff-effect 崩溃问题。基于卷积神经网络的主流深度联合信源信道编码（JSCC）可缓解该问题，但通常无法保留补丁级结构，从而阻碍自适应每令牌功率分配，并限制用于超分辨率（SR）的令牌域补偿。为应对这些挑战，本文提出一种带超分辨率的令牌通信框架（TokenComSR）。具体而言，设计了任务敏感型功率分配（TSPA）模块和信噪比（SNR）条件型令牌细化模块（TRM）；TSPA 将任务敏感性的训练估计提炼为推理令牌功率权重，TRM 则在解码前于令牌域估计 SNR 条件型残差以校正信道诱导的失真。基于 TSPA 和 TRM，所提 TokenComSR 将基于 Swin Transformer 的令牌收发器与接收端 SR 模块配对，用于资源受限的无线图像传输。仿真结果证实了所提 TSPA 和 TRM 的有效性，表明其在重建保真度和感知质量上均优于分离编码与 JSCC-SR 基线。
英文摘要
For resource-constrained wireless edge devices over bandwidth-limited fading channels, wireless image transmission using traditional separate coding suffers from the cliff-effect collapse. Prevailing deep joint source-channel coding (JSCC) based on convolutional neural networks can mitigate this issue but usually fail to preserve patch-level structures, thereby preventing adaptive per-token power allocation and limiting token-domain compensation for super-resolution (SR). To address these challenges, we propose a token communication framework with SR (TokenComSR). Specifically, we conceive a task-sensitive power allocation (TSPA) module and a signal-to-noise ratio (SNR)-conditioned token refinement module (TRM). TSPA distills training estimates of task sensitivity into inference token power weights, while TRM estimates an SNR-conditioned residual to correct channel-induced distortion in the token domain before decoding. Building on TSPA and TRM, the proposed TokenComSR pairs a Swin Transformer-based token transceiver with a receiver-side SR module for resource-constrained wireless image transmission. Simulation results confirm the effectiveness of the proposed TSPA and TRM, demonstrating improvements over separate coding and JSCC-SR baselines in both reconstruction fidelity and perceptual quality.
Comments6 pages, 5 figures, and 2 tables
