TokenComSR: Task-Sensitivity-Guided Token Communication for Wireless Image Super-Resolution

2026-09-03

AI Summary

为解决带宽受限的无线边缘设备图像传输中的悬崖效应问题，提出了一种基于任务敏感功率分配和信噪比条件令牌精炼模块的TokenComSR框架，提高了重构保真度和感知质量。

Abstract

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.

Problem

• Wireless Image Transmission
• Cliff-Effect Collapse
• Joint Source-Channel Coding
• Token Communication
• Super-Resolution

Innovation

• Task-Sensitivity-Guided
• Token Communication
• Power Allocation
• SNR-Conditioned Refinement
• Wireless Image Super-Resolution